:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lee, Chaeeun, Yates, T. Michael, Minervini, Pasquale, Simpson, T. Ian
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2602.14160
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints
by: Gema, Aryo Pradipta, et al.
Published: (2024)

FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
by: Kim, Chaeeun, et al.
Published: (2025)

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages
by: Ghazaryan, Gayane, et al.
Published: (2024)

Low-Rank Compression of Language Models via Differentiable Rank Selection
by: Sundrani, Sidhant, et al.
Published: (2025)

Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning
by: Yu, Simon, et al.
Published: (2024)

VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models
by: Saxena, Rohit, et al.
Published: (2026)

On the Independence Assumption in Neurosymbolic Learning
by: van Krieken, Emile, et al.
Published: (2024)

FLARE: Faithful Logic-Aided Reasoning and Exploration
by: Arakelyan, Erik, et al.
Published: (2024)

PosterSum: A Multimodal Benchmark for Scientific Poster Summarization
by: Saxena, Rohit, et al.
Published: (2025)

Probing the Emergence of Cross-lingual Alignment during LLM Training
by: Wang, Hetong, et al.
Published: (2024)

Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
by: Sayin, Burcu, et al.
Published: (2024)

A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression
by: Devoto, Alessio, et al.
Published: (2024)

An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering
by: Murphy, Alexander, et al.
Published: (2025)

MedMMV: A Controllable Multimodal Multi-Agent Framework for Reliable and Verifiable Clinical Reasoning
by: Liu, Hongjun, et al.
Published: (2025)

Internalizing Outcome Supervision into Process Supervision: A New Paradigm for Reinforcement Learning for Reasoning
by: Ding, Fei, et al.
Published: (2026)

Lost in Time: Clock and Calendar Understanding Challenges in Multimodal LLMs
by: Saxena, Rohit, et al.
Published: (2025)

SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
by: Solano, Jesus, et al.
Published: (2023)

FairBelief -- Assessing Harmful Beliefs in Language Models
by: Setzu, Mattia, et al.
Published: (2024)

Process-Supervised Reinforcement Learning for Interactive Multimodal Tool-Use Agents
by: Tan, Weiting, et al.
Published: (2025)

VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
by: Yue, Yu, et al.
Published: (2025)

DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning
by: He, Yang, et al.
Published: (2026)

Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning
by: Yang, Pei, et al.
Published: (2025)

A Policy-Driven Runtime Layer for Agentic LLM Serving
by: Zhang, Rui, et al.
Published: (2026)

Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning
by: Hu, Zhiyuan, et al.
Published: (2026)

Process-Supervised Reinforcement Learning for Code Generation
by: Ye, Yufan, et al.
Published: (2025)

Strat-Reasoner: Reinforcing Strategic Reasoning of LLMs in Multi-Agent Games
by: He, Yidong, et al.
Published: (2026)

Closing Reasoning Gaps in Clinical Agents with Differential Reasoning Learning
by: Liu, Jinsong, et al.
Published: (2026)

SuperRL: Reinforcement Learning with Supervision to Boost Language Model Reasoning
by: Liu, Yihao, et al.
Published: (2025)

DRAFT-RL: Multi-Agent Chain-of-Draft Reasoning for Reinforcement Learning-Enhanced LLMs
by: Li, Yuanhao, et al.
Published: (2025)

Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets
by: Zhang, Simpson, et al.
Published: (2025)

Conditional computation in neural networks: principles and research trends
by: Scardapane, Simone, et al.
Published: (2024)

Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
by: Deng, Yihe, et al.
Published: (2025)

Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
by: Jo, Yonghyeon, et al.
Published: (2026)

Mixtures of In-Context Learners
by: Hong, Giwon, et al.
Published: (2024)

A Multi-Agent Approach to Neurological Clinical Reasoning
by: Sorka, Moran, et al.
Published: (2025)

Agentic Uncertainty Reveals Agentic Overconfidence
by: Kaddour, Jean, et al.
Published: (2026)

Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways
by: Ardimento, Pasquale, et al.
Published: (2026)

Process Reward Agents for Steering Knowledge-Intensive Reasoning
by: Sohn, Jiwoong, et al.
Published: (2026)

AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning
by: Fu, Suzhong, et al.
Published: (2026)

Concept Learning for Cooperative Multi-Agent Reinforcement Learning
by: Ge, Zhonghan, et al.
Published: (2025)