Hugging Face Trending Papers
SciExam for ENSO: Can AI Agents Build Climate Models?
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arXiv Machine Learning
Oct 8
SciExam for ENSO: Can AI Agents Build Climate Models?
arXiv:2610.10513v1 Announce Type: cross Abstract: Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer...
arXiv AI
Oct 15
Delegating Earth science model calibration to knowledge-guided LLM agents
arXiv:2610.18378v1 Announce Type: new Abstract: Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-mod...
arXiv AI
Sep 3
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
The paper introduces Belief-Calibrated Optimization (BCO), a method that records and updates a persistent in‑context document representing an agent’s belief about how the environment responds to edits. By continually revising this world model as new candidates are evaluated, BCO improves the performance of frozen LLM agents across five benchmarks, outperforming a control lacking the world model. An offline ablation shows that the document’s content provides reusable, accurate predictions of environmental responses, beyond mere form.
arXiv AI
Oct 21
Ask the World Before Acting: Environment Probing for Calibrating Agent World Models
arXiv:2606.31422v3 Announce Type: replace Abstract: Long-horizon language agents enable autonomous decision-making in complex environments by planning and executing sequences of interdependent action...
Hugging Face Trending Papers
Jul 9
What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both.
arXiv AI
Oct 15
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
arXiv:2610.14381v1 Announce Type: new Abstract: Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system...