3 papers
cs.AI2026
Proper Scoring Rules for Agentic Uncertainty Quantification
Suresh Raghu, Satwik Pandey, Shashwat Pandey
Language-model agents increasingly emit uncertainty signals throughout a trajectory, but existing agentic UQ evaluations often conflate ranking usefulness with probabilistic truthf…
cs.AI2026
SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio
Satwik Pandey, Suresh Raghu, Shashwat Pandey
Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, while common single-pass proxies…
cs.AI2026
Don't Blink: Evidence Collapse during Multimodal Reasoning
Suresh Raghu, Satwik Pandey
Reasoning VLMs can become more accurate while progressively losing visual grounding as they think. This creates task-conditional danger zones where low-entropy predictions are conf…