The Core of Research: The Ability to Ask Key Questions

In AI-native research,automated programmingand large-model capabilities have already driven the cost of "producing answers" very low: code generation, solution search, and experiment execution can all be delegated to AI. When answers become cheap, the focus of research is no longer "can it be built," but ratherwhether it is worth doing and where to direct the effort—and this is precisely the ability ofasking key questions.

What a Key Question Typically Needs to Make Clear

Asking key questions is not about probing implementation details, but about transforming vague intuition into atestable, evolvable, and division-of-labor-readyresearch structure. It must at minimum define the following clearly:

True goals and multi-objective conflicts: What is the quantity that truly needs improvement? How should multiple objectives be traded off, and which takes priority?
Non-negotiable constraints: Which conditions are hard boundaries that no solution may violate?
Observable evaluation metrics: What quantity, measured under what conditions, can make the conclusions hold up?
Candidate space boundaries: How large is the feasible range for search and evolution, and what diversity and robustness are needed?
Verification protocol and failure criteria: What evaluation budget to use, what process to follow for verification, and which outcomes should be judged as failures?

Question Quality Determines Solution Space Quality

The solution space AI explores is bounded by the problem definition. The clearer the problem definition, the more likely search and evolution will land in meaningful regions, and the more likely the output will beinterpretable and reusable. The vaguer the problem definition, the more the model can only randomly probe an excessively large space, and results often appear feasible yet fail to accumulate. Take "using EvoX for optimization" as an example: the key questions should be reframed as: "What is the real conflict? How are goals and constraints quantified? What are the evaluation budget and verification criteria? What diversity and robustness are needed? Which outcomes should be judged as failures?" Only then can AI efficiently produce answers while humans steer the direction.

Human-AI Division of Labor and the Researcher's Core Taste

The core of human-AI division of labor is:AI expands the answer space and execution paths, while humans control the direction, trade-off criteria, and scientific significance. In system-level continuous evolution practices like Genesis, this is especially evident—candidate modifications must pass compilation, testing, and higher-level acceptance before they can enter an accepted project version and become the historical foundation for subsequent work. If the key questions fail to clearly define the evaluation protocol and failure criteria, the acceptance gate has no basis for judgment, AI output remains a one-off code artifact, and it cannot accumulate persistently across agent contexts and task cycles.

Therefore, the researcher's core taste manifests as three kinds of judgment:what is worth asking,what evidence is acceptable,what solutions are feasible but not worth pursuing. In EvoX practice, key questions should lead to a runnable, evaluable, and iterable workflow, rather than merely pursuing a one-off implementation result.

Learning Objectives

Clarify the core discernment and key questioning ability that researchers should uphold in the era of automated programming: define direction, conflicts, constraints, evaluation, and taste boundaries clearly, and let AI handle answer generation and execution.
Additional thread: human-AI division of labor and the researcher's core taste—AI expands the answer space, while humans safeguard the direction, trade-off criteria, and scientific significance.

Key Questions Checklist Mind Map

This mind map breaks key questions into true goals and multi-objective conflicts, non-negotiable hard constraints, observable evaluation metrics, candidate space boundaries, and verification protocols and failure criteria. It shows that question quality determines which region AI search and evolution will land in: taking EvoX optimization as an example, only when conflicts, quantification, and acceptance criteria are made clear can output be more interpretable and reusable.

Question 1

In AI-native research, why do automated programming and large-model capabilities shift the core of research up to "problem construction"?

Because search and execution no longer require any human judgment, and problems can be generated automatically

Because research evaluation standards are completely eliminated, and implementation quality no longer matters

Because large models cannot generate any runnable code and can only write text

Because the cost of producing answers is significantly lowered, and humans' relative advantage concentrates on problem definition and steering direction

Question 2

The essence of "key questioning" is closest to which of the following?

Probing every detail of the code implementation until you fully control the entire technical path

Writing longer and more detailed prompts, using prompts as a substitute for problem definition

Expanding computing power and parallelism as much as possible, using resources to cover uncertainty

Transforming vague intuition into a testable, evolvable, and division-of-labor-ready research structure

Question 3

Which of the following sets of elements best matches what "a key question typically needs to clarify"?

Journal for submission, author order, funding sources, conference dates

True goals and multi-objective conflicts, non-negotiable constraints, observable evaluation metrics, candidate space boundaries, verification protocols and failure criteria

Model parameter count, GPU memory usage, training duration, number of parallel GPUs

Programming language choice, framework version, code style, variable naming conventions

Question 4

Regarding the relationship between "question quality" and "the solution space AI can explore," which of the following descriptions is most accurate?

The clearer the problem definition, the more likely search and evolution will produce interpretable and reusable research results

The vaguer the problem, the more freely AI can perform, usually yielding better results

Question quality only affects paper writing, not the experimental process or results

As long as computing power is sufficient, problem definition can be ignored and results will naturally converge

Question 5

A researcher's "core taste" is most directly manifested as which of the following sets of judgments?

How to maximize model parameter scale and training budget

How to write more code as quickly as possible and rack up higher line counts

What is worth asking, what evidence is acceptable, and what solutions are feasible but not worth pursuing

How to avoid discussing research directions with others to reduce external interference

Case Study: Turning "Using EvoX for Optimization" into an Acceptance-Ready Research Question

Read the scenario below and answer the questions.

A research group wants to use EvoX to improve its system's performance. The lead's requirement was just one sentence: "Use EvoX for optimization." The team promptly had AI generate a runnable optimization script, but after it ran successfully they found: they didn't know which metric to use to judge good or bad, weren't clear on which constraints must not be violated, and the conclusions from multiple runs contradicted each other—making it impossible to determine whether this attempt should be kept and carried into subsequent project versions.

1. Please transform the vague requirement "Use EvoX for optimization" into a set of key questions, covering at least the four categories: goals and conflicts, constraints and boundaries, evaluation and verification, and failure criteria.

Answer:
Reference answer (as long as all key points are covered):
(1) Goals and conflicts: What is the performance quantity that truly needs improvement? If "accuracy" and "latency/cost" are both important, how should their weights or priorities be set, and which one should be sacrificed in case of conflict?
(2) Constraints and boundaries: Which conditions are hard constraints and non-negotiable (e.g., interface compatibility, reproducible runtime environment, resource limits)? In which dimensions is the candidate solution space allowed to vary, and where are the boundaries?
(3) Evaluation and verification: Which observable metric serves as the primary evaluation measure? Measured on what data and under what conditions? What is the evaluation budget (acceptable number of trials or computation)? What evaluation process is needed to make two experiments comparable?
(4) Failure criteria: Which situations should judge this attempt as a failure (metrics drop instead of rising, variance too large to reproduce, hard constraints violated, gains insufficient to justify maintenance costs)?
Only when the above is written into a question card does it become a problem definition that AI can use for efficient search and that humans can use for acceptance.

2. Drawing on Genesis's acceptance mechanism, explain how question quality determines whether AI can accumulate an experiment into a continuously evolving project outcome, and identify the human-AI division of labor within it.

Answer:
Reference answer:
(1) Genesis's mechanism treats code submitted by the Agent as a "candidate modification." Only after passing compilation, testing, behavior checks, and higher-level acceptance can it enter an accepted project version and become the historical foundation for subsequent executors; failed attempts are rejected.
(2) Whether the acceptance gate can function depends on whether the problem definition provides observable metrics, a verification protocol, and failure criteria. If these are not made clear at the questioning stage, acceptance has no basis for judgment, candidate modifications can only be approved by "it runs," results cannot accumulate, and the project cannot persistently build up across agent contexts and task cycles.
(3) Human-AI division of labor: AI is responsible for expanding the answer space and execution paths—generating candidates, searching, running tests; humans are responsible for problem definition, trade-off criteria, and scientific significance—delineating goals and conflicts, hard constraints, evaluation budgets, and failure criteria, and judging whether to ultimately accept. The higher the question quality, the more focused AI's search, the more grounded human acceptance, and the more likely the outcome becomes a runnable, evaluable, iterable workflow.