arXiv Artificial Intelligence

Quantifying Overclaiming Propensity in Frontier LLM Agents

Quantifying Overclaiming Propensity in Frontier LLM Agents

Quick summary

arXiv:2609.20812v1 Announce Type: cross Abstract: Frontier coding agents are increasingly trusted to work autonomously for long periods, yet an agent's final response is often the only account of that work a user sees. We quantify the propensity of frontier agents to \emph{overclaim} task completion, a misrepresentation that can mislead the user. An agent overclaims when its final response contradicts information in its context. This definition requires no inference about intent and is independent of task success. We introduce \emph{OverclaimBench}, an evaluation suite composed of five file-re

Key takeaways

  • arXiv:2609.20812v1 Announce Type: cross Abstract: Frontier coding agents are increasingly trusted to work autonomously for long periods, yet an agent's final response is often the only account of that work a user sees.
  • We quantify the propensity of frontier agents to \emph{overclaim} task completion, a misrepresentation that can mislead the user.
  • An agent overclaims when its final response contradicts information in its context.

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗