AI Native Research Paradigm and Hands-On Introduction to EvoX

As the cost of code generation and implementation drops rapidly, the research bottleneck is shifting from"Can we build it?"to"Is it worth doing, and are we doing it right?".The AI Native research paradigmtreats large models, GPUs, agents, and automated workflows as the default research infrastructure. A researcher's core responsibility likewise shifts from personally writing implementation code to asking questions, defining evaluations, judging direction, and critically assessing results.

Paradigm Shift: Redrawing the Human-AI Division of Labor on Top of Default Infrastructure

When the cost of implementation approaches zero, "writing a few more versions and running a few more rounds" becomes cheap in itself. What is truly scarce ischoosing which direction to explore, and before the results,staying criticalThe division of labor is thus redrawn: AI handles implementing answers, accelerating experiments, and broadening the search; humans handle steering direction, constraints, taste, and key decisions. This is not about outsourcing judgment as well, but about moving human attention up from "typing code" to "setting standards, making trade-offs, and reading results."

EvoX: An Intelligent Evolution System, Not a Single-Algorithm Tool

EvoX inherits the technical lineage of evolutionary computation, targeting complex multi-objective problems, open-ended exploration, and system-level continuous evolution. It offers not just a solver, but anrunnable, composable, sustainably explorableengineering foundation: the artifacts of one experiment can be inherited by the next, and the search process itself can keep evolving.

Problem Definition: identify the objective conflicts and constraints in real problems, rather than picking an algorithm first.
Evaluation Design: write clearly what "good" means — metrics, weights, and acceptance thresholds.
Evolution Process: AI generates candidate solutions in batches, rapidly iterates code, and runs and compares them in parallel.
Runtime Scheduling: manage compute, concurrency, and runtime state so exploration stays sustainable and reproducible.

Hands-On Entry Path: From "Choosing an Algorithm" to "Designing a Process"

A typical misconception is treating the entry point as "picking one from a set of algorithms." The AI Native approach is to first identify objective conflicts and constraints, then turn the problem into an exploration process EvoX can execute: humans first define the objective conflicts, constraints, and acceptance criteria; then AI rapidly generates and iterates code; EvoX runs candidates in batches and compares them; and finally, critiquable, verifiable results emerge. For example, in a multi-objective optimization experiment, you don't let AI write all the code and then take the output at face value — machines handle implementation and exploration, while humans handle definition and judgment.

Core Principles

Code is cheap. Taste matters. When code is no longer scarce, problem taste, evaluation standards, trade-off judgment, and acceptance ability become the research competencies that are hardest to replace. AI handles implementation and batch exploration; humans handle asking questions, defining evaluations, steering direction, and critiquing results.

AI Native Research Division-of-Labor Architecture

The macro architecture diagram shows the division of labor in the AI Native research ecosystem: large models, GPUs, agents, and automated workflows serve as default infrastructure; AI handles implementation, accelerating experiments, and broadening the search; researchers handle problem definition, evaluation design, direction trade-offs, and critical assessment of results. As the engineering foundation, EvoX supports batch runs, candidate comparison, and makes experimental results inheritable and the process sustainably evolvable.

Question 1

Under the AI Native research paradigm, what is the most fundamental change in a researcher's responsibility?

Solely picking the best-performing one among existing algorithms

Handing all the work to AI and minimizing human judgment

Personally writing more and more complex implementation code to demonstrate research ability

Shifting from "writing code to implement" to "asking questions, defining evaluation standards, and judging direction"

Question 2

Regarding EvoX's positioning in AI Native research, which of the following descriptions is the most accurate?

A runnable, composable, sustainably explorable intelligent evolution system and engineering foundation

A library of algorithm examples used only for classroom demonstrations

A fully automated system that can make all decisions in place of the researcher

A single optimization algorithm usable by merely tuning parameters

Question 3

When using EvoX to enter a real problem, what is the correct starting point of AI Native?

First compare the convergence speed of various evolutionary algorithms, then decide which to adopt

First fix the number of generations and population size, then consider the problem itself

First have AI generate all the code, then directly accept its runtime results

First identify objective conflicts and constraints, then turn the problem into an executable exploration process

Question 4

What does "Code is cheap. Taste matters." mean in research practice?

The shorter the code, the better — length is the sole standard for code quality

As long as your taste is good enough, experiments no longer need verification or reproduction

When code is no longer scarce, problem taste, evaluation standards, and acceptance judgment become the scarcer competencies

Since code is cheap, the importance of algorithm implementation has vanished entirely

Question 5

Which of the following practices conflicts with the AI Native research paradigm?

Having AI generate and iterate code in batches, and using EvoX to compare candidate solutions in parallel

Having humans critically accept the results, forming reproducible and cumulative conclusions

Having humans first define the objective conflicts, constraints, and acceptance criteria

Having AI write all the code, then directly adopting its runtime results without human definition of goals and standards

Case Study: Designing a Multi-Objective Optimization Experiment with EvoX

Read the scenario and answer the questions.

A team wants to use EvoX for multi-objective optimization in a real engineering task: they need to simultaneously optimize cost, performance, and robustness. The three metrics conflict with one another and are constrained by compute budget and delivery deadlines. The team plans to let AI rapidly generate and iterate code, and use EvoX to run and compare candidate solutions in batches — but has not yet clarified who is responsible for defining goals and acceptance.

Following the AI Native entry approach, how should the team arrange the human-AI division of labor? Please explain the stages handled by humans and by AI separately.

Answer:
Humans handle definition and judgment: pose the question, explicitly write out the objective conflicts among cost, performance, and robustness along with compute and timeline constraints, design evaluation metrics and acceptance thresholds, and make direction judgments and critical acceptance when facing results. AI handles implementation and exploration: rapidly generate and iterate code, run candidate solutions in batches, broaden the search, and accelerate experiments. Together they form the feedback loop of "humans set direction — AI expands exploration — humans do acceptance." If either side is absent, the results will be unusable or untrustworthy.

If the team's entry point remains stuck at "which evolutionary algorithm to choose," what problems might arise? What should they focus on instead?

Answer:
Staying at "selecting an algorithm" mistakes an engineering problem for an algorithm-selection exercise: objective conflicts aren't explicitly defined, evaluation standards are vague, constraints are ignored — and even if results are produced, it's hard to judge quality, let alone reproduce and accumulate knowledge. Instead, focus on "process design": clarify problem definition and evaluation methods, plan how candidate solutions are generated and how they are batch-run and compared, how Runtime schedules compute and concurrency, and bring each round's results into critique and acceptance — so exploration can sustainably evolve and results can be inherited.