# Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actions

arXiv: https://arxiv.org/abs/2511.13240 · HTML: https://mlsanity.com/paper/2511.13240/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 21 of 21)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://mlsanity.com/paper/2302.09664/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Measuring short-form factuality in large language models](https://mlsanity.com/paper/2411.04368/) (2024)
- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://mlsanity.com/paper/2308.08469/) (2023)
- [Sycophancy in Large Language Models: Causes and Mitigations](https://mlsanity.com/paper/2411.15287/) (2024)
- [Large Language Models Must Be Taught to Know What They Don't Know](https://mlsanity.com/paper/2406.08391/) (2024)
- [Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey](https://mlsanity.com/paper/2503.15850/) (2025)
- [Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate](https://mlsanity.com/paper/2305.13160/) (2023)
- [Large language model validity via enhanced conformal prediction methods](https://mlsanity.com/paper/2406.09714/) (2024)
- [Large Language Models for Disease Diagnosis: A Scoping Review](https://mlsanity.com/paper/2409.00097/) (2024)
- [Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities](https://mlsanity.com/paper/2402.10835/) (2024)
- [StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?](https://mlsanity.com/paper/2510.02209/) (2025)
- [UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making](https://mlsanity.com/paper/2506.17419/) (2025)

## Built on by (most cited first, 4 of 4)

- [BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation](https://mlsanity.com/paper/2606.30850/) (2026)
- [Beliefs and Behavior in Language Models](https://mlsanity.com/paper/2609.07943/) (2026)
- [Diagnosing and Improving Probabilistic Reasoning in Large Language Models](https://mlsanity.com/paper/2609.38005/) (2026)
- [Training Language Models To Be Coherent Decision-Makers](https://mlsanity.com/paper/2610.09164/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly deployed in agentic and multi-turn workflows where they are tasked to perform actions of significant consequence. In order to deploy them reliably and manage risky outcomes in these settings, it is helpful to access model uncertainty estimates. However, confidence elicitation methods for LLMs are typically not evaluated directly in agentic settings; instead, they are evaluated on static datasets, such as Q&A benchmarks. In this work we investigate the relationship between confidence estimates elicited in static settings and the behavior of LLMs in interactive settings. We uncover a significant action-belief gap -- LLMs frequently take actions that contradict their elicited confidences. In a prediction market setting, we find that models often bet against their own high-confidence predictions; in a tool-use setting, models fail to reliably invoke information-seeking tools when their internal confidence is low; and in a user-challenge setting, models change their answers when they have high confidence in them, whilst sticking to answers they have low confidence in. Crucially, we show that static calibration is an insufficient predictor of consistency in the above dynamic settings, as stronger, better calibrated models are somtimes less consistent than their smaller and weaker open-source counterparts. Our results highlight a critical blind spot in current evaluation methodologies: ensuring that a model knows what it knows does not guarantee that it will act rationally on that knowledge.