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Quantifying Overclaiming Propensity in Frontier LLM Agents

Paper recorded by Signals 4 on 2026-09-17 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-17 on arXiv · recorded by Signals 4 on 2026-09-18

Category: cs.AI · 人工智能 · first seen 2026-09-18

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 r

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#6 most recent of 300 cs.AI papers we have recorded · ↑ newer: ERCPMP-Gx: Endoscopic Image and Video Dataset for Morphological, Histo · ↓ older: An Empirical Study of Harness Design for Coding Agents
Cite this page: Quantifying Overclaiming Propensity in Frontier LLM Agents: the #6 most recent of 300 cs.AI papers we have recorded (as of 2026-09-17). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/quantifying-overclaiming-propensity-in-frontier-llm-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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