Shared communication and evaluation tools

benchkit/ provides shared SIM communication, central case scheduling, data/result formats, recording, source snapshots, audits and reports. Its libraries and commands are not a mandatory per-action control process. Agent-side decision loops live under AgentPolicies/policy_support/; sim_server/ implements transport, serialization and session lifecycle used by benchmark-owned servers and trusted drivers. Native tasks/scorers and model decisions do not belong here.

contracts.py / interfaces.py: public observation, action and lifecycle types.
methods.py: protocol-first method composition and per-component usage declarations.
recording.py: default-on optional interaction/client retention preferences and SDK sink.
registry.py: explicit lazy extension registration; no eager native SDK imports.
sim_server/: the only shared SIM communication implementation; not a compatibility alias.
campaigns/: retained central task coordination: fixed-plan preparation, permanent case claims, explicit launches and progress. It currently executes frozen local adapters, not remote service campaigns; it is not an agent decision loop, automatic retry engine or distributed resource scheduler.
catalog/: packaged benchmark declarations and their loader/validator.
cli.py: shared catalog/diagnostic commands; participant entrypoints can use the service protocol directly.
model_profiles.py: configuration/factory validation and bounded model-source capture; actual factories remain under AgentPolicies/<profile>/.
source_snapshot.py: bounded output-tool, benchmark-service and explicitly declared trusted-driver source capture. An inventory's storage path does not change the ownership of the captured code.
diagnostics/: shared synthetic and fixed-action infrastructure fixtures, captured with the harness source; these are not participant models or task-solving baselines.
audits/: offline record validation, not native replay or provider attestation.
reports/: shared run summaries, campaign coverage, historical result indices, fixed-plan SR comparison and named-model publication. The panel report preserves policy failures, infrastructure errors and missing closed results; it does not select or retry cases or infer that missing evidence means no attempt.
checks/ / artifact_checks/: lightweight regression and ownership checks.

Native adapters and selection plans live under benchmarks/simBenchmarks. Policies, shared policy support and results live under AgentPolicies.

python -m benchkit catalog --check
python -m benchkit list
python -m benchkit.campaigns --help
python -m benchkit.checks.check_boundaries

From a repository checkout, run the CPU-only regression groups with a Python environment that already provides NumPy, Pillow and PyAV:

python -B -m benchkit.checks.run_regressions --jobs 3 --output /absolute/new/cpu-check-report

This retains per-group logs and a JSON summary without starting a native simulator, a model or a hardware session. Keep the source tree unchanged while the checks run: source-freezing fixtures intentionally reject concurrent edits. Checks are not packaged in the distribution wheel. make check parses active source and checks ownership/catalog layout without traversing frozen results.

The console entrypoint remains starvla-eval. Campaign commands use python -m benchkit.campaigns; history adoption uses python -m benchkit.reports.result_index. Removed flat modules and the benchkit.runner bridge have no compatibility shims. All live decision-loop callers import AgentPolicies.policy_support.runner directly. Old frozen campaigns retain their original package names, bytes and verification rules; never rewrite a frozen package just to update an import path. This directory migration does not start evaluations or complete native full presets.

Closed coverage reports bind the run's declared policy identity, model profile, prompt and stop settings to its campaign; mismatches remain invalid evidence in the denominator. This checks record consistency, not provider attestation. The participant-coverage reporter rejects declared infrastructure-diagnostic campaigns; their raw native outcomes remain available through diagnostic indices. reports/result_index.py is specifically for adopting closed anonymous historical runs, not a general named-model importer. It rejects explicit non-anonymous profiles even when their provider/model fields are null, and marks fixed-action infrastructure diagnostics separately with archived media. Original native outcomes and source records are never rewritten by either reporting tool.

For ordinary closed results use benchkit.reports.basic_result; it does not require events, media or source snapshots. Optional interaction_history needs sufficient event evidence. See recording/results. Neither report makes Python Policy mandatory or attests native/provider execution.

The specialized benchkit.reports.publication turns one already-closed, complete named-model panel into a compact public view. It checks the raw summary, status, manifest, panel and episode counts, records hashes of those inputs, exports selected media with descriptive names, and writes only under the model's results/<benchmark>/ directory. It never moves raw evidence or changes a score.

python -B -m benchkit.reports.publication \
  --raw-run AgentPolicies/gpt6_astra/results/robodojo/raw/accepted-run \
  --results-root AgentPolicies/gpt6_astra/results/robodojo \
  --task-description classify_objects=category-sorting