Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Jul 21

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

arXiv:2505. 02722v2 Announce Type: replace Abstract: Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited.

By Junu Kim, Chaeeun Shim, Sungjin Park, Su Yeon Lee, Gee Young Suh, Chae-Man Lim, Seong Jin Choi, Song Mi Moon, Kyoung-Ho Song, Eu Suk Kim, Hong Bin Kim, Sejoong Kim, Chami Im, Dong-Wan Kang, Yong Soo Kim, Hee-Joon Bae, Sung Yoon Lim, Han-Gil Jeong, Edward Choi

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 21

OR Else: A Differentiable Trust Region for Policy Optimization

arXiv:2607. 18163v1 Announce Type: cross Abstract: PPO and the GRPO baseline studied here use clipped surrogate objectives whose favorable-direction saturation introduces an abrupt change in the scalar objective's derivative.

By Chinmay Rane, Kanishka Tyagi, Michael Manry

llms reinforcement-learning

arXiv AI

Jul 21

PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization

arXiv:2607. 16206v1 Announce Type: new Abstract: This paper introduces PPO-HSC (Proximal Policy Optimization with High-order Sampling Coverage), an exploratory reinforcement learning framework designed to address the "Invisible Shackles" of mode collapse in Large Language Model (LLM) fine-tuning.

By Yujie Shen, Haowen Chen

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

ReqGenX: An Empirical Study of Atomic Decomposition, Artifact Regeneration, and Reconstruction for Legacy SRS Documents

arXiv:2607. 16564v1 Announce Type: cross Abstract: Background: Evaluating automated Software Requirements Specification (SRS) generation is challenging because few datasets provide fine-grained traceability between source requirements, intermediate elicitation artifacts, and generated specifications.

By Ragib Shahariar Ayon, Rayed Fahmi, Sumon Biswas, Shibbir Ahmed

llms

arXiv AI

Jul 21

Committed Before Reasoning: Behavioral Reproduction and Preliminary Activation-Level Evidence of Answer Pre-Commitment in an Open-Weight LLM

arXiv:2607. 16451v1 Announce Type: cross Abstract: Chat models sometimes commit to an answer and then produce reasoning that justifies it rather than deriving it -- even when the answer contradicts a task premise.

By Heejin Jo

llms safety

arXiv AI

Jul 21

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification

arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.

By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 21

RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts

arXiv:2607. 16716v1 Announce Type: new Abstract: Large language models and LLM-based agents are widely used as personal chat assistants, enterprise copilots, and autonomous workflow agents.

By Mihir Shriniwas Arya

llms agents benchmarks

arXiv AI

Jul 21

Interactive Task Alignment as a POMDP

arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.

By Andy Dai, Zexue He, Zhenyu Zhang, Alex Pentland, Jiaxin Pei

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 21

Content Creation with Spillovers: An Incentive Design Approach

arXiv:2603. 14372v2 Announce Type: replace Abstract: The rise of AI amplifies the economic phenomenon of \emph{positive spillovers}: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination.

By Sagi Ohayon, Boaz Taitler, Omer Ben-Porat

llms

arXiv AI

Jul 21

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

arXiv:2504. 17356v3 Announce Type: replace Abstract: Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task.

By Weiliang Zhang, Xiaohan Huang, Yi Du, Ziyue Qiao, Qingqing Long, Zhen Meng, Yuanchun Zhou, Meng Xiao

llms agents reinforcement-learning

arXiv AI

Jul 21

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

arXiv:2607. 18006v1 Announce Type: cross Abstract: Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets.

By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Zifeng Ding, Volker Tresp, Yunpu Ma

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

I wanted it to feel more personal: Customization of social AI as AI individualism in practice

arXiv:2607. 17826v1 Announce Type: cross Abstract: Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.

By Marita Skjuve, Anna Gr{\o}ndal Larsen, Asbj{\o}rn F{\o}lstad, Nena van As, Petter Bae Brandtzaeg

llms

arXiv AI

Jul 21

CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

arXiv:2607. 17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties.

By Ruogu Chen, Weihua Xiao, Ramesh Karri, Jie Han

llms

arXiv AI

Jul 21

A small language model detects behavioural faithfulness gaps that frontier judges and human raters miss

arXiv:2607. 09306v2 Announce Type: replace-cross Abstract: Whether a language model behaves as it claims is a judgement on which independent human raters cannot agree (Fleiss kappa = 0.

By Kwan Soo Shin, In Seok Kang, Yunkyung Min, Munho Lee

llms benchmarks

arXiv AI

Jul 21

Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation

arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.

By Haiyang Wang, Luca Mainardi

llms computer-vision

arXiv AI

Jul 21

Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory

arXiv:2607. 17879v1 Announce Type: new Abstract: LLM-based autonomous agents require external memory to overcome their statelessness and limited context window for long-term interaction and dynamic knowledge reasoning.

By Ganesh Senrayan, Moyuru Yamada, Ishan Jindal, Kiran Purohit

llms agents benchmarks

arXiv AI

Jul 21

Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models

arXiv:2607. 17779v1 Announce Type: new Abstract: Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to adversarial misuse, particularly in generating Not-Safe-For-Work (NSFW) images.

By Dongdong Yang, Deyue Zhang, Zhao Liu, Zonghao Ying, Wenzhuo Xu, Jiankai Jin, Xiangzheng Zhang, Quanchen Zou

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 21

PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model

arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.

By Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao

llms rag agents robotics fine-tuning multimodal safety

arXiv AI

Jul 21

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.

By Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid, Simon Blattner, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese

llms rag

arXiv AI

Jul 21

CIGPO: Contextual Information-Gain Policy Optimization for Multi-Turn Evidence-Reading LLM Agents

arXiv:2607. 16244v1 Announce Type: cross Abstract: Training multi-turn evidence-reading agents with outcome-only reinforcement learning is unstable because intermediate turns receive little direct credit.

By Hao Dou

llms agents reinforcement-learning

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