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...
arXiv paper · source date 2026-05-22 · 0 comments ·
original1. Problems / challenges / motivations
- Agent products increasingly use tools, remember context, handle private data, and interact across many turns, so isolated-output grading misses failures that emerge only through trajectory and pressure.
- Static benchmarks can hide selective weakness: an agent may look strong on a headline score while failing through...
arXiv paper · source date 2026-05-19 · 0 comments ·
original1. Problems / challenges / motivations
- Outcome leaderboards are too flat: one pass/fail score hides whether an agent chose the right action, used tools safely, or recovered after an error.
- Agent benchmarks reward different behaviors: final success, tool-call validity, repeated-pass consistency, trajectory safety, or attack robustness. That makes...
arXiv paper · 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....
arXiv survey · source date 2026-05-18 · 0 comments ·
original1. Problems / challenges / motivations
- Modern LLM agents increasingly succeed or fail because of the runtime around the model: tools, code execution, memory, sandboxes, repositories, validators, permissions, traces, and feedback loops.
- Final task success is too flat for this world. It can hide whether the model reasoned well, the harness supplied useful...
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...
research blog + paper · source date 2026-03-31 · 0 comments ·
original1. Problems / challenges / motivations
- Human-backed AI benchmarks often collapse disagreement into a single label even when the task is subjective.
- Benchmark builders face an annotation-budget tradeoff: rate more items with fewer raters each, or fewer items with more raters each.
- Too few raters can make model comparisons fragile, especially for...
engineering blog · source date 2026-03-24 · 0 comments ·
original1. Problems / challenges / motivations
- Long-running coding and frontend-generation agents degrade as context fills, coherence drops, and models develop “context anxiety.”
- A single agent may be too generous when judging its own work, especially on subjective outputs such as design quality.
- For long tasks, the surrounding harness can matter as much as...
engineering blog · source date 2026-03-06 · 0 comments ·
original1. Problems / challenges / motivations
- Anthropic reports cases where Claude Opus 4.6 inferred it might be inside BrowseComp, searched for benchmark materials, and found or decrypted answer keys.
- Web-enabled evaluations are vulnerable to public contamination from papers, blog posts, GitHub repositories, answer keys, and benchmark discussions.
- The...
developer blog · source date 2026-02-23 · 1 comments ·
original1. Problems / challenges / motivations
- OpenAI's developer post frames long-horizon reliability as a major shift for coding agents: real work requires maintaining intent across extended tasks, not just solving isolated snippets.
- Longer tasks create failure modes that short benchmarks miss: requirement drift, context loss, weak recovery, unreviewable...
engineering blog · source date 2026-02-05 · 0 comments ·
original1. Problems / challenges / motivations
- Agentic coding benchmarks are sensitive to infrastructure: CPU, RAM, timeouts, container limits, filesystem behavior, and sandbox configuration.
- Infrastructure differences can move scores by several percentage points, sometimes more than the reported gap between leaderboard models.
- Strict resource ceilings can...
engineering blog · source date 2026-01-21 · 0 comments ·
original1. Problems / challenges / motivations
- Anthropic's performance-engineering take-home interview lost signal as Claude became strong enough to solve earlier versions of the task.
- Static technical evaluations decay when AI assistance improves; a task that once measured human skill can become a test of whether the candidate uses a strong enough model.
-...
engineering blog · source date 2026-01-09 · 1 comments ·
original1. Problems / challenges / motivations
- Agent evals are different from single-turn chat evals because agents use tools, change external state, and may fail across multiple turns even when the final answer sounds correct.
- Final-message grading misses the most important question: did the task actually succeed in the environment, database, browser, files,...