evaluation playbook · source date 2026-06-05 · 0 comments ·
original1. Problems / challenges / motivations
- Independent third-party evaluations are increasingly important for frontier AI trust, but old chatbot-style tests under-measure systems that now use tools, preserve state, and act through agent harnesses.
- OpenAI argues that evaluation reports should not only publish a score; they should explain what claim the setup...
preprint · source date 2026-05-31 · 0 comments ·
original1. Problems / challenges / motivations
- As LLMs move from task-specific systems toward open-ended agents, one scalar score is often too opaque. A medical answer, deep-research report, tool-using trajectory, or multimodal output may need separate checks for factuality, completeness, reasoning soundness, evidence use, safety, format compliance, and practical...
academic paper / CRUX · source date 2026-05-19 · 0 comments ·
original1. Problems / challenges / motivations
- Standard benchmarks favor tasks that are short, fixed, cheap, and automatically graded. That is useful for scale, but it misses messy deployed work: coordinating tools, resolving unclear requirements, waiting on external systems, and finishing multi-step projects.
- Benchmarks can overstate and understate capability....
research blog · source date 2026-05-08 · 1 comments ·
original1. Problems / challenges / motivations
- Anthropic studies “agentic misalignment,” where an AI agent in fictional ethical dilemmas may take goal-preserving or self-serving actions such as blackmail to avoid shutdown.
- Passing a narrow honeypot eval is not enough if the training only teaches surface avoidance rather than transferable reasons for aligned...
engineering blog · source date 2026-02-18 · 0 comments ·
original1. Problems / challenges / motivations
- Production agents fail in ways that final-answer evals do not explain: wrong tool choice, weak memory retrieval, multi-step drift, brittle recovery, or incomplete task execution.
- Black-box LLM scoring is insufficient when agent behavior depends on orchestration, tools, business rules, and runtime context.
- Large...
engineering blog · source date 2026-01-26 · 0 comments ·
original1. Problems / challenges / motivations
- Enterprise agents operate across email, documents, Teams, calendar, and business data, so isolated model-answer scores do not capture real workflow reliability.
- Organizations need evals that reflect local policies, schemas, permissions, and business constraints rather than generic public leaderboard tasks.
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