# EvoX Intelligent Evolution System and AI Native Research Practice Course Lecture Notes
Class Hours: 1 · Courseware Pages: 16
## Reading Navigation
- [Lesson 6 | Topic: System-Level Continuous Evolution: Genesis and the Sedimentation of the Software World](#lecture-lesson-1)
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## Lesson 6 | Topic: System-Level Continuous Evolution: Genesis and the Sedimentation of the Software World
### System-Level Continuous Evolution: Genesis and the Sedimentation of the Software World
*Page 1*
> **Page Objective:** Learn how Genesis enables project code to continuously accumulate and evolve across lifecycles through multi-agent collaboration and strict verification mechanisms.
> **Page Objective**
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> How a project can go beyond completing a single coding task and instead allow code to continuously accumulate and evolve throughout its entire lifecycle. There are two core threads: first, the engineering pattern formed by multi-agent collaboration and recursive task decomposition; second, the mechanism by which compilation, testing, and acceptance sediment effective modifications into project history.
**Key Teaching Points**
- 1. First point out the meaning of "continuous evolution." Software is not a product that is finalized once written, but an entity that is continuously modified, supplemented, and refactored over time. Genesis's positioning is precisely to provide a runnable organizational approach for this long-term evolution, rather than a technique for one-time code generation. 2. Multi-agent collaboration addresses "who divides the work." Facing a real project, the task scale often exceeds single-pass processing capacity, so role division and collaborative workflows are needed to make the responsibilities of different stages clear and traceable. 3. Recursive task decomposition addresses "how to get started." A larger goal is decomposed layer by layer into executable small tasks, until each subtask is specific enough to be independently completed and verified. This both reduces the difficulty of each step and allows the result of each step to be evaluated individually. 4. Strict verification addresses "what counts as valid." Compilation passing, testing passing, and acceptance passing are three progressively tightening gates. Only modifications that pass these gates are qualified to be written into project history
- Modifications that fail are discarded or reworked. Collaboration and decomposition generate a large number of candidate modifications; verification is responsible for screening; the screened results sediment into project history, and project history in turn becomes the starting point for the next round of evolution. This is the complete loop of "system-level continuous evolution."
**Comprehension Check**
Ask students to explain in their own words the sequential relationship among "decomposition—verification—sedimentation," and answer: if a modification passes compilation but fails testing, what state would it be in under the mechanism described on this page? Based on this, the teacher confirms whether students truly understand the role of the verification gates, rather than merely memorizing terms.
> **Transition Prompt**
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> This page establishes the overall framework; subsequent content will move into more specific practices and examples. After confirming that students can restate this loop, continue deeper into specific stages.
### Large Model Capability Boundaries and the Necessity of System-Level Evolution
*Page 2*
> **Page Objective:** Understand the capability boundaries of current large models in complex system development and the necessity of building continuously evolving systems.
> **Page Objective**
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> Understand the capability boundaries of large models in complex system development, and why intelligent components must be embedded into a system loop capable of continuous evolution across lifecycles.
> **Explanation Sequence**
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> Large models are powerful intelligent components, but they are not self-sustaining systems. They already possess usable capabilities in understanding, generation, reasoning, and tool invocation, and can serve as high-quality hypothesis proposers and code producers; however, they lack the state, goals, and feedback mechanisms required for continuous evolution across lifecycles.
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> Understanding, generation, reasoning, tool invocation ultimately yield a static snapshot, and the state resets to zero when the session ends. On the right is the embedding of large models as small modules into a system-level evolution loop: proposal, evaluation, selection, sedimentation. Large models handle proposal; surrogate models reduce evaluation cost; the selection stage handles multi-objective trade-offs; sedimentation forms cross-session memory; swarm intelligence maintains candidate diversity and returns to the next round of proposal.
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> State boundary—limited context window and lack of persistent cross-session memory mean long-cycle projects cannot be self-sustaining; goal boundary—real problems often involve multi-objective conflicts and preference trade-offs, and models struggle to autonomously provide defensible criteria for trade-offs; feedback boundary—lack of executable evaluation, selection, and elimination mechanisms means generated solutions cannot be objectively verified and iteratively retained.
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> Improved model capability does not equal improved system capability. Capability is a snapshot; environment is a process. Only by embedding large models into the "generation—evaluation—selection—sedimentation" evolution loop, maintaining diversity through swarm intelligence, compressing evaluation cost through surrogate models, and retaining effective experience through selection and sedimentation, can intelligence remain adaptive in dynamic environments. This is also EvoX's foothold.
**Key Teaching Points**
- Large models possess powerful understanding and generation capabilities, but cannot independently evolve systems. Real-world complex systems require continuous evolution capability across lifecycles; static snapshots are insufficient to cope with environmental change. Intelligent components need to be paired with evolution mechanisms to adapt to dynamic environments; key mechanisms include the evolution loop, swarm intelligence, and surrogate models. State boundary, goal boundary, feedback boundary.
**Exercise Handling**
For core concept questions, choose "Understand the capability boundaries of current large models in complex system development and the necessity of building continuously evolving systems."
For the remaining true/false questions, prioritize options that can be explained "with evidence or examples"; relying only on intuition, skipping material and reciting, or comparing by changing all factors at once does not meet the requirements of this page.
When the answer is correct, affirm that the student can connect it to the concepts on this page and explain the basis; when the answer is incorrect, prompt them to return to the explanation to find key evidence or examples, focusing on reviewing the three types of boundaries and the "proposal—evaluation—selection—sedimentation" loop.
First explain that the core meaning is "large models possess powerful understanding and generation capabilities but cannot independently evolve systems," then give examples of why complex systems require continuous evolution capability across lifecycles.
**Comprehension Check**
Ask students to explain in their own words "why improved model capability does not equal improved system capability," and identify at least two of the three types of boundaries: state, goal, and feedback. If students can clearly explain the relationship from standalone capability to loop evolution, proceed to the next page; if not, return to the comparison diagram and re-sort the left and right paths.
> **Transition Prompt**
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> After confirming that students can state the core relationship "large models are components; the evolution loop is the system," proceed to subsequent content on system-level continuous evolution, Genesis, and the sedimentation of the software world.
### Boundary Expansion from Numerical Parameters to Program Systems
*Page 3*
> **Page Objective:** Master how large models expand the objects of evolution from traditional numerical parameters to programs, model structures, and system states.
> **Page Objective**
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> Master how large models expand the objects of evolution from traditional numerical parameters to programs, model structures, and system states, and understand the driving conditions and evaluation changes behind this boundary expansion.
> **Explanation Sequence**
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> The objects are usually low-dimensional, predefined numerical parameter vectors, such as hyperparameters, weights, and continuous variables; the search space is limited by manually preset structures and encoding methods.
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> After the expansion of computational scale and engineering foundations, the search for high-dimensional, discrete, and structured objects changed from infeasible to tractable, and the search boundary expanded in sync with compute power and toolchains.
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> With code understanding, contextual reasoning, and candidate generation capabilities, program-level objects can be directly constructed, modified, and evaluated, reducing dependence on manually designed encodings and mutation operators.
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> If candidate generation methods are still manually fixed, the system will only repeat established routines; incorporating the candidate generation method itself into the objects of evolution, such as the mutation strategy and parameter settings of differential evolution, is precisely a manifestation of boundary expansion.
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> From bottom to top: numerical parameters → model structures → algorithm mechanisms → programs and software engineering states; upward, the dimensionality of the search space and semantic complexity increase; on the right is support from large model capabilities; above are executable verification and engineering cost constraints.
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> The higher the level, the stronger the semantic constraints and the longer the evaluation chain; candidates must be executable and verifiable; fitness evaluation changes from a scalar function to a joint judgment of runtime results and engineering constraints.
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> This is not replacing parameter optimization, but incorporating parameter optimization into a larger program and system evolution loop, forming a continuous spectrum from local tuning to system-level improvement.
**Key Teaching Points**
- Expanded computational scale brings a broader search boundary. Large models can understand code and context and construct complex candidates. The objects of evolution expand from numerical parameters to programs and software engineering states, passing through model structures and algorithm mechanisms. First, compute power and engineering foundations expand; second, large models possess code understanding, contextual reasoning, and candidate generation capabilities.
**Keyword Coverage**
- Numerical parameters, model structures, algorithm mechanisms, programs and software engineering states, search boundary, code understanding, contextual reasoning, candidate construction, executable verification, engineering cost, fitness evaluation, continuous spectrum. Exercise handling and feedback: Large models expand the objects of evolution from traditional numerical parameters to programs, model structures, and system states. If the answer deviates, prompt them to return to the concept sentence and retry. Large models can understand code and runtime context, and based on this construct and modify complex program-level candidates
- Incorrect options often confine large models to manually encoded numerical parameters. The correct direction of Exercise 3, Exercise 4, and Exercise 5 is not recitation, intuition, or changing all factors at once, but being able to explain the relevant conclusion with evidence or examples. Feedback can emphasize: only being able to connect to the concepts on this page and explain the basis counts as true mastery. The team first only tunes numerical hyperparameters such as learning rate, batch size, and number of network layers
- Then let the large model read the training script and runtime logs, directly rewrite the optimizer implementation, data pipeline, and algorithm control flow, and then hand candidates to the runtime for execution and return metrics for the next round of program-level evolution. You can ask: What is the core meaning? What are the two conditions driving expansion? Answer points: Traditional evolution takes low-dimensional numerical parameter vectors as objects, and the search space is limited by manual presets
- Expansion of compute power and engineering foundations makes high-dimensional, discrete, and structured search tractable
- Large models can construct and rewrite program-level objects, so the objects of evolution gradually expand to model structures, algorithm mechanisms, and programs or software engineering states. Then ask: How do large models participate in the construction and evaluation of program-level candidates? Answer points: Read scripts and runtime context, rewrite optimizers or control flow to generate executable candidates
- The runtime executes and checks whether they can run, then compares them with engineering constraints such as resources and time budgets; returned metrics drive the next round of evolution.
**Comprehension Check**
Have students restate the hierarchy expansion chain in their own words, and fill in the large model capabilities on the right and the verification and cost constraints above. You may follow up: "Why does fitness evaluation become more like a joint judgment of runtime results and engineering constraints at higher levels?" Confirm that students can state reasons such as executability, verifiability, and longer evaluation chains.
> **Transition Prompt**
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> After confirming that students can distinguish the relationship between parameter optimization and program- or system-level evolution, proceed to the next page on multi-agent collaboration and verification sedimentation.
### Complex Challenges in the Real World: Multi-Objective Trade-Offs and Diversity Maintenance
*Page 4*
> **Page Objective:** Understand the importance of mutually constraining complex objectives in real-world problems and preserving diverse candidate solutions.
**Key Teaching Points**
- Under multiple objectives there is no natural total order. When seeing a set of candidates, first ask "under what preference are we comparing," then discuss superiority or inferiority; otherwise it is easy to mistake personal preference for objective conclusion.
- Retaining only the current best will cause premature convergence and mode collapse in the population. Mechanisms such as crowding distance, niching, and external archives can be used to spread candidates across different regions of the front and cover more trade-off intervals.
- Algorithms are responsible for generating differentiated usable options, while domain experts are responsible for completing evaluation and trade-offs based on preferences and constraints. Do not confuse "generating candidates" with "making decisions for people."
Page Objective
Understand the essence of mutually constraining objectives in real-world problems, and explain why the system should retain a set of differentiated candidate solutions rather than converge to a single "optimal answer."
Explanation Sequence
Real problems rarely have a unique optimal solution. Multiple objectives constrain each other, and improvement in one metric usually comes at the cost of degradation in others. Therefore, the system should retain a set of differentiated non-dominated candidate solutions, making trade-off relationships explicitly visible and keeping decisions within human judgment.
Gray scatter points represent possible solutions, and colored points represent mutually non-dominated candidates, which together form the front. In the figure, A favors strength, B is balanced, C favors efficiency, and D has extremely low drag. The Pareto front can only be approximated, not eliminated.
3. Expand on four key understandings. First, objective conflict is structural, not a tuning problem: there is no simultaneous optimum among objectives such as lift-to-drag ratio, structural strength, cost, and manufacturability. Second, under multiple objectives there is no total order: any two solutions may be mutually non-dominated, and "best" must be defined with a preference direction attached; when seeing a set of candidates, first ask "under what preference are we comparing," then discuss superiority or inferiority. Third, sparse feedback is the norm: real engineering evaluation depends on high-cost simulation or measurement, feedback is scarce, noisy, and gradients are unavailable, making single-point hill-climbing search difficult to advance effectively; evolutionary algorithms maintain a population and simultaneously explore multiple directions under few evaluations.
Pursuing only the current best will cause premature convergence and mode collapse in the population, ultimately losing entire unexplored trade-off intervals; mechanisms such as crowding distance, niching, and external archives can be used to spread candidates across different regions of the front.
Algorithms are responsible for "generating a set of differentiated usable options," while domain experts are responsible for "completing evaluation and trade-offs based on preferences and constraints." A candidate solution set is not about making decisions for people, but about placing comparable options on the table.
In supercritical airfoil design, aerodynamic efficiency, drag divergence characteristics, structural strength, and manufacturing cost constrain each other. EvoX's value is not in providing a unique "optimal airfoil," but in outputting a family of candidate airfoils covering different trade-off intervals for engineers to select according to specific flight conditions and process constraints.
Key Teaching Points
Define "best" with a preference direction; actively maintain diversity; distinguish generation from trade-offs. Under multiple objectives there is no natural total order, and retaining only the current best leads to population collapse; algorithms generate differentiated candidates, and experts complete the final judgment based on preferences and constraints. The two small figures on the right side of the diagram already intuitively present the difference between single-point convergence and diverse distribution: the former collapses into a cluster, while the latter spreads into a front.
Exercise Handling
The absence of a total order means preference direction must participate in the definition; sparse feedback makes single-point search difficult to advance because evaluation is expensive, noisy, and gradients are unavailable; the value of dispersed candidates is to leave choice and domain knowledge to experts; pursuing only the current best leads to premature convergence and mode collapse; in supercritical airfoil design, EvoX's positioning is to output a family of candidate airfoils covering different trade-off intervals. During feedback, if students choose incorrectly, guide them back to the three keywords "preference direction," "sparse feedback," and "diversity is an asset."
Case Study Guidance
In the urban air taxi scenario, conflicts can be identified between range and payload, noise and aerodynamic efficiency, and safety redundancy and manufacturing cost. These objectives constrain each other, so there is no single design that is simultaneously optimal, only a mutually non-dominated Pareto front. EvoX can treat range, payload, noise, safety redundancy, and cost as multiple objectives, run population search, and output mutually non-dominated candidate solutions distributed across different regions of the front; domain experts then perform high-fidelity simulation or measurement on the candidates according to operating scenarios, regulatory constraints, and process conditions, and complete evaluation and final trade-offs based on preference direction.
Comprehension Check
Ask students to give a daily multi-objective example in their own words, such as the trade-off among performance, price, weight, and battery life when buying a laptop, and answer "under what preference are we comparing." If students can explain that the same set of candidates yields different reasonable choices under different preferences, it shows they understand the core of this page.
Transition Prompt
After confirming that students can distinguish "generating candidates" from "making decisions for people," proceed to the next page on improvements in computational methods or methodological improvements. This page answers "why multi-objective trade-offs and diverse candidates are needed"; subsequent content will respectively answer "how to compute faster" and "how to improve the method itself."
### Tensorized Computation and Unified Architecture for Parallel Exploration Support
*Page 5*
> **Page Objective:** Understand how EvoX uses batch tensor computation to support high-intensity parallel evolutionary exploration.
> **Page Objective**
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> Understand how EvoX uses batch tensor computation to support high-intensity parallel evolutionary exploration, and grasp the two mutually supporting main lines: the tensorized computation graph and the open, decoupled unified architecture.
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> Suggested Explanation Sequence:
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> When the population expands from dozens to tens of thousands, the core problem of evolutionary computation is no longer "can a good solution be found," but "can enough candidates be evaluated within limited time." Diversity depends on throughput; maintaining diversity is essentially a compute problem.
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> Tensorized computation is responsible for "running fast," and unified architecture is responsible for "being changeable."
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> Traditional serial CPU loops execute "evaluation—recording—update" per individual, incurring three types of costs: high scheduling overhead, fragmented operators, and insufficient GPU utilization; the tensorized approach organizes population maintenance, batch evaluation, and state updates as batch tensor operations, allowing the same generation or even multiple generations of candidates to be computed in parallel. For example, with N candidate solutions, serial execution loops one by one, while tensorization organizes the population into a tensor and completes batch evaluation and parallel state updates at once, thereby reducing scheduling overhead and improving GPU occupancy.
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> Rewriting only the loop is not enough; if algorithms, problems, and scheduling logic are coupled, changing the algorithm or task requires rewriting. The unified architecture decouples algorithms, problems, workflows, and Runtime scheduling, and combined with heterogeneous compute scheduling performs batch task-level management of CPU/GPU, allowing the same parallel mechanism to be reused in method evolution scenarios such as MetaDE and AutoPSO.
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> On the left is the traditional serial CPU loop, serial per individual with low GPU occupancy; on the right is GPU batch tensor computation, population tensor → batch evaluation → parallel state update; at the bottom is the open, decoupled unified foundation, including algorithms, problems, workflows, and Runtime scheduling.
**Key Teaching Points**
- EvoX not only solves problems, but also incorporates the method of producing candidate solutions itself into the objects of evolution. In MetaDE, the outer algorithm evolves the configuration and strategy of the inner algorithm; AutoPSO and EvoGO further combine components and use historical feedback to learn candidate generation methods, moving from "changing parameters" to "changing algorithm structure." This kind of inner-outer dual-layer evolution naturally requires a large number of inner tasks to be batch evaluated and state updated. It changes the organizational form of computation, batching the entire path of "population—evaluation—update" into tensor operations, rather than replacing a certain operator or changing the encoding method. If performance profiling shows "too many small operators, low GPU occupancy, and high CPU scheduling share," it usually indicates that the system is still in a per-individual serial loop, which is the entry signal for introducing a tensorized computation graph. Doing only tensorization means changing algorithms or tasks requires rewriting
- Doing only unified architecture without batch parallelism still cannot make it fast.
**Exercise Handling**
The core transformation of tensorization is rewriting population maintenance, batch evaluation, and state updates into batch tensor operations; the main costs of traditional serial CPU loops are high scheduling overhead, fragmented operators, and insufficient GPU utilization; the purpose of opening and decoupling the unified architecture is to make the parallel mechanism reusable across different algorithms and tasks; with N candidate solutions in the same generation, the key benefit is completing batch evaluation and parallel state updates at once; the foundation brought by GPUs and large models elevates batch computation from local optimization to a foundational capability for system-level evolutionary exploration. Answer feedback should guide students back to the two keywords "batch parallelism" and "decoupled reuse," avoiding distractors such as "compress the population," "approximate evaluation," and "fixed workflow."
The case question requires migrating differential evolution from single-machine serial execution to a batch parallel foundation. Answer points for Question 1: Per-individual serial execution brings high scheduling overhead, fragmented operators, and insufficient GPU utilization; tensorization alleviates this by organizing the population into tensors and merging batch evaluation and state updates, while keeping the semantics of mutation, crossover, and selection unchanged. Answer points for Question 2: The unified architecture decomposes and decouples by algorithm layer, problem layer, workflow layer, and Runtime scheduling layer;
The algorithm layer only describes candidate generation and update semantics, the problem layer encapsulates batch evaluation interfaces, the workflow layer orchestrates inner and outer tasks, and the Runtime scheduling layer handles heterogeneous compute scheduling and batch task-level management, thereby allowing the same foundation to be reused in method evolution scenarios such as MetaDE and AutoPSO.
**Comprehension Check**
Have students explain in their own words "what the tensorized computation graph solves and what the unified architecture solves," and give examples of why the two must work together.
> **Transition Prompt**
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> After confirming that students can distinguish "running fast" from "being changeable," proceed to the next page on specific cases or engineering mechanisms of system-level continuous evolution.
### The Evolution of Algorithms Themselves: From Solving Answers to Improving Methods
*Page 6*
> **Page Objective:** Understand the basic principle of incorporating the candidate generation method itself into the optimization object to achieve algorithm self-evolution.
**Key Teaching Points**
- The inner layer seeks answers, and the outer layer changes methods. Seeing "strategies, parameters, operators, and components placed into search" means method evolution; seeing "only candidate solutions changing while generation rules remain unchanged" means traditional evolutionary algorithms.
- Preference signals first enter answer evaluation, then undergo cross-task aggregation and stability checks before being allowed into method selection. Skipping this step means the outer layer will chase a pleasing but irreproducible result.
- How large is the method space? How many inner tasks should run each round? What independent task set is used to verify that outer-layer improvements truly generalize? If these three accounts are unclear, bilevel search can easily degenerate into more expensive random tuning.
Page Objective
Traditional evolutionary algorithms search under a fixed generation mechanism, and what is always optimized is the candidate solution; method evolution incorporates the method that produces candidate solutions into the search object as well, completing the evolution from "solving answers" to "improving methods."
Explanation Sequence
1. First compare the difference between traditional evolutionary algorithms and method evolution, pointing out that "the object being optimized" has shifted.
2. Introduce the definition of method evolution, and explain that EvoX advances along this route by incorporating the method of candidate generation itself into the objects of evolution.
The inner layer seeks answers, and the outer layer changes methods.
4. Locate which layer preference optimization should be placed in, emphasizing that the two types of objectives cannot be confused.
5. Give MetaDE, AutoPSO, and EvoGO as typical forms.
6. Converge on benefits and costs, and handle exercises and cases.
Key Teaching Points
Rules such as selection, crossover, and mutation—"how to produce answers"—are written in advance by humans, and search only moves within the answer space. After computational scale opens up, fixed generation mechanisms instead become the ceiling.
Algorithms not only output better answers, but also continuously improve the strategies, parameters, and components that produce answers through outer-layer search. EvoX is work on this route, for example incorporating the mutation strategy and parameter settings of differential evolution into the objects of evolution.
The inner layer uses the current method to solve a specific problem and produce candidate solutions; the outer layer compares, selects, and updates the method to be used in the next round based on feedback such as solution quality, preference signals, stability, or cost. The object moved up into the search space is not some solution, but the candidate generation mechanism itself, so it becomes an object that can be evaluated, selected, and updated.
The upper layer is "the method of evolving candidate solutions," including strategies/parameters/operator components, outer evolutionary search, and improved generation methods; the lower layer is "solving a specific problem," including the current method, candidate solutions, and solution quality evaluation. The two loops respectively represent outer-layer results feeding back into the inner layer, and solution quality/preference/cost feedback driving both layers. When writing on the board, first draw the upper and lower layers, then use arrows to mark "update generation method" and "feedback signal."
It can participate in upper-layer feedback, but "evaluating the current answer" and "improving the method of generating answers" must be separated. Reading "this solution is more pleasing" directly as "this strategy is better" is using answer preference to impersonate method superiority, and the outer layer will be led astray by the noise of a single result. Preference signals first enter answer evaluation, then undergo cross-task aggregation and stability checks before being allowed into method selection.
MetaDE lets the outer layer evolve the mutation strategy and parameters of differential evolution, while the inner layer solves tasks as usual; AutoPSO and EvoGO further combine particle swarm components and use historical feedback to learn candidate generation methods, achieving the leap from "changing parameters" to "changing algorithm structure."
The benefit is pushing algorithm design from one-time manual tuning to sustainable adaptation. There are three costs: search space expansion, since the method space is far larger than the solution space; increased evaluation cost, since each candidate method must run inner tasks, usually relying on GPU parallel batch evaluation to amortize; increased verification complexity, since whether outer-layer improvements truly generalize requires independent verification design.
Exercise Clues and Answer Guidance
1. Compared with traditional evolutionary algorithms, what is the most essential difference of method evolution in terms of "the object being optimized"? The correct direction is: putting the strategies, parameters, and operator components of "how to produce answers" into the search object as well. If students choose "larger population size" or "removing fitness evaluation," prompt them to return to the shift in the optimization object: what is optimized is no longer only the answer, but the mechanism that produces the answer.
Feedback from inner task performance such as solution quality, preference signals, stability, or cost. If students mistakenly choose "random noise" or "manually specified by engineers," guide them to ask further: On what basis does the outer layer judge that one strategy is better than another? This "on what basis" is the feedback signal.
Let preference signals participate in upper-layer feedback, but treat "evaluating the current answer" and "improving the generation method" as two separate types of objectives. If students directly treat answer preference as method superiority, remind them: preference must undergo cross-task aggregation and stability checks before it can be upgraded from "answer preference" to "method criterion."
Method space expansion, each method incurring inner evaluation cost, and increased verification difficulty. This can be summarized as the triple pressure of space, compute, and verification; method evolution raises the search object by one level, and the cost rises accordingly.
5. Which statement about MetaDE and AutoPSO is accurate? The correct direction is: MetaDE uses the outer layer to evolve the mutation strategy and parameters of differential evolution, while the inner layer is still responsible for candidate solving of specific tasks. AutoPSO and EvoGO go further toward component combination and structural-level improvement, with the direction moving from changing parameters to changing structure.
Case Handling
The scenario is that a team optimizes chip layout on a GPU cluster. They already have a differential evolution solver, but the mutation strategy and parameters depend on manual experience, and performance fluctuates noticeably. They plan to introduce MetaDE-style bilevel optimization and use user preferences to guide search.
The inner-layer input is the current method (mutation strategy and parameter configuration) and a specific layout problem instance, and the output is a candidate layout and its solution quality. The outer-layer input is aggregate statistics of inner task performance, and the output is the updated strategy and parameter configuration. Feedback signals must be aggregated across tasks and repeated runs, such as multi-task averages, quantiles, or stability indicators, and cannot be taken from a single run. The reason is that the quality of a single solution depends heavily on the specific instance and randomness; it measures "whether this answer is good this time," not "whether this method is good." If single-run results directly drive the outer layer, the outer layer will be dominated by instance differences and noise.
The inner layer can use a preference-weighted evaluation function to screen candidate layouts; the outer layer must first transform it into a cross-task stable comparison criterion, such as the weighted score of wirelength and congestion metrics and its variance, before using it to select method configurations, avoiding treating one answer preference directly as method superiority.
Search space expansion can be controlled with parameter grouping, hierarchical search, and coarse-to-fine strategies; increased evaluation cost can be reduced with GPU parallel batch evaluation, small-sample surrogate evaluation, and early stopping; increased verification complexity requires retaining independent verification task sets and repeated runs to check whether outer-layer improvements truly generalize.
Comprehension Check and Transition
The inner layer seeks answers, and the outer layer changes methods. Seeing "strategies, parameters, operators, and components placed into search" means method evolution; seeing "only candidate solutions changing while generation rules remain unchanged" means traditional evolutionary algorithms. After confirming that students can explain "the object being optimized has changed" and "preference cannot directly impersonate method superiority," proceed to the next page's example problems, applications, or summary.
### MetaDE and AutoPSO: The Bilevel Architecture of Method Evolution
*Page 7*
> **Page Objective:** Master how MetaDE and AutoPSO realize automatic evolution of algorithm variants and parameters through inner-outer bilevel optimization.
> **Page Objective**
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> Understand how MetaDE and AutoPSO realize automatic evolution of algorithm configuration and structure through inner-outer bilevel optimization architecture, and master their cost structure and engineering trade-offs.
> **Explanation Sequence**
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> Algorithms no longer only find solutions; they also improve "how they themselves find solutions."
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> 2. Present the general abstraction of the inner-outer bilevel architecture, emphasizing that the outer layer searches the algorithm space, the inner layer solves the target task, and inner-layer performance is fed back as a fitness signal to form a loop.
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> 3. Explain the two implementation paths of MetaDE and AutoPSO respectively, clarifying that the former manages the configuration layer and the latter manages the structure/component layer.
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> 4. Analyze the multiplicative cost effect of bilevel optimization, leading to engineering trade-offs such as budget allocation, approximate evaluation, early stopping, and parallelization.
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> Shift from manually designing a single algorithm to designing an evolvable algorithm space and evaluation mechanism.
**Key Teaching Points**
- Method evolution (Meta-Evolution) takes "the method that produces candidates" itself as the optimization object. What the outer layer searches is algorithm configuration, component combination, or strategy, not task solutions. The outer layer is responsible for searching the algorithm space (hyperparameters, strategies, component combinations, and other "definitions of algorithms")
- The inner layer uses candidate algorithms to actually solve the target task and obtain measurable performance
- Inner-layer performance is aggregated into fitness and returned to the outer layer; the outer layer eliminates inferior configurations, retains and mutates high-quality configurations, forming a continuous improvement loop. Retain differential evolution as the inner solver, and hand DE's own configuration to outer-layer search—mutation strategy, crossover rate, scaling factor, etc. adaptively adjust with the problem and optimization stage. It answers "how should this configuration be set at the current task stage," belonging to configuration-layer method evolution. Treat PSO's core parameters and learning mechanisms as composable components—inertia weight, learning factors, and different combinations of individual and swarm learning mechanisms—and let the outer layer automatically search and combine PSO variants suited to different problems. It transforms "choosing an algorithm" into "searching algorithm structure," achieving the leap from parameter adaptation to mechanism self-generation. MetaDE leans toward automatic tuning at the configuration and hyperparameter layer, while AutoPSO leans toward automatic combination at the structure and component layer, together covering the evolution range from parameter adaptation to mechanism self-generation. The key cost of bilevel optimization lies in the multiplicative effect—each time the outer layer evaluates a candidate algorithm, it often requires multiple inner runs (repeated evaluation due to randomness, joint validation across tasks, etc.), significantly amplifying the computational budget. The actual computational load is approximately "number of outer candidate algorithms × number of inner runs per candidate." Budget allocation, approximate evaluation, early stopping, parallelization. Efficient execution depends on tensorized parallelism, Runtime scheduling, and algorithm composability
- Parallelization can alleviate wall-clock time, but does not reduce total computation. What researchers deliver is no longer a fixed algorithm, but a search space and feedback rules that allow the algorithm to continuously improve itself.
**Comprehension Check**
You may ask the following questions in sequence and give answer points:
1. What is the direct object of outer-layer search?
The algorithm's own configuration, structure, and component combinations. The essence of method evolution is treating the algorithm itself as the object being optimized; the outer layer searches configurations and structures in the algorithm space, rather than directly searching task solutions. Note the distinction between the two layers: the inner layer directly searches solutions; the outer layer searches "what algorithm to use and how to configure it."
2. In MetaDE, which group of objects does the outer layer mainly evolve?
The mutation strategy, crossover rate, and scaling factor of differential evolution. MetaDE allows DE's configuration to adaptively adjust with the problem and stage, belonging to configuration/hyperparameter-layer method evolution, rather than the structure of other algorithms.
3. Which description of the division of labor between AutoPSO and MetaDE is most accurate?
AutoPSO focuses on the automatic combination of parameters and learning mechanisms, turning "choosing an algorithm" into "searching algorithm structure"; MetaDE focuses on configuration-layer evolution. The two are complementary: MetaDE covers the configuration and hyperparameter layer, and AutoPSO covers the structure and component layer, together constituting the evolution range from parameter adaptation to mechanism self-generation, and both must undergo inner-layer evaluation.
4. Which statement about the cost structure of bilevel optimization is correct?
Each outer-layer evaluation of a candidate algorithm often requires multiple inner runs, significantly amplifying the budget and requiring budget allocation, approximate evaluation, or early stopping. The main cost of the bilevel architecture is the multiplicative effect; parallelization can only alleviate, not eliminate, total overhead.
5. After adopting the bilevel architecture of method evolution, what is the cognitive shift in the researcher's role?
From manually designing a single algorithm to designing the space and evaluation mechanism by which algorithms can be evolved. What is delivered is no longer a fixed algorithm, but "an evolvable algorithm space + fitness evaluation rules"; outer-layer search and inner-layer solving thus form a continuous improvement loop. Although a single algorithm is no longer manually fixed, the core work becomes designing the search space and evaluation mechanism, and the responsibility is actually heavier.
**Exercise Handling**
The case "building a bilevel evolution service for continuously changing tasks" can be used after explaining the architecture and costs, letting students answer independently first and then discuss collectively.
Please explain what the inner layer and outer layer are respectively responsible for, and how the signal flows.
The outer layer searches candidate algorithms in the algorithm space—it can evolve DE's mutation strategy, crossover rate, and scaling factor (MetaDE path), and can also automatically combine components such as PSO's inertia weight, learning factors, and learning mechanisms (AutoPSO path). The inner layer uses each candidate algorithm to run and evaluate on the current day's target task, outputting comparable performance; inner-layer performance is aggregated into fitness and returned to the outer layer, which accordingly eliminates inferior candidates, retains and mutates high-quality configurations, and continuously iterates, forming a loop of "outer layer searches algorithm space, inner layer solves target task, performance feedback drives improvement."
Facing the budget pressure that "one outer-layer evaluation requires multiple inner runs," give at least two trade-off measures and explain their costs or risks.
① Budget allocation—set computational quotas separately for the outer and inner layers and limit inner repetitions, at the cost of increased evaluation variance and possible misselection; ② approximate evaluation—use data subsets or short runs to estimate fitness, at the cost of noisy signals and possible underestimation of the best candidates; ③ early stopping—terminate inner runs early for obviously inferior candidates, at the cost of possibly prematurely eliminating slow-burning configurations that perform well later; ④ tensorized parallelism and Runtime scheduling—batch concurrent inner runs, which can significantly alleviate wall-clock time but does not reduce total computation, and is limited by memory and scheduling overhead. The answer must explain the correspondence between each measure and its cost or risk, and reflect the multiplicative estimate of "outer search scale × number of inner runs each time."
Feedback Language:
Correct. This answer can connect to the concepts on this page and explain the basis.
Please return to the explanation on this page, find key evidence or examples, and then judge again.
Be careful to distinguish "layers"—the inner layer directly searches solutions, while the outer layer searches "what algorithm to use and how to configure it"; MetaDE's object is the configuration of differential evolution itself, not the structure of other algorithms; the main cost of the bilevel architecture is the multiplicative effect, and parallelization can only alleviate, not eliminate, total overhead; although a single algorithm is no longer manually fixed, the researcher's core work becomes designing the search space and evaluation mechanism, and the responsibility is actually heavier.
> **Transition Prompt**
>
> After confirming that students can distinguish "configuration-layer evolution" from "structure/component-layer evolution" in their own words, and can state the inner-outer signal loop and the budget multiplicative effect, proceed to the next page's example problems, applications, or summary.
### Multi-Objective Practice Case of Supercritical Airfoil Design
*Page 8*
> **Page Objective:** Learn how to use EvoX to handle complex engineering constraints and generate diverse trade-off solutions in airfoil design.
> **Page Objective**
>
> Understand why supercritical airfoil design is a multi-objective problem, and master how to use EvoX to generate diverse trade-off solutions under strict engineering constraints.
> **Explanation Sequence**
>
> Supercritical airfoil design is a multi-objective practice of EvoX in real engineering. Airfoil shape parameters such as camber, thickness, and trailing edge are mutually coupled, and changing the shape simultaneously affects multiple aerodynamic metrics.
>
> Improving one metric often comes at the cost of another, so it cannot be handled as single-objective optimization and requires multi-objective trade-offs.
>
> Requirements such as lift, angle of attack, and thickness define the feasible design domain, and search must proceed under satisfied constraints to ensure candidate solutions are engineering-usable.
>
> What EvoX generates is a diverse candidate solution set balancing various metrics, not a unique optimal solution; the candidate set allows domain experts to compare different benefits and costs and make trade-offs.
>
> This case transforms a real complex problem into an explorable and evaluable process, seeking interpretable trade-off solutions amid constraints and objective conflicts.
**Key Teaching Points**
- Changes in supercritical airfoil shape affect multiple aerodynamic metrics, and objectives often conflict, requiring multi-objective trade-offs. The design must satisfy multiple strict constraints such as lift, angle of attack, and thickness, and search should proceed within the feasible design domain. Generate diverse candidate solution sets to help experts evaluate different benefits and costs and make trade-offs. First continuously explore in the airfoil parameter space (camber, thickness, trailing edge), then approach the multi-objective trade-off front (metric A and metric B), and finally form a candidate solution set. Supercritical airfoil, multi-objective trade-offs, feasible design domain, candidate solution set, EvoX.
> **Exercise Clues**
>
> - The root of the multi-objective problem, the role of constraints, EvoX's practical output, the significance of diverse candidates, and the value of the method. Correct understanding should land on: shape parameters simultaneously affect multiple metrics, causing objective conflicts
> - Constraints define the feasible design domain
> - The output is a diverse candidate solution set
> - Different candidates represent different benefits and costs
> - The value of the method lies in transforming a real complex problem into an explorable and evaluable process.
**Exercise Handling**
If students mistakenly choose "only optimize lift" or "seek a unique optimal solution," prompt them to return to shape parameter coupling and objective conflict.
If students mistakenly choose "constraints can be relaxed at will" or "unrelated to engineering usability," prompt them that constraints define the feasible design domain and cannot be relaxed at will.
If students mistakenly choose "a single optimal airfoil" or "random shape," prompt them that the output should be a diverse candidate solution set balancing various metrics.
Correct. This answer can connect to the concepts on this page and explain the basis.
Please return to the explanation on this page, find key evidence or examples, and then judge again.
**Classroom Activity**
Because changes in shape parameters simultaneously affect multiple aerodynamic metrics, improving one often comes at the cost of another, requiring multi-objective trade-offs; constraints define the feasible design domain and ensure candidate solutions are engineering-usable.
**Comprehension Check**
In supercritical airfoil design, how does EvoX generate a diverse candidate solution set under strict constraints for experts to evaluate benefits and costs. The teacher supplements missing keywords based on the answer: objective conflict, feasible design domain, candidate solution set, multi-objective trade-offs.
> **Transition Prompt**
>
> After confirming that students can distinguish single-objective optimization from multi-objective trade-offs, and can explain the purpose of the candidate solution set, proceed to the next page's example problems, applications, or summary.
### Challenges Across Lifecycles: Context Exhaustion and Project-Level Persistence
*Page 9*
> **Page Objective:** Understand the contradiction between large model context limits and real engineering projects lasting months or years.
**Key Teaching Points**
- A single context is a workbench; the project repository is long-term memory. Do not leave population state, failed configurations, and decision rationales only in the session window; before the session closes, they should be written into project-level artifacts, and the next session should read them before continuing.
- Inheritable persistence requires artifacts to be versionable, retrievable, and explainable: code, tests, experiment records, decision logs, evaluation metrics, and acceptance conclusions must be able to answer why this choice was made, what has failed, and what conflicts remain.
- Upper-layer agents decompose goals, and lower-layer agents implement and test. Set checkpoints before each session ends: update decision logs and evaluation metrics; when a new session starts, inherit the checkpoint first, then decide local actions, thereby crossing multiple finite lifecycles.
> **Page Objective**
>
> Understand the contradiction between the context limits of large model agents and real engineering projects lasting months or even years, and establish the necessity of project-level persistence. Core judgment: a single context window Cmax is a finite resource, while the project lifecycle Tproject can last months or even years, so Tproject ≫ Cmax. The real difficulty is not insufficient single-pass reasoning capability, but that experience cannot cross agent lifecycles.
>
> Explanation Sequence
>
> A single agent context can only contain the current goal, local observations, and recent reasoning; after the session closes, the state is cleared, and temporary conclusions and experience are no longer available. Without persistence, subsequent sessions can only repeat exploration.
>
> Project-level persistence. Externalize failed configurations, trade-off processes, candidate solutions, and acceptance conclusions into continuously existing project-level artifacts, including code, tests, experiment records, decision logs, evaluation metrics, and acceptance conclusions. Subsequent agents do not need to start from zero, but read prior experience and judge which directions have been verified, which conflicts remain unresolved, and which metrics have been met.
>
> The object of evolution is no longer just single-run numerical parameters or candidate solutions, but a continuously existing, versionable, inheritable software world. New artifacts produced in each session change the state of this world, and the next session continues evolving on the updated world, so the optimization process becomes project-level continuous evolution.
>
> Multiple agents with finite lifecycles can work collaboratively; upper-layer executors understand goals and decompose tasks, and lower-layer executors complete local implementation and testing, recursively decomposing further when necessary. Write key state before closing, and after a new agent starts, read project memory first before deciding local actions.
>
> Long-cycle projects often have goal conflicts, for example performance, cost, and robustness are difficult to optimize simultaneously; feedback may also be sparse, with one experiment requiring hours or even days. Persistent decision logs and evaluation metrics can transform sparse feedback into inheritable experience, preventing subsequent agents from repeatedly falling into the same pits.
>
> Key Teaching Points
>
> A single context is a workbench; the project repository is long-term memory. Do not leave population state, failed configurations, and decision rationales only in the session window; before the session closes, they should be written into project-level artifacts, and the next session should read them before continuing.
>
> Persistence is not log accumulation. Inheritable persistence requires artifacts to be versionable, retrievable, and explainable: code, tests, experiment records, decision logs, evaluation metrics, and acceptance conclusions must be able to answer why this choice was made, what has failed, and what conflicts remain.
>
> Update decision logs and evaluation metrics before each session ends; when a new session starts, inherit the checkpoint first, then decide local actions, thereby crossing multiple finite lifecycles.
>
> Project-level continuous evolution means migrating the process of generating candidates, evaluating candidates, recording trade-offs, and acceptance conclusions from a single agent context to project-level persistent artifacts, allowing the project to continuously iterate across multiple agent lifecycles.
>
> Exercise Clues
>
> Choose "the context window is limited, and state and experience after session closure cannot be relied upon." During feedback, emphasize: the hard boundary lies in limited length and state clearing after session closure, not parameter scale, parallelism, or objective function.
>
> Choose "code, tests, experiment records, decision logs, evaluation metrics, and acceptance conclusions." During feedback, explain that these artifacts together constitute project memory.
>
> Choose "expanding from single-run numerical parameters/candidate solutions to a continuously existing, versionable, inheritable software world." During feedback, distinguish changes in training paradigm from changes in the boundary of the object of evolution.
>
> Choose "the upper layer understands goals and decomposes tasks, the lower layer completes local implementation and testing, and recursively decomposes when necessary." During feedback, emphasize that cross-lifecycle continuity is guaranteed by project-level persistence.
>
> Choose "after each session closes, population state, failed configurations, and decision rationales are lost, and subsequent agents repeat exploration." During feedback, explain that lack of persistence will not automatically resolve goal conflicts or expand context.
>
> When correct, affirm that "it can connect to the concepts on this page and explain the basis"; when incorrect, ask students to return to the explanation on this page, find key evidence or examples, and then judge again.
>
> Exercise Handling
>
> The goals include aerodynamic efficiency, structural strength, and manufacturing cost, the three conflict, simulation feedback is sparse, and the team hopes multiple short-lifecycle agents can collaborate continuously. Question 1 requires explaining why project memory cannot be placed only in a single context, and listing at least three types of artifacts that should be persisted. Answer points: a single context window is limited and the state is cleared after session closure, while the project spans months, Tproject ≫ Cmax; relying only on a single context would lose population state, simulation results, failed configurations, metric conflicts, and decision rationales; persistence should include at least code and surrogate model training scripts, test and evaluation metric records, experiment records and failed configurations, decision logs and trade-off rationales, candidate solutions and acceptance conclusions.
>
> At session startup, first read decision logs, evaluation metrics, experiment records, and candidate solutions to restore population state and unresolved conflicts; during the session, write new surrogate models, simulation results, candidate evaluations, and trade-off analyses; before closing, update the decision log, record this round's failed configurations, metric conflicts, reasons for discarding, and next-step suggestions, and submit a versioned update; subsequent sessions inherit failed configurations and decision rationales by reading these artifacts, avoiding repeated exploration.
**Comprehension Check**
Ask students to summarize in their own words "why project memory cannot be stored only in a single agent context," and state at least three types of project-level persistence objects. Transition prompt: after confirming that students can state the core relationship on this page—Tproject ≫ Cmax, and project-level persistence externalizes experience into an inheritable software world—proceed to the next page's example problems, applications, or summary.
### Genesis Architecture and the Sedimentation Paradigm of the Software World
*Page 10*
> **Page Objective:** Master how Genesis enables project code to continuously accumulate and evolve across lifecycles.
> **Page Objective**
>
> Help students understand how Genesis re-anchors intelligence from volatile session processes to an accumulable software world, enabling project code and engineering results to continuously accumulate, evolve, and be inherited across a single context window and a single lifecycle.
> **Explanation Sequence**
>
> First present the judgment criterion "sessions are volatile; the repository is long-term memory," then sequentially unfold the essential differences of the sedimentation paradigm, the division of labor among the four types of sedimentable assets, the prerequisite of stable interfaces, the sedimentation loop and acceptance gatekeeping, and cross-lifecycle inheritance, finally using one sentence to clarify the boundary of labor between EvoX and Genesis.
**Key Teaching Points**
- 1. To judge whether a piece of work has sedimentation value, ask only one question—can the next person reproduce it without coming to ask any questions? If not, it has not yet become an asset. Conversational workflows evaporate as context ends, and outputs remain in "this session"
- The software world uses repositories, versions, and artifacts as its medium, and naturally possesses accumulativeness. Genesis precisely formalizes this medium as the long-term memory layer of the evolution system; what it remembers is not conversation, but verified engineering results. Code manages "can it run," methods manage "how to choose," state manages "where are we," and evidence manages "why believe it." Code carries executable capability and is a solving process that can be directly run and reproduced
- Methods carry algorithm and strategy decisions, such as configuration selection and component combination in bilevel evolution
- State carries project context and runtime memory, recording current progress, constraints, and known boundaries
- Evidence carries experiment records and acceptance conclusions, answering why the results are credible. If any of the four is missing, sedimentation becomes irreproducible or non-inheritable: with only code and no evidence, conclusions cannot be trusted
- With only methods and no state, inheritors do not know where to continue. Therefore the four types of assets must be solidified as a set, rather than scattered separately. 3. The prerequisite for sedimentation is stable interfaces. Only when module boundaries and contracts are relatively stable are new outputs assets
- Otherwise every change will affect surrounding implementations, and sedimentation will quickly turn into maintenance debt. This is mutually conditional with Genesis's open decoupled foundation design: stable interfaces allow sedimentation to be safely inherited, and decoupling allows inheritance without rewriting history. Solidifying contracts before sedimentation is often more valuable than writing a few extra documents. Exploration → output → strict acceptance → solidification into assets → direct inheritance in the next round of evolution. Among these, acceptance is the gatekeeper of sedimentation—unverified outputs do not enter the asset layer, thereby avoiding solidifying noise, accidental phenomena, and erroneous conclusions into project "experience." Without a gatekeeper, sedimentation repositories grow longer and less credible. 5. The key to cross-lifecycle inheritance lies in decoupling assets from specific runtime instances, so that new sessions, new agents, and new tasks can restore project-level cognition by "reading the repository," without relying on replaying historical conversations. Inheritance cost drops from "retelling the entire project" to "checkout and load," and project cognition is no longer limited by the capacity of a certain context window. Genesis is responsible for "what to sediment, in what form to sediment, and how it is inherited"
- Multi-agent collaboration and recursive task decomposition are responsible for "how to produce outputs"
- Acceptance and historical inheritance mechanisms are responsible for "how outputs are judged credible." Only when the three are combined do they constitute complete project-level continuous evolution capability—missing any link means the project can only repeat in place.
**Keyword Coverage**
- Genesis, project-level continuous evolution, EvoX system architecture, code/method/state/evidence, acceptance gatekeeper, cross-lifecycle inheritance. This page organizes content through the "Genesis three-layer architecture and sedimentation loop diagram"; on the left, from top to bottom, are the asset layer (code·method·state·evidence), sedimentation mechanism (versioning·acceptance gateway·retrieval and inheritance), and EvoX Runtime (tensorized parallelism·heterogeneous compute·algorithm modules)
- On the right, a ring diagram presents the loop of exploration, output, acceptance, solidification, reuse, and re-evolution, annotated with "assets thicken as lifecycles increase." The page also includes three prompts: do not treat sessions as memory, the four types of assets are indispensable, and unstable interfaces turn sedimentation into debt. During explanation, use this diagram to guide students to restate the loop along the arrows, then point out that acceptance is located after output and before solidification.
> **Exercise Clues**
>
> - The page includes five multiple-choice questions and one case. Question 1 points to the accumulable software world (repositories, versions, and artifacts). If students mistakenly choose context windows, prompt them to return to the starting point "nothing remains after the session ends"
> - Question 2 tests the correspondence of responsibilities among the four types of assets, and the correct item is that code carries executable capability
> - Question 3 tests the boundary between assets and maintenance debt, and the correct item is "module boundaries and contracts are relatively stable." It must be emphasized that the key is not how much output there is or how new the tools are
> - Question 4 tests the gatekeeper in the loop, and the correct item is strict acceptance
> - Question 5 tests cross-lifecycle inheritance, and the correct item is "assets are decoupled from runtime instances, and new sessions can restore project cognition by reading the repository." When correct, affirm that it can connect to the concepts on this page and explain the basis; when incorrect, ask them to return to the explanation to find key evidence or examples before judging. After the multi-objective exploration of the supercritical airfoil ends, the team is left with runnable MetaDE bilevel configurations and surrogate model training scripts, evaluation metric definitions, intermediate states covering the airfoil parameter interval and geometric operating condition constraints, and this round's acceptance conclusions. Question 1 requires organizing assets by the four categories of code, method, state, and evidence, and explaining the gatekeeping role of acceptance—only outputs that pass strict acceptance are allowed to enter the asset layer, and the four types of assets must be solidified as a set; missing any one makes them irreproducible or non-inheritable. Question 2 requires comparing "replaying historical conversations" with "reading the repository": the former depends on the context window, cost grows linearly with the project, and cognition is lost once the conversation is truncated
> - The latter reduces inheritance cost to checkout and load; results are versionable, reproducible, and auditable, so the cost of repeated trial and error in this round sediments into the reuse starting point for the next round.
**Classroom Activity**
Ask students to restate the content of this page using an example close to their own experience, and explain the relationship between that example and the judgment criterion "can the next person reproduce it without coming to ask you."
**Comprehension Check**
Randomly ask a student to summarize the key points of this page in their own words, and the teacher supplements missing keywords based on the answer, focusing on confirming that students can state both "the division of labor among the four types of assets" and "acceptance is the gatekeeper of sedimentation."
> **Transition Prompt**
>
> After confirming that students can state the core relationship on this page—sedimentation occurs on top of stable contracts, is guarded by acceptance, and is ultimately inherited by reading the repository—proceed to the next page's application or summary.
### Engineering Pattern of Multi-Agent Collaboration and Recursive Task Decomposition
*Page 11*
> **Page Objective:** Understand how agents with multiple finite lifecycles collaborate to complete complex project development and task decomposition.
**Key Teaching Points**
- When decomposing tasks, first ask "what verifiable thing must this layer deliver," rather than first thinking "which agent should it be handed to." Once deliverables (datasets, interfaces, checkpoints, evaluation reports) are clearly defined, responsibility boundaries and hierarchy will naturally emerge.
- The stopping point of recursive decomposition is not "decompose to the smallest," but "decompose until executable, verifiable, and able to complete a closed loop within a single agent's context." Decomposing too shallowly exceeds the lower layer's capability, while decomposing too deeply brings additional coordination and handoff overhead.
- Overlapping responsibilities will cause multiple agents to repeatedly implement the same thing and produce conflicting results. Use clear interface definitions and acceptance standards to solidify boundaries, and at the same time agree on what the lower layer may decide on its own and what must be escalated to the parent task for adjudication.
Page Objective
When a single executor's context capacity and processing capability are insufficient to carry the entire engineering goal, how to degrade a large problem into a set of deliverable small problems, and make the collaboration results at each layer verifiable and recoverable.
Explanation Sequence
First use "degrading a large problem into a set of deliverable small problems" to introduce the topic, then unfold along the three-layer structure, afterward explain the motivation and termination conditions of recursive decomposition, and finally land on the bidirectional handoff relationship of the task tree and three engineering prompts. During explanation, use the diagram to point out nodes layer by layer, helping students map abstract division of labor to the concrete task tree.
Key Teaching Points
1. Three-layer structure and respective responsibilities. Upper-layer executors face the overall goal, answer "what to do," and output delegable subtasks, constraints, and acceptance standards; middle-layer executors take on subtasks that are still too large and continue recursive decomposition, converging loose large goals into a stable task hierarchy; lower-layer executors complete local implementation and testing within responsibility boundaries, produce verifiable results, and deliver upward, forming a local closed loop. In the diagram, the upper-layer Agent splits into task modules A, B, and C; module B is further split into subtasks B1 and B2 because it is still too heavy, and the four leaf nodes ultimately each land on lower-layer Agent local implementation and testing.
The upper layer stands at the root node and produces delegable tasks and acceptance standards, rather than personally completing implementation.
2. Why recursive decomposition is necessary. Executors' context windows, callable tools, and task scale processable in one pass are all finite. If a single executor holds the entire project context for a long time, cost rises with scale, and goal drift, state pollution, and dilution of key information are also likely. Recursive decomposition disperses this pressure across layers: each executor only needs to understand the goals and constraints of its own layer, complex goals are degraded layer by layer into executable and deliverable work units, and overall consistency is guaranteed by the task hierarchy.
Task tree and bidirectional handoff. Parent tasks and subtasks form a recursive task tree. Flowing downward is task delegation—goals, boundary constraints, and acceptance standards; flowing upward is result recovery—outputs, verification evidence, and unresolved problems. Whether collaboration truly holds depends on three things: whether the task hierarchy is clear, whether responsibility boundaries are mutually exclusive, and whether handoff artifacts (interface definitions, datasets, test cases, checkpoints) are explicitly agreed upon. Missing any one of these causes multiple executors to degenerate into duplicated labor and conflicting results.
The depth of decomposition, granularity of delegation, and strategy of result recovery can likewise be continuously evaluated and adjusted. When feedback such as a subtask repeatedly being reworked in turn affects how the next decomposition is done, this collaborative process enters a self-improvement loop.
5. Engineering prompts. The stopping condition of recursive decomposition is not "decompose to the smallest," but "decompose until executable, verifiable, and able to complete a closed loop within a single executor's context." Decomposing too shallowly exceeds the lower layer's processing capability, while decomposing too deeply brings substantial coordination overhead and handoff loss. You can first define deliverables, then decide who does them; unclear boundaries are often the largest cost of collaboration.
Exercise Handling
This page includes a set of concept judgment questions. It is recommended that after students answer, ask them to point out the basis, not just state the option.
The correct answer is "understand the overall goal and decompose it into delegable subtasks and acceptance standards." If students choose incorrectly, prompt them to return to the positioning of the root node—what is produced is tasks and acceptance standards, not code itself.
The correct answer is "when the subtask is still too complex and exceeds a single processing scope." It must be emphasized that recursion is not an unconditional action; the trigger condition is that the subtask is still too large to be completed and verified in one pass.
What happens if there is only task issuance? What happens if there is only result recovery? Use this to explain that collaboration must be bidirectional.
The correct answer is "disperse context and processing pressure across layers, reducing single-point context burden and goal drift risk." The core word is "disperse," not "guarantee one-pass success."
The correct answer is "let leaf nodes retain executable, deliverable work units, and clarify interfaces and test outputs." You can review accordingly: blurred boundaries or removing layers will both lead to duplicated implementation and conflicting results.
Classroom Activity
The case study "Collaborative Delivery of an Intelligent Customer Service Quality Inspection System" is suitable as a group activity. First have students draw the recursive task tree: the root node is responsible for overall goal understanding and produces three subtasks A, B, and C plus acceptance standards; module B is further decomposed by the middle layer into B1 and B2, with their interfaces clarified; leaf nodes are the four types of work units A, B1, B2, and C. Then ask each group to list handoff artifacts in both downward and upward directions, such as data interface specifications and cleaned dialogue datasets, feature dictionaries and annotation specifications, model checkpoints and validation reports, evaluation metrics and deployment configurations, and explain why the leaf nodes already satisfy the stopping condition.
For the follow-up question where B1 discovers a conflict between the "annotation specification" and "evaluation metrics," guide students to summarize handling principles: conflicts are escalated for adjudication, and acceptance standards are not modified on one's own; interfaces and standards maintain a single source and are uniformly maintained by the upper layer; before adjudication, the local closed loop remains verifiable; adjudication results update the task tree and related subtask inputs in reverse; the entire process records conflicts, adjudication basis, and final definitions for traceability.
Comprehension Check
What is the upper layer responsible for, under what conditions does recursion continue, and what information is transmitted in the upward and downward directions. If students can simultaneously state the stopping condition and "define deliverables before division of labor," this page can be considered mastered.
Transition Prompt
After confirming that students can distinguish "decomposing tasks" from "personally implementing," and can clearly explain the bidirectional handoff of the task tree, proceed to the next page's discussion of verification and sedimentation mechanisms, so as to connect this page's division-of-labor pattern with subsequent compilation, testing, and acceptance stages.
### Strict Acceptance and Historical Inheritance Mechanisms
*Page 12*
> **Page Objective:** Master how to sediment effective modifications into project history through compilation, testing, and acceptance gateways.
> **Explanation Sequence**
>
> First use one sentence to point out "when seeing any system that 'automatically generates and automatically improves,' ask three questions first: What identity do Agent outputs have by default (candidate?), what verifications must they pass to be adopted (acceptance?), and after adoption, whose foundation do they become (baseline?). Separate these three things, and the overall logic of the mechanism will not be chaotic." Then combine it with examples, diagrams, or keywords on the page to explain why it matters.
**Key Teaching Points**
- What identity do Agent outputs have by default (candidate?), what verifications must they pass to be adopted (acceptance?), and after adoption, whose foundation do they become (baseline?). Separate these three things, and the overall logic of the mechanism will not be chaotic.
- Compilation → testing → behavior checks → upper-layer acceptance is a downhill path of "cost from low to high": the cheaper, the earlier; the more expensive, the later. Remember "cheap first screening, expensive later review," and you will not waste upper-layer review on massive garbage candidates.
- Passing acceptance does not equal actually being better. When test coverage is insufficient or behavior checks are missing, the gate will let harmful modifications through. Develop two habits: supplement regression cases and tolerance definitions for key behaviors
- Always retain observable metrics and manual spot checks as the last line of defense when standards are incomplete.
Strict Acceptance and Historical Inheritance Mechanisms
11
AI999
Genesis and the Sedimentation of the Software World
On what basis can modifications automatically generated by Agents be written into the project trunk? Strict acceptance and historical inheritance mechanisms completely decouple generation from adoption—any code submitted by an Agent is only a candidate patch, and must first pass a set of verification gates before it is qualified to be recognized as part of project history.
Candidate modification → four-layer acceptance gate → trunk / rejection branching Candidate modification queue Agent submission · untrusted by default Candidate A pending verification Candidate B pending verification Candidate C pending verification Candidate D pending verification ① Compilation gate Syntax and interfaces buildable · lowest cost, fastest elimination ② Testing gate Unit/integration tests all green · covers existing contracts ③ Behavior check gate Semantic consistency · numerical tolerance and equivalence ④ Upper-layer acceptance Performance · speedup · maintainability Rejection · failure archive area · retained as negative samples and diagnostic material · isolated storage, does not pollute trunk · rejection itself is noise filtering · avoids repeated exploration of the same dead end Trunk version history (monotonic accumulation) Baseline v3 · inheritable, traceable Baseline v2 Baseline v1 Historical inheritance feedback: adopted baselines become the execution starting point for subsequent Agents Candidate-default principle Agent outputs are by default treated as untrusted input: they are strictly isolated from adopted versions in permissions, storage, and references, and can neither directly read/write the trunk nor be treated by other modules as stable dependencies.
The sole purpose of this principle is to eliminate "generation means entering the repository." Only after passing all gates does a candidate modification obtain equal status with the trunk. Four-layer progressive filtering: increasing cost, fail early Compilation: buildable at the syntax and interface level. Lowest cost, fastest elimination, first blocking obviously invalid modifications. Testing: unit tests and integration tests pass, covering existing contracts. Behavior check: semantic and behavioral consistency, including numerical tolerance, equivalence, and regression comparison, specifically guarding against "tests pass but behavior drifts." Upper-layer acceptance: final adjudication oriented toward task goals and engineering constraints, including judgments on performance, speedup, maintainability, etc.
Gates are arranged from low to high cost, so that the vast majority of invalid modifications are cut off at the cheap stage, leaving expensive upper-layer review for a small number of high-quality candidates—this is precisely the economics of "fail early." The value of rejection and historical inheritance Attempts that fail do not enter the trunk, but can be retained as failure archives/negative samples for diagnosis, deduplication, and preventing repeated exploration—rejection itself is a noise filtering. Modifications that pass acceptance become new baseline versions, and subsequent executors continue evolving on this basis, forming a monotonically accumulating evolution chain, rather than starting from zero each round. As the merge rule and quality contract for multi-agent collaboration and recursive task decomposition, the acceptance gateway defines "who can write into history," thereby determining whether collaboration results converge or increase in entropy.
In scenarios with strong correctness constraints such as compiler refactoring and scientific software upgrades, this mechanism demonstrates extremely high reliability and brings considerable runtime speedup. Failure boundary The completeness of the acceptance standards themselves becomes the new bottleneck: when test coverage is insufficient or behavior checks are missing, the gate will let through modifications that "pass acceptance but are actually harmful." Therefore observable metrics and manual spot checks are needed as a fallback, and regression cases and tolerance definitions must be continuously strengthened.
In the strict acceptance mechanism, what status does code submitted by an Agent have by default?
A finished product whose usability is determined by the submitter
A formal version that can be directly merged into the trunk
A candidate modification pending verification, strictly isolated from adopted versions in permissions, storage, and references
A byproduct used only for logging and not participating in any process
Candidate-default treatment regards Agent outputs as untrusted input, strictly isolated from the trunk, institutionally preventing "generation means entering the repository."
Unverified outputs should not have the same status as adopted versions.
Correct. This answer can connect to the concepts on this page and explain the basis.
Please return to the explanation on this page, find key evidence or examples, and then judge again.
In what order are the four-layer acceptance gates arranged, and what is the design intent?
From high cost to low cost, prioritizing resources for the most expensive upper-layer review
Sorted by candidate submission time, first come first verified
In random order, to increase verification diversity
Compilation → testing → behavior check → upper-layer acceptance, so that the vast majority of invalid modifications are cut off at the cheap stage
Cheap gates handle large-scale elimination, and expensive gates only face a small number of high-quality candidates.
Think about which layer is most expensive and which is fastest. The gate order should make expensive review encounter as little garbage as possible.
What type of problem does the behavior check (third-layer gate) mainly guard against?
Insufficient number of unit test cases
"Tests pass but behavior drifts," meaning old and new implementations are inconsistent in semantics or numerical output
Code syntax errors making compilation impossible
Trunk version history occupying too much storage
Behavior checks use numerical tolerance, equivalence, and regression comparison to compare semantic consistency and intercept behavior drift not covered by tests.
The compilation layer handles "can it build," the testing layer handles "are contracts satisfied," and the behavior check focuses on more hidden semantic drift.
How should the value of candidate modifications rejected by the gate be understood?
They should be completely deleted to avoid occupying storage and interfering with subsequent judgment
They should be directly written into the trunk as negative references
They can be retained as failure archives/negative samples for diagnosis, deduplication, and preventing repeated exploration
They should be repeatedly resubmitted without modification until they automatically pass
Failed attempts are isolated outside the mainline but can still be used as negative samples—rejection itself is a noise filtering.
The key difference lies in whether it is allowed to pollute the trunk and whether it can be transformed into reusable diagnostic information.
Which of the following situations is most likely to let the acceptance gateway pass modifications that "pass acceptance but are actually harmful"?
Insufficient test coverage or missing behavior checks, making the acceptance standards themselves incomplete
Upper-layer acceptance simultaneously evaluating performance, speedup, and maintainability
Multiple submissions from different Agents existing simultaneously in the candidate modification queue
Failure archives being retained long-term for diagnosis
Gates can only block problems they can detect; when acceptance standards are incomplete, observable metrics and manual spot checks are needed as a fallback.
Risk often does not come from the submitter, but from coverage blind spots in the acceptance standards themselves.
Acceptance of a compiler kernel rewrite patch
Read the scenario and answer the two questions below.
A team uses multi-agent collaboration to refactor a compiler kernel. Agent X submits a kernel rewrite patch claiming it can improve the running speed of a certain benchmark by about 1.4×. The system processes the patch according to "strict acceptance and historical inheritance mechanisms": the patch first enters the candidate modification queue, then successively undergoes compilation, testing, behavior checks, and upper-layer acceptance; if it passes, it is written back to the trunk and becomes the starting point for the next generation of Agents; if it fails, it enters the isolated failure archive area.
1. Please explain in order which checks the patch must pass, and explain why upper-layer acceptance is placed last.
Compilation → testing → behavior check → upper-layer acceptance. Compilation checks whether it can build at the syntax and interface level, with the lowest cost and fastest elimination; testing checks whether unit/integration tests are all green and covers existing contracts; behavior check compares semantic consistency between old and new implementations, including numerical tolerance, equivalence, and regression comparison; upper-layer acceptance makes the final adjudication oriented toward task goals and engineering constraints, judging performance, speedup, and maintainability. Upper-layer acceptance is placed last because it is the most costly and requires manual or high-level review involvement; only after the earlier gates cut off the vast majority of invalid candidates can this expensive resource be concentrated on a small number of high-quality candidates, achieving "increasing cost, fail early."
On some inputs, the numerical output of the new implementation exceeds the agreed tolerance compared with the old implementation. How should the system handle it? Does it still have value? What would happen if the behavior check were removed?
The patch must not be written into the trunk; it should be rejected and transferred to the isolated failure archive area. Because it already passed compilation and testing, this precisely shows it belongs to the dangerous type of "tests pass but behavior drifts." It still has value: it can be retained as a negative sample for diagnosing the source of the difference, preventing subsequent Agents from repeating the same dead end, and prompting the team to supplement regression cases or re-examine tolerance definitions. If the behavior check were removed, such patches would directly enter upper-layer acceptance or even be written back to the trunk while tests are all green, introducing behavior drift into the historical baseline. This is precisely the failure boundary of the mechanism—gates can only block problems they can detect, so observable metrics and manual spot checks are needed as a fallback.
Candidate, acceptance, baseline
What identity do Agent outputs have by default (candidate?), what verifications must they pass to be adopted (acceptance?), and after adoption, whose foundation do they become (baseline?). Separate these three things, and the overall logic of the mechanism will not be chaotic.
Remember the gate order as a downhill path
Compilation → testing → behavior check → upper-layer acceptance is a downhill path of "cost from low to high": the cheaper, the earlier; the more expensive, the later. Remember "cheap first screening, expensive later review," and you will not waste upper-layer review on massive garbage candidates.
Gates can only block problems they can test
Passing acceptance does not equal actually being better. When test coverage is insufficient or behavior checks are missing, the gate will let harmful modifications through. Develop two habits: supplement regression cases and tolerance definitions for key behaviors; always retain observable metrics and manual spot checks as the last line of defense when standards are incomplete.
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Master how to sediment effective modifications into project history through compilation, testing, and acceptance gateways.
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From EvoClass draft
> **Page Objective**
>
> Master the mechanism of sedimenting effective modifications into project history through compilation, testing, and acceptance
**Key Teaching Points**
- Strict acceptance and historical inheritance mechanisms
- Code submitted by an Agent serves only as a candidate modification and must pass compilation, testing, behavior checks, and upper-layer acceptance before it can enter an adopted project version. Failed attempts can be rejected, while verified results become the historical foundation for subsequent executors. In compiler refactoring and scientific software upgrades, this mechanism demonstrates extremely high reliability and runtime speedup.
**Key Teaching Points**
- Strict acceptance and historical inheritance mechanisms
- Code submitted by an Agent serves only as a candidate modification and must pass compilation, testing, behavior checks, and upper-layer acceptance before it can enter an adopted project version. Failed attempts can be rejected, while verified results become the historical foundation for subsequent executors. In compiler refactoring and scientific software upgrades, this mechanism demonstrates extremely high reliability and runtime speedup. Strict acceptance and historical inheritance mechanisms
- Code submitted by an Agent serves only as a candidate modification and must pass compilation, testing, behavior checks, and upper-layer acceptance before it can enter an adopted project version. lesson_id_distribution 86818163433546df25555ab4cb1c2d59fc1effedc37a25c4e84b74b7d294e704
**Material Basis**
From EvoClass draft
> **Material Clues**
>
> - Master the mechanism of sedimenting effective modifications into project history through compilation, testing, and acceptance
> - Strict acceptance and historical inheritance mechanisms
> - Code submitted by an Agent serves only as a candidate modification and must pass compilation, testing, behavior checks, and upper-layer acceptance before it can enter an adopted project version. Failed attempts can be rejected, while verified results become the historical foundation for subsequent executors. In compiler refactoring and scientific software upgrades, this mechanism demonstrates extremely high reliability and runtime speedup.
> - Code submitted by an Agent serves only as a candidate modification and must pass compilation, testing, behavior checks, and upper-layer acceptance before it can enter an adopted project version.
**Classroom Activity**
Ask students to restate the content of this page using an example close to their experience, and ask students to explain the relationship between this example and "when seeing any system that 'automatically generates and automatically improves,' ask three questions first: What identity do Agent outputs have by default (candidate?), what verifications must they pass to be adopted (acceptance?), and after adoption, whose foundation do they become (baseline?). Separate these three things, and the overall logic of the mechanism will not be chaotic."
> **Corresponding Objectives**
>
> - Understand the engineering pattern of multi-agent collaboration and recursive task decomposition
> - Master the mechanism of sedimenting effective modifications into project history through compilation, testing, and acceptance
**Comprehension Check**
Randomly ask a student to summarize the key points of this page in their own words, and the instructor supplements missing keywords based on the answer.
> **Transition Prompt**
>
> After confirming that students can state the core relationship on this page, proceed to the next page's example problems, applications, or summary.
### Classroom Activity: Simulating Multi-Agent Collaboration and Automated Acceptance Gateway
*Page 13*
> **Page Objective:** Deepen understanding of multi-agent collaborative development and automated acceptance mechanisms through simulation exercises.
> **Page Objective**
>
> Through a classroom simulation exercise, understand task division in multi-agent collaborative development, convergence of candidate modifications, and how the automated acceptance gateway intercepts unqualified modifications; the focus is on running through the engineering loop of "collaborative development—automated acceptance—historical inheritance," rather than having Agents discuss their way to correctness.
> **Explanation Sequence**
>
> Divide classroom groups into planning/decomposition Agents, implementation Agents, test generation Agents, and review/coordination Agents. The same task can produce multiple candidate modifications, which are then uniformly adjudicated by the acceptance gateway.
>
> Task decomposition, parallel implementation, automated acceptance, historical inheritance. Task decomposition should break goals into independently verifiable subtasks, such as "interface validation—algorithm repair—regression testing"; in the parallel implementation stage, multiple implementation Agents submit candidate patches, and test Agents generate boundary cases; in the automated acceptance stage, automated tests, static checks, and contract checks are run, and the gateway judges by executable standards; in the historical inheritance stage, passing modifications are merged into the historical baseline, and rejected modifications retain failure reasons and reproduction information for retry.
>
> On the left is recursive task decomposition, in the middle are multi-agent candidate modifications, and on the right are automated testing and the acceptance gateway. Candidates converge and enter the gateway; if they pass, they are merged into the historical baseline; if rejected, they are sent back with reproducible feedback, and the feedback flows back to implementation and testing.
>
> Criteria must be executable and cannot remain at "looks correct." Focus on intercepting modifications that fail to satisfy interface contracts, introduce regressions, fail to cover boundary conditions, or violate quality constraints. A usable piece of feedback should at least include the failing test case, logs, reproduction steps, expected behavior, and related constraints.
>
> After passing modifications enter the baseline, subsequent Agents do not need to start from zero; rejected modifications become searchable negative examples, avoiding repeated mistakes. During evaluation, focus on whether acceptance rules are reproducible, whether feedback is actionable, and the different risks of false interception and missed interception.
>
> If the gateway is missing or standards are vague, multi-agent collaboration is not necessarily more efficient, and may instead become chaotic due to the lack of unified adjudication; the value of automated gates lies in replacing subjective approval with reproducible evidence.
**Key Teaching Points**
- Before acceptance, first write runnable tests, contract assertions, and boundary cases
- If a rule cannot be reproduced, it cannot stably intercept. Rejected candidates should be accompanied by failure logs, reproduction commands, expected behavior, and related constraints, so that the next Agent can directly locate and revise them. Decomposition granularity that is too fine raises merge and acceptance costs, while too coarse causes candidate modifications to overwrite each other
- Prioritize decomposing into subtasks that can be tested and accepted individually.
> **Material Clues**
>
> - Recursive task decomposition, multi-agent candidate modifications, automated testing and acceptance gateway. The diagram also marks "candidate convergence," "feedback return," and "pass enters baseline, rejection returns reproducible feedback," suitable for explaining the loop while pointing at the diagram. You can have students design a set of executable acceptance gateway rules around the case "the payment service crashes when inputting negative numbers or overly long strings," covering at least interfaces, regression, boundaries, and quality constraints. Reference direction: the interface layer requires the amount field to reject negative numbers and overly long strings, return a unified error code, and perform contract assertions
> - The regression layer requires original payment success, refund, and reconciliation cases to all pass with no new failures
> - The boundary layer includes cases for 0, negative numbers, maximum allowed amount, overly long strings, empty strings, and special characters, asserting no crash and explainable errors
> - Quality constraints include static checks passing, key path coverage not below the agreed threshold, and no high-risk alerts. All rules should be expressed as runnable tests or scripts and be reproducible in continuous integration.
> **Exercise Clues**
>
> - False interception wastes the work of correct candidates and reduces collaboration efficiency, and may also induce the team to relax standards
> - Missed interception lets defects enter the historical baseline, and subsequent Agents continue working on an erroneous baseline, amplifying regression risk. Mitigation methods include retaining failure evidence, sampling and reviewing interception results, sedimenting newly added failure cases into the regression set, and using a combination of boundary cases, contract checks, and canary validation to control risk.
**Comprehension Check**
Randomly ask a student to summarize the key points of this page in their own words, and see whether they can state the three key points "executable standards," "actionable feedback," and "decomposition by independently verifiable boundaries." If they can give examples combined with the payment service case, consider it mastered; if they only answer "let Agents discuss automatically," prompt them to return to the acceptance gateway standards and closed-loop process.
> **Transition Prompt**
>
> After confirming that students can clearly explain the relationship among multi-agent collaboration, automated acceptance, and historical inheritance, proceed to the next page's applications, example problems, or summary.
### AI Native Work Style and Convenient Entry Points
*Page 14*
> **Page Objective:** Understand how to quickly start EvoX experiments through concise entry points and configuration in AI tools.
**Key Teaching Points**
- Remember this division-of-labor line: the AI Native entry point handles installation, basic configuration, and environment checks; you handle problem definition, direction selection, and evaluation standards. Invest the saved time in the latter for the benefits to truly land on research; otherwise you are only moving the waiting from the configuration stage to the rework stage.
- The value of a one-sentence instruction is to let you quickly enter experimentation, not to skip verification. After installation is complete, actively confirm the version, dependencies, and GPU visibility, and treat the status reported by the tool as a clue rather than a conclusion, so that subsequent experiments have a reliable reference point.
- Before cross-machine reproduction, check item by item: whether dependency and version lists are consistent, whether GPU models and the number of available devices are the same, and whether random seeds and key hyperparameters are fixed. Only after these three pass should you scale up the experiment, which can avoid later troubleshooting costs far exceeding earlier configuration costs.
Page Objective**
What exactly does the AI Native work style change, and where are its boundaries. After learning, students should be able to state three points clearly: the interaction entry point shifts from manually checking documentation, installing dependencies, configuring environments, and writing scaffolding to launching experiments with one sentence in an AI tool; installation, basic configuration, and environment checks are undertaken by the tool and reported back; problem definition, direction selection, and evaluation standards remain the researcher's responsibility.
Key Teaching Points**
1. Entry-point migration is the defining feature. The traditional path is "consult documentation → install dependencies → configure environment → write scaffolding → begin the first experiment," and each link can go wrong, and every switch requires rebuilding context. The AI Native path is "one-sentence instruction → tool takes over installation and configuration → environment check passes → enter experiment." What changes is how people begin work, not the object being optimized itself.
2. The nature of friction is transfer, not disappearance. The real burden often does not come from how difficult a single-step operation is, but from frequent context switching: researchers should be focusing on problem modeling, experiment design, and result interpretation, but are constantly interrupted by version conflicts, missing dependencies, and environment paths. Convenient entry points hand off the entire engineering chain, returning attention to research itself.
Complete EvoX installation and basic configuration within a single-sentence instruction. For example, input "install EvoX and complete basic configuration," and the tool executes and feeds back the result, after which the researcher immediately enters experimentation. Note that this is only the starting point, not the endpoint—the "configuration complete" reported by the tool is a state requiring verification, not a conclusion that can be directly trusted.
4. The shorter the path, the smaller the distance from idea to runnable experiment; ideas can be verified the same day, and failed hypotheses can be quickly eliminated. This is especially obvious for method evolution topics that require repeatedly building inner-outer experimental structures: time can be spent on strategy design, evaluation criteria, and result attribution, rather than repeatedly fixing environments.
Convenient entry points reduce onboarding and configuration costs, but do not replace the researcher's judgment about problem value, research direction, and evaluation metrics. Misreading "save time" as "save judgment" is the most common deviation on this page. Only when the saved time is invested in the judgment stage do the benefits truly land on research; otherwise you are only moving the waiting from the configuration stage to the rework stage.
Material Clues**
The upper row in gray shows the four steps of the traditional path, labeled below as "friction concentrated"; the middle row in blue shows the four steps of the AI Native path, with "one-sentence instruction" highlighted, labeled below as "friction transferred"; the bottom orange block is "positioning boundary." During explanation, you can directly ask: which segment in the diagram has been moved away, and which segment remains? Guide learners to discover—what is transferred is installation, basic configuration, and environment checks; what remains is problem, direction, and evaluation standards.
Exercise Handling**
The accompanying five multiple-choice questions are recommended to be handled together after explanation, giving a one-sentence basis for judgment for each to facilitate self-checking.
The correct judgment is "the default interaction entry point changes from manually checking documentation, configuring environments, and writing scaffolding to directly launching experiments with concise instructions in AI tools." If students choose search strategy, cloud migration, or network structure replacement, it shows they conflate "entry-point change" with "algorithm change," and need to return to this page's emphasis: this point describes how people begin work.
The correct judgment is "the tool completes installation, basic configuration, and environment checks within a single-sentence instruction and reports status, and the researcher enters experimentation based on that." If students believe the tool guarantees no further need to check versions and GPU visibility, or believe it automatically determines research questions and evaluation metrics, it shows they have expanded the scope—it only covers the engineering chain segment of installation and basic configuration.
The correct judgment is "reduce onboarding and configuration costs, but do not replace the researcher's judgment about problem definition, research direction, and evaluation standards." Reading "save time" as "save judgment," or believing it is only valuable to beginners, requires returning to the boundary conclusion for repositioning.
The correct judgment is "the value comes from the transfer of engineering friction and shortened iteration cycles, and the condition for validity is that researchers can still verify configuration results and experiment reproducibility." Here it is necessary to prompt distinguishing "reducing manual operations" from "reducing manual judgment."
Question 5 is an inference of the consequences of not verifying; the correct judgment is "the environment is not reproducible, differences between experiments are difficult to locate, and result stability cannot be guaranteed." During feedback, point out: the problem is not the tool's capability ceiling, but the absence of a verification stage; configurations that have not been checked will plant hidden dangers for subsequent reproduction and fault localization.
Classroom Activity**
The case discussion can unfold around "running an EvoX method evolution experiment in three days." Question 1 requires clearly explaining what cost is reduced and what responsibility is retained: what is reduced is engineering friction cost, and what is retained is modeling decisions about problems and directions, determination of evaluation standards, and configuration verification. Question 2 focuses on multi-machine reproduction, and learners can be guided to give at least two checkable practices: save the versions and dependency lists actually installed by the tool and compare them item by item across machines; check each machine's GPU model, memory, and number of visible devices, fixing device IDs when necessary; fix key settings such as random seeds and batch size, first rerun the benchmark task under the same configuration to confirm results fall within the expected fluctuation range, then scale up the experiment.
Comprehension Check**
"If installation is entirely handed to the tool, what do you most need to personally confirm the next day?" Being able to state verifying versions, dependencies, and GPU visibility counts as mastery.
Transition Prompt**
After confirming that learners can state the division-of-labor line "the tool handles engineering preparation, and the researcher handles research judgment," proceed to the next page's example problems, applications, or summary.
### The Researcher's Core Taste: Human-Machine Division of Labor and Critical Thinking
*Page 15*
> **Page Objective:** Understand the researcher's irreplaceable scientific taste and critical judgment in the AI-assisted evolution process.
> **Page Objective**
>
> Understand the researcher's irreplaceable scientific taste and critical judgment in the AI-assisted evolution process. The core question is: when AI greatly reduces the cost of "implementing answers," where does research value shift to?
**Key Teaching Points**
- AI / EvoX can quickly complete code implementation, batch experiment runs, candidate generation, parameter search, and feedback recording; engineering cost is no longer the core bottleneck. Researchers decide what problems are worth studying, what results are meaningful, and what evidence is sufficient to support conclusions. AI expands implementation and search space
- Researchers control problem definition, objective conflicts, constraint boundaries, and acceptance standards. Treat AI outputs as hypotheses to be tested, checking whether hypotheses hold, whether evidence is sufficient, whether boundary conditions are considered, and whether negative results are ignored, avoiding being misled by fluent but unverified answers. Under open exploration and sparse feedback, direction trade-offs cannot be outsourced to AI
- Final judgment and research responsibility still belong to the researcher. On the left is AI / EvoX, corresponding to generating candidates, executing experiments, search space, and recording feedback
- On the right is the researcher, corresponding to raising questions, evaluating evidence, weighing trade-offs, and reflecting on direction
- In the middle is "Code is cheap. Taste matters." Below, "feedback loop: critical checkpoints" indicates that this is not a one-way process, but a cycle in which AI outputs feedback to people, and people then define problems and standards. In EvoX multi-objective optimization tasks, AI quickly generates a large number of candidate solutions and experiment code. Researchers need to judge: does the objective function reflect real needs? Is the baseline fair? Do the metrics omit key constraints? If only looking at scores, one may select a solution that appears optimal but does not meet actual needs. The way "Code is cheap. Taste matters" is embodied here is: AI is responsible for rapid implementation and search, while researchers examine problem definitions and evidence standards and judge which results are truly meaningful.
**Exercise Handling**
The researcher's irreplaceable scientific taste and critical judgment, and the division of labor in which AI is responsible for implementation and humans are responsible for judgment. The typical feature of incorrect options is skipping concepts, skipping material and reciting, drawing conclusions only from intuition, or comparing results by changing all factors at once. The answer to the classroom case should land on: treating AI outputs as hypotheses to be tested, checking the objective function, baseline, and metrics; at the same time making clear that direction trade-offs and research responsibility cannot be outsourced.
**Comprehension Check**
Ask students to distinguish in their own words "implementation cost" and "judgment value," and each give one stage that can be handed to AI and one stage that must be the researcher's responsibility. Once they can clearly explain the relationship between the two, proceed to the next page.
### Course Summary: Building Infrastructure for a Continuously Evolving Software World
*Page 16*
> **Page Objective:** Summarize the core content of the whole course and clarify the vision of building open, evolving, continuously sedimenting intelligent infrastructure.
**Key Teaching Points**
- Candidate solutions, methods that produce candidates (such as MetaDE's inner-outer configuration), and project-level assets are three different levels. Before design or answering, first clarify "which level am I optimizing now," which can avoid conflating method evolution and project-level continuous evolution.
- Only outputs that pass strict acceptance are allowed to enter the experience repository and be inherited. Once unaccepted results are inherited by downstream tasks, errors will be continuously amplified along the inheritance chain, and the source is difficult to locate.
- At the start of each new task, ask yourself: which verified outputs and experience entries from the previous round did I directly reuse this round? If the answer is empty, it means the current process is still stuck at a single task, rather than continuously evolving infrastructure.
Page Objective
Elevate "intelligent evolution" from single search and single optimization to software infrastructure for the real world. The focus is not introducing another new algorithm, but letting students see clearly how the system crosses a single task and a single Agent, relying on the loop of "task—exploration—acceptance—inheritance—re-evolution" to remain open, reliable, and continuously connected to real problems.
Key Teaching Points
1. Upgrade of the object of evolution. Genesis expands the object of evolution from candidate solutions of a single task to project-level continuous evolution across Agents and task cycles. The end of one run no longer clears state; the end of a task instead becomes the starting point of the next round.
2. Engineering sedimentation paradigm. Strict acceptance, historical inheritance, and experience sedimentation are indispensable: acceptance is the gate, deciding what is qualified to remain; inheritance is the path, deciding whether sedimented material can truly be invoked in the next round; experience sedimentation allows the system to accumulate stability across multiple tasks. Writing documents and weekly reports is not engineering sedimentation; assets that can be executed, inherited, and reused are.
3. EvoX's positioning. EvoX sits at the bottom layer, providing the compute foundation, parallel execution, and evolution scheduling, and supports upward the construction of open, composable, continuously accumulable systems; it is not a module that replaces Agent decision-making, nor a result visualization display component.
4. Final vision. The software world is not a closed product delivered once, but intelligent infrastructure capable of continuous evolution, continuous sedimentation, and continuous connection to real problems. The keywords are continuous evolution, continuous sedimentation, continuous connection.
Material Clues
Acceptance outputs (staged results that pass strict acceptance), experience repository (reusable strategies and structured failure lessons), historical inheritance (the direct starting point of the next round of tasks). The left layered diagram reads from bottom to top: EvoX Runtime to project-level continuous evolution, then to multi-agent collaboration, and finally connecting to real-world problems, forming a continuous evolution loop.
Actionable Organization
Multiple Agents are respectively responsible for literature retrieval, code implementation, and experiment reproduction; each exploration is written into experiment records; staged results must be jointly confirmed by manual and automated checks before entering the shared repository; the next round of tasks starts directly from confirmed results. In this way, the project is not a document collection "finished and archived," but a pipeline from task to exploration, acceptance, inheritance, and re-evolution. It should be emphasized: without acceptance, sedimentation will be polluted by errors; without inheritance, sedimentation cannot generate compounding, and the project degenerates into repeated labor.
Common Misconceptions
Equating engineering sedimentation with writing documents and weekly reports. The sedimented material that can truly be invoked by subsequent evolution is executable assets: outputs that pass acceptance, structured experience repository entries, inheritable strategies and configurations. Documents are only the shell; "sedimentation" lacking acceptance and inheritance channels will not generate compounding.
Exercise Handling
Expand the object of evolution from a single task to project-level continuous evolution across Agents and task cycles. Feedback points: this question asks about a change in the level of the object of evolution, not a change in sampling or execution method.
Strict acceptance, historical inheritance, and experience sedimentation together constitute a stable and reliable sedimentation paradigm. Feedback points: review the three cards "acceptance outputs, experience repository, historical inheritance"; missing one link causes sedimentation to fail.
EvoX is intelligent evolution infrastructure connecting to the real world, supporting the construction of open, composable, continuously accumulable systems. Feedback points: EvoX is the lowest-level capability provider, not a display component or a substitute decision-maker.
Method evolution lets "the method that produces candidates" participate in iteration, while project-level continuous evolution sediments assets across task cycles on the same foundation. Feedback points: first ask "what is the object being optimized"—candidate solutions, the method that produces candidates, or project-level assets.
The software world is not a static deliverable, but intelligent infrastructure capable of continuous evolution, continuous sedimentation, and continuous connection to real problems. Feedback points: compare "deliverable," which has an endpoint, with "infrastructure," which continuously connects and continuously grows.
Case Study Handling
The case requires mapping a cross-month research project to the loop of "task—exploration—acceptance—inheritance—re-evolution." Students can be guided to point out: acceptance and inheritance are the core responsibilities of engineering sedimentation. To judge whether it is truly continuously evolving, testable criteria can be set: is the inheritable rate increasing; is the acceptance pass rate trending and are similar failures decreasing; are experience repository entries truly invoked; can conclusions be traced back to experiments and acceptance records; is the system also evolving the methods and configurations that produce candidates themselves. If these metrics do not improve over the long term, it means the system is only repeatedly executing tasks.
Comprehension Check
First, which verified outputs and experience entries from the previous round did this round's task directly reuse? Second, which level is the current optimization object—candidate solutions, the method that produces candidates, or project-level assets? If the first question cannot be answered, it means the process is still stuck at a single task; if the second level is confused, it means it is easy to conflate method evolution and project-level continuous evolution.
Transition Prompt
The end of a task is not the endpoint, but an inheritable asset for the next evolution; the software world is not a static deliverable, but intelligent infrastructure continuously connected to real problems.