BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)
Copied to clipboard
Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou
| Challenge: | Existing work on confidence in LLMs is limited. |
| Approach: | They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level. |
| Outcome: | The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods. |
Similar Papers
The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents (2026.acl-long)
Copied to clipboard
| Challenge: | a fundamental pillar of trustworthiness is calibration, which refers to an agent’s ability to express confidence that reliably reflects its actual performance. |
| Approach: | They propose a reinforcement learning framework that jointly optimizes task accuracy and calibration, supported by a holistic benchmark of reward designs. |
| Outcome: | The proposed framework improves calibration across tool types and shows that trained agents achieve superior calibration and exhibit robust generalization from local training environments to noisy web settings and to distinct domains such as mathematical reasoning. |
Confidence Estimation for LLMs in Multi-turn Interactions (2026.findings-acl)
Copied to clipboard
Caiqi Zhang, Ruihan Yang, Xiaochen Zhu, Chengzu Li, Tiancheng Hu, Yijiang River Dong, Deqing Yang, Nigel Collier
| Challenge: | Despite recent progress, most prior work studies confidence in single-turn question answering. |
| Approach: | They propose a logit-based probe that measures confidence in multi-turn dialogues . they propose 'infoECE' and a "hinter-guesser" paradigm for generating controlled evaluations based on data . |
| Outcome: | The proposed framework is grounded in calibration and monotonicity of confidence as more information becomes available. |
Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMs (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing uncertainty quantification methods treat each step in isolation, ignoring how confidence evolves and compounds across a full task trajectory. |
| Approach: | They propose a framework for trajectory-level confidence analysis in the tool-use agent setting. |
| Outcome: | The proposed framework will expose early warning signals for agent failure and offer interpretable diagnostic tools for understanding when and why LLM agents lose confidence. |
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate. |
| Approach: | a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights . |
| Outcome: | The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc. |
Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering (2025.acl-short)
Copied to clipboard
| Challenge: | Existing evaluations of test-time scaling assume that a reasoning system should always give an answer to any question provided. |
| Approach: | They propose to increase compute budget at inference time to increase confidence in correct responses by considering settings with non-zero levels of response risk. |
| Outcome: | The proposed model can answer more questions correctly and have higher confidence in correct responses. |
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used in high-stakes areas such as healthcare, law, and education. |
| Approach: | They propose a concept of Confidence-Probability Alignment that connects an LLM’s internal confidence to the confidence conveyed in the model’s response when explicitly asked about its certainty. |
| Outcome: | The proposed model shows the strongest confidence-probability alignment across a wide range of tasks. |
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have revolutionized inference across diverse natural language tasks, with larger models performing better but at higher computational costs. |
| Approach: | They propose a confidence-driven strategy that dynamically selects the most suitable model based on confidence estimates. |
| Outcome: | The proposed approach reduces token usage by approximately 60% and improves cost efficiency on the Massive Multitask Language Understanding (MMLU) benchmark. |
Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence (2026.acl-long)
Copied to clipboard
| Challenge: | Guided by Gut (GG) is an efficient self-guided TTS framework for Large Language Models (LLMs) that performs step-by-step reasoning at a low cost without any reward models or verifiers. |
| Approach: | They propose a self-guided TTS framework that enables LLMs to perform step-by-step reasoning at a low cost without any reward models or verifiers. |
| Outcome: | Empirical evaluations show that GG performs better than TTS with PRMs while reducing GPU memory usage by up to 10. |
Enhancing Multi-Agent Debate System Performance via Confidence Expression (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-Agent Debate systems leverage multiple LLMs to improve task performance. |
| Approach: | They propose to integrate confidence expression into MAD systems to help LLMs communicate their confidence levels. |
| Outcome: | The proposed approach improves debate effectiveness and overall system performance by integrating confidence expression into MAD systems. |
Beyond Blind Following: Evaluating Robustness of LLM Agents under Imperfect Guidance (2026.eacl-long)
Copied to clipboard
Yao Fu, Ran Qiu, Xinhe Wang, Jacob Sansom, Sathvika Ayyappa Prabhu, Huijie Tang, Jaekyeom Kim, Sungryull Sohn, Honglak Lee
| Challenge: | Large language models (LLMs) have shown strong capabilities as task-solving agents across interactive domains, but in complex environments, auxiliary guidance may be imperfect. |
| Approach: | They propose a benchmark to measure the robustness of large language models under imperfect guidance. |
| Outcome: | The proposed benchmark compared LLM agents in navigation, cooking, and gaming in a variety of environments with auxiliary guidance and noisy or underspecified instructions extracted from demonstrations. |