* feat: add deterministic eval graders (AGI SDK + WebArena-Infinity)
Two new benchmark integrations with programmatic grading — no LLM judge.
AGI SDK / REAL Bench (52 tasks):
- 11 React/Next.js clones of consumer apps (DoorDash, Amazon, Gmail, etc.)
- Grader navigates browser to /finish, extracts state diff from <pre> tag
- Python verifier checks exact values via jmespath queries
WebArena-Infinity (50 hard tasks):
- 13 LLM-generated SaaS clones (Gmail, GitLab, Linear, Figma, etc.)
- InfinityAppManager starts fresh app server per task per worker
- Python verifier calls /api/state and asserts on JSON state
Infrastructure:
- GraderInput extended with mcpUrl + infinityAppUrl for parallel workers
- Each worker gets isolated ports (no cross-worker state contamination)
- CI workflow: pip install agisdk, clone webarena-infinity repo
* chore: switch eval configs back to kimi-k2p5
* fix: register deterministic graders in pass rate calculation
Add agisdk_state_diff and infinity_state to PASS_FAIL_GRADER_ORDER
in both runner types and weekly report script, so scores show correctly
in the dashboard.
* chore: temp switch to opus 4.6 for eval run
* chore: restore kimi-k2p5 as default eval config
* ci: add timeout and continue-on-error for trend report step
