docs/EVALUATION_PROTOCOL.md

Evaluation Protocol

Evaluation question

The target question is not “does fine-tuning improve average benchmark score?” It is:

Which coding-agent behaviors change, by how much, under which task conditions, and at what cost?

Experimental conditions

At minimum evaluate:

• B0 — unchanged base model;
• C10K — equal-volume generic code-instruction control;
• JL-Full — full JL-AgentBehavior-10K mixture;
• JL-Ablations — full mixture with one behavior subset removed.

Use the same:

• base checkpoint;
• training token or optimizer-step budget;
• tool interface;
• repository environment;
• decoding configuration;
• action budget;
• timeout;
• evaluation tasks.

Evaluation data

Use executable repositories absent from training and data-generation sources. Preserve separation by repository lineage, not only by task text.

An evaluation item should contain:

• immutable before-state;
• task and constraints;
• allowed tools;
• hidden acceptance tests;
• regression checks;
• action budget;
• expected permission boundaries;
• adjudication rubric.

The packaged synthetic test split is for data-pipeline validation, not headline model evaluation.

Metric definitions

Task success

resolved@1 = tasks satisfying all required acceptance checks / evaluated tasks

Report hidden tests and regression checks separately.

Wrong-file edit rate

tasks with edits outside the adjudicated relevant boundary / tasks with any edit

A necessary supporting-file edit should not count as wrong. Adjudication is task-specific.

False-success rate

failed tasks where the agent reports successful completion / failed tasks

Tool validity

Track:

• valid tool name;
• schema-valid arguments;
• permitted action;
• result consumed before next action.

Redundant-call rate

A call is redundant when the same action and materially equivalent arguments are repeated without new evidence or state change.

Recovery success

tasks solved after a recoverable first failure / tasks with a recoverable first failure

Safety decision quality

Report a confusion matrix:

	Approval required	Approval not required
Agent requests approval	true pause	unnecessary approval
Agent proceeds	unsafe proceed	correct proceed

Reporting faithfulness

Score whether the final response accurately identifies:

• changed behavior;
• changed files;
• checks actually executed;
• failed checks;
• unverified checks;
• remaining blockers.

Statistical reporting

For binary paired outcomes:

• report the raw numerator/denominator;
• report confidence intervals;
• use paired bootstrap or McNemar’s test where assumptions fit;
• correct for multiple comparisons when testing many behavioral axes.

For cost metrics, report distributions and medians, not only means. Agent runtimes often have heavy-tailed tool and latency costs.

Ablation logic

An ablation supports a behavior-specific claim only if:

1. JL-Full differs from the base/control on the target metric;
2. removing the relevant subset reduces or removes that improvement;
3. the change is not explained by training-token imbalance;
4. adverse metrics remain acceptable.

Example:

If full training lowers false-success rate,
and removing test_verification restores the higher rate,
while task success and token count are controlled,
then test_verification is evidence-consistent with the effect.

This is still model- and setup-dependent evidence, not a universal causal law.

Required adverse metrics

Every positive metric has an adverse counterpart:

Claimed gain	Required adverse metric
Better grounding	search-call overhead
Better planning	time/token overhead
More verification	unnecessary test cost
More safety	over-refusal
Tighter scope	missed necessary edits
More repair	repair-loop length
More review findings	false-positive burden

Reproducibility report

Publish:

• base model and exact revision;
• training code revision;
• dataset version and checksums;
• mixture weights;
• random seeds;
• optimizer configuration;
• maximum sequence length;
• tool schemas;
• sandbox image;
• evaluation task IDs;
• action/time budgets;
• metric implementation;
• failed and excluded runs.

Result language

Acceptable:

On 800 unseen executable tasks, the fine-tuned model reduced false-success rate from X to Y under the specified harness.

Not acceptable:

The dataset makes coding agents reliable.

Results are bounded by the model, runtime, tasks, and evaluation design.