docs/BEHAVIOR_SPEC.md

Behavioral Specification

Purpose

JL-AgentBehavior-10K models coding-agent quality as a vector of observable behaviors rather than a single task-success label. This specification defines the behavioral axes, target policies, failure patterns, and measurement boundaries used in version 1.0.0.

A behavior is included only when it can be connected to:

1. an observable agent decision;
2. a supervision signal;
3. a measurable outcome;
4. an adverse or counter-metric.

System model

The dataset assumes an agent loop with the following state transition:

task + constraints
  -> observation
  -> repository/tool evidence
  -> policy decision
  -> action
  -> environment result
  -> state update
  -> verification or repair
  -> final report

The dataset supervises decisions and action selection. It does not establish that a synthetic environment result is true.

Behavioral axes

1. Repository grounding

Target policy: Locate and inspect relevant repository evidence before selecting an edit target.

Positive indicators

• searches for the named behavior, symbol, or error;
• reads the implementation and nearest relevant test;
• references paths returned by evidence;
• updates the hypothesis when repository structure contradicts the initial assumption.

Failure indicators

• invents files or symbols;
• edits a guessed path;
• reads the entire repository without a retrieval strategy;
• treats the user’s wording as proof of implementation location.

Primary metrics

• wrong-file edit rate;
• unsupported path/symbol reference rate;
• relevant context recall.

Counter-metrics

• redundant search calls;
• context tokens consumed before a useful action.

2. Planning and decomposition

Target policy: Use the shortest plan that makes a multi-step task controllable and revise it when evidence changes.

Positive indicators

• identifies implementation and verification boundaries;
• separates investigation, edit, and validation;
• keeps steps executable;
• avoids planning overhead for trivial tasks.

Failure indicators

• broad redesign hidden inside a small fix;
• plan items that cannot be verified;
• long narrative instead of executable steps;
• continuing a stale plan after contradictory evidence.

Primary metrics

• plan adherence;
• plan revision quality;
• multi-step task success.

Counter-metrics

• time to first useful action;
• plan-token overhead;
• unnecessary step rate.

3. Tool selection and execution

Target policy: Choose the lowest-cost valid tool that reduces uncertainty or advances the verified task.

Positive indicators

• targeted search before broad inspection;
• schema-correct arguments;
• narrow tests before a full suite;
• no repeated call without state change.

Failure indicators

• invalid tool arguments;
• repeated identical calls;
• broad commands when a narrow one is available;
• editing before required evidence exists.

Primary metrics

• correct tool@1;
• invalid call rate;
• redundant call rate.

Counter-metrics

• total tool cost;
• latency;
• unnecessary environment access.

4. Bounded code editing

Target policy: Make the minimum sufficient architecture-aligned change.

Positive indicators

• preserves the public contract when required;
• reuses existing abstractions;
• edits only relevant files;
• adds narrow regression coverage when appropriate.

Failure indicators

• unrelated refactor;
• dependency addition without need;
• API change outside scope;
• generated replacement that ignores local conventions.

Primary metrics

• patch applicability;
• scope violation rate;
• regression rate.

Counter-metrics

• patch underfitting;
• failure to update necessary callers or tests.

5. Test and verification discipline

Target policy: Use executable evidence before claiming successful completion.

Positive indicators

• selects a targeted test tied to the acceptance criterion;
• runs static checks relevant to changed code;
• inspects the final diff;
• labels unexecuted checks as unverified.

Failure indicators

• fabricated “all tests pass” statement;
• running an unrelated test;
• treating compilation as behavioral correctness;
• treating one targeted test as proof of all regressions.

Primary metrics

• false-success rate;
• targeted-test selection accuracy;
• fabricated result rate.

Counter-metrics

• unnecessary test calls;
• full-suite cost;
• over-testing trivial changes.

6. Failure diagnosis and repair

Target policy: Convert failure evidence into an updated hypothesis and a non-repeating repair action.

Positive indicators

• classifies the failure signal;
• inspects the failing path;
• explains which assumption was disproven;
• reruns the relevant check after repair.

Failure indicators

• repeats the same patch;
• changes unrelated code;
• ignores the actual failure output;
• stops after the first recoverable failure.

Primary metrics

• recovery success;
• repeated-action rate;
• attempts to recovery.

Counter-metrics

• repair-loop length;
• new regressions introduced by repair.

7. Code review and security

Target policy: Produce only evidence-backed, actionable findings with calibrated severity and confidence.

Positive indicators

• identifies a concrete execution path;
• distinguishes correctness, security, compatibility, and style;
• points to affected code;
• suggests a bounded remediation.

Failure indicators

• stylistic preference labeled as critical;
• theoretical concern without reachable path;
• missed trust boundary;
• review summary with no evidence.

Primary metrics

• valid finding precision;
• security finding recall;
• severity calibration.

Counter-metrics

• false-positive burden;
• review latency.

8. Permission and scope safety

Target policy: Pause at a real trust boundary and proceed with safe, reversible, in-scope local work.

Positive indicators

• requests approval for destructive or external state changes;
• explains why approval is required;
• does not request approval for ordinary local inspection;
• preserves user changes and task scope.

Failure indicators

• destructive operation without authorization;
• unnecessary refusal;
• silent scope expansion;
• secret exposure.

Primary metrics

• unsafe action rate;
• correct approval-decision rate;
• scope violation rate.

Counter-metrics

• over-refusal;
• unnecessary approval prompts.

9. Final reporting

Target policy: State what changed, what was actually checked, and what remains uncertain.

Positive indicators

• reports changed behavior;
• names executed verification;
• distinguishes targeted checks from a full suite;
• discloses blockers or unverified assumptions.

Failure indicators

• generic “done” response;
• unsupported success claim;
• omitted limitation;
• process narration without outcome.

Primary metrics

• evidence completeness;
• fabricated verification rate;
• uncertainty calibration.

Counter-metrics

• unnecessary verbosity;
• duplicated implementation detail.

Multi-label policy

Each record has one primary_behavior used for quota control and ablation. Up to two secondary_behaviors express cross-cutting effects. Evaluation should report the primary axis first and avoid double-counting a single record as independent evidence for multiple axes.

Reasoning representation

The release stores short, inspectable observations and decision bases. It intentionally avoids long hidden-chain-of-thought style supervision. The target representation is:

observable state -> bounded decision basis -> action -> expected evidence

This makes review and masking easier and reduces incentives for verbose internal narration.

Evidence tiers

Tier	Meaning
silver_structural	Schema-valid synthetic policy record; not executed
gold_executed	Materialized environment, recorded tools, applicable patch, executed checks
gold_reviewed	Gold executed plus human adjudication and provenance review

Only silver_structural appears in v1.0.0.