arXiv AI
GRUET: Quantifying Uncertainty of Agentic Reasoning-and-Acting Processes
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From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
The paper introduces RUPA, a trajectory‑level uncertainty quantification framework for large language model agents. RUPA models an agent’s execution as a directed graph of reasoning states, tool interactions, and environment feedback, then propagates uncertainty across this graph to capture long‑range dependencies. Experiments on benchmarks such as τ‑2, Terminal‑Bench‑2, and GAIA show that RUPA outperforms existing methods, enabling earlier failure detection and more reliable agent execution.
arXiv AI
Sep 7
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity
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arXiv AI
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Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
arXiv:2609.09030v1 Announce Type: new Abstract: Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accur...
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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.
arXiv AI
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Geometry of Divergence: Tracking Hidden-State Trajectories for Adaptive Multi-Turn Reasoning
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arXiv AI
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Decoupling Thought from Speech: Knowledge-Grounded Counterfactual Reasoning for Resilient Multi-Agent Argumentation
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