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* 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