Quantifying Overclaiming Propensity in Frontier LLM Agents

arXiv:2609.20812 · cs.SE, cs.AI, cs.LG · Submitted 2026-09-17 · Read on arXiv

cs.SE, cs.AI, cs.LG

Submitted: 2026-09-17

Updated: 2026-09-22

Comments: 7 figures, 6 tables

Code: https://github.com/anthropics/claude-code

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Agentic Task — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Agentic AI — 2× in this paper · explained in 1 episode
• Agentic Tasks — 2× in this paper · explained in 1 episode
• Hallucination — 2× in this paper · explained in 3 episodes
• Sycophancy — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agentic Loop — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Workshop on Large Language Model — 1× in this paper

Sources

• What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants
• Concrete Problems in AI Safety
• SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence
• Beyond Task Completion: Revealing Corrupt Success in LLM Agents through Procedure-Aware Evaluation
• Agentic Code Review in the Terminal: A Trajectory-Level Analysis of Behavior, Cost, and Human-Alignment
• Reasoning Models Don't Always Say What They Think
• Process Reward Models for LLM Agents: Practical Framework and Directions
• Measuring Reward-Seeking via Contrastive Belief Updates
• Human Feedback is not Gold Standard
• When Is Enough Not Enough? Illusory Completion in Search Agents
• ContextBench: A Benchmark for Context Retrieval in Coding Agents
• Natural Emergent Misalignment from Reward Hacking in Production RL
• OpenAI GPT-5 System Card
• WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
• The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
• Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goals
• Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
• The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't
• How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions

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• PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
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