2026-10-11 17:10 UTC

Independent evaluations will determine whether AgentAbstain reliably measures when LLM agents should abstain and whether current agents consistently avoid inappropriate actions.

state: expiredheat: lowuncertainty: highconvergesscott: mediumagent-abstention agent-evaluation

What is this?

AgentAbstain is a benchmark and evaluation framework for testing whether tool-using LLM agents recognize when they should not act, including triggers visible before execution and those discovered at runtime. It contains 263 paired tasks across 42 executable environments, organized around eight abstention scenarios; its project site reports that the best of 17 frontier models correctly handled only 59.5% of task pairs. The supplied evidence comes mainly from the paper, project site, and derivative coverage, so it establishes the benchmark authors’ findings but not yet independent validation of its methodology or results.

Why it matters to Scott

AgentAbstain operationalizes Scott’s Stand-Pat/null-candidate principle and supplies preliminary evidence for Architecture, Not Vibes: agents cannot yet be trusted to self-police action reliably, so abstention must be tested and backed by external controls. If independently validated, the benchmark could become a useful evaluation suite for SiloOS and earned-autonomy decisions, but the supplied results are still author-reported.
ip:concept.stand-patip:framework.architecture-not-vibesip:framework.two-leashesip:concept.evaluation-driven-developmentdev:project.silo-osradar:concept.agent-safetyradar:concept.ai-benchmarksradar:concept.benchmark-integrityradar:concept.agent-harnesses
queries asked of Scott's wikis
  • agent halt conditions and abstention policies
  • evaluation harnesses for unsafe or irreversible tool actions
  • human approval gates for autonomous agents
  • confidence calibration versus action authorization
  • runtime constraint discovery and fail-safe behavior
  • paired counterfactual tests for agent reliability

Measured heat

no measured readings yet — the hourly heat pass fills this in

How the heat travelled

no chain yet — the hourly chain pass fills this in

Evidence (3) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟧 hnAgentAbstain: Do LLM Agents Know When Not to Act?embedding-shape20
🟧 echo.paper ⭐Introduces AgentAbstain to evaluate whether LLM agents recognize when they should not act.AgentAbstain authors——
🟧 hnFrom knowledge-based inference to presence-based verificationoffaxis10

Interpretation history

Decision trace