[Submitted on 8 Sep 2026]
Title:Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations
View a PDF of the paper titled Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations, by Priyanka Mary Mammen and 1 other authors
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Abstract:As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. In comparison to the traditional machine learning systems, agentic workflows have complex failure modes with planning, tool invocation and dynamic environment interactions. In this paper, we investigate whether model's internal representations provide stronger signals of eventual task success in multi-turn agentic setups. We introduce two complementary methods: Latent Trajectory Dynamics (LTD), which summarizes changes in residual-stream representations across an an interaction trajectory, and the Action Representation Probe (ARP), which predicts success from representations formed at action decisions. Across three interactive benchmarks (Bash, SQL, Python) and three model families (Qwen14B, Qwen7B, DeepSeek6.7B), our methods consistently outperform surface level generation and sequence-based calibration baselines providing a zero-overhead reliability monitor that requires neither prompt alterations nor multi-sample rollouts.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.09448 [cs.AI]
(or arXiv:2609.09448v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.09448
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Priyanka Mary Mammen [view email] [v1] Tue, 8 Sep 2026 20:58:49 UTC (23 KB)
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