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

Jun 30

Expert Evaluation of Clinical AI Tools on Real Point-of-Care Clinical Queries

arXiv:2606. 28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions, not those actually asked in practice.

By Jean Feng, Vishal Patel, Patrick Heagerty, Yifan Mai, Venkatesh Sivaraman, Patrick Vossler, Jialin Ouyang, Anupam B. Jena

llms benchmarks

arXiv AI

Jun 30

Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning

arXiv:2602. 13562v2 Announce Type: replace-cross Abstract: While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures.

By Yanbo Wang, Minzheng Wang, Jian Liang, Lu Wang, Yongcan Yu, Ran He

llms reinforcement-learning efficiency safety

arXiv AI

Jun 30

Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.

By Xiaojun Wu, Xiaoguang Jiang, Huiyang Li, Jucai Zhai, Dengfeng Liu, Qiaobo Hao, Huang Liu, Zhiguo Yang, Ji Xie, Ninglun Gu, Jin Yang, Kailai Zhang, Yelun Bao, Jun Wang

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 30

Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.

By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek

llms nlp fine-tuning

arXiv AI

Jun 30

LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

arXiv:2606. 28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality.

By Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

arXiv:2606. 29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging.

By Chengfeng Zhao, Yuqiao Tan, Shizhu He, Yequan Wang, Jun Zhao, Kang Liu

llms rag agents benchmarks

arXiv AI

Jun 30

How to Train Your Long-Context Visual Document Model

arXiv:2602. 15257v3 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text.

By Austin Veselka

llms nlp fine-tuning benchmarks

arXiv AI

Jun 30

Data Provenance for Image Auto-Regressive Generation

arXiv:2606. 28386v1 Announce Type: cross Abstract: Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models.

By Bihe Zhao, Louis Kerner, Michel Meintz, Tameem Bakr, Franziska Boenisch, Adam Dziedzic

llms diffusion

arXiv AI

Jun 30

HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression

arXiv:2606. 28831v1 Announce Type: cross Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.

By Yuxuan Yang, Feiyang Ren, Bowen Zeng, Dalin Zhang, Jinpeng Chen, Gang Chen, Huan Li

llms efficiency benchmarks

arXiv AI

Jun 30

On the Nonlinearity of Learning Rate Scaling for LLM Training

arXiv:2606. 29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs.

By Zaiwen Yang, Huaqing Zhang, Jing Xu, Jingzhao Zhang

llms

arXiv AI

Jun 30

SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing

arXiv:2606. 29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies, rather than relying on predefined risk taxonomies.

By Jiacheng Zhang, Haoyu He, Sen Zhang, Shen Wang, Xiaolei Xu, Yuhao Sun, Meng Shen, Feng Liu

llms benchmarks safety

arXiv AI

Jun 30

DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training

arXiv:2606. 30345v1 Announce Type: cross Abstract: Enabling large language models to achieve stable self-improvement without external expert supervision remains a central challenge in complex reasoning tasks.

By Haisen Luo, Yiwei Liu, Haoning Wang, Dan Liu, Junxi Yin, Haotian Wang, Lei Zhang, Xiaoyu Tian, Shuaiting Chen, Yuansheng Song, Baoyan Guo, Xiongfei Yan, Bolan Yang, Chengwei Liu, Ming Cui, Jiong Chen

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jun 30

Be Faithful When Response: Returning Fluent and Grounded Answers for Vision-Language Models Reinforcement Learning

arXiv:2606. 29984v1 Announce Type: new Abstract: Reinforcement Learning (RL) is an important paradigm for improving the reasoning capabilities of Vision-Language Models (VLMs).

By Peng, Lee, Yin Zhang, Yanglin Zhang, Haonan Wu, Zishan Liu, Ruoxi Zang, Xin Zhu, Jiayin Zheng, Jian Yao, Zefeng Ji, Fei Ma

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jun 30

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback

arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.

By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 30

Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent

arXiv:2606. 29776v1 Announce Type: cross Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise.

By Zheng Fang, Chen Yang, Yusen Tan, Yunpeng Zhao, Fanjie Xu, Hongxin Xiang, Hanyu Sun, Hanyu Gao, Xiaojian Wang, Wenjie Du, Yuqiang Li, Jun Xia

llms agents benchmarks safety

arXiv AI

Jun 30

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models

arXiv:2606. 29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case.

By Rafael Kaufmann, Felix Neub\"urger, Michael Walters, Thomas Kopinski, Dimitrije Markovi\'c

llms agents benchmarks

arXiv Machine Learning

Jun 30

SamatNext v0.2-B: An Exploratory Study of RMS-Normalized Hybrid Decoders for Curriculum Retention in Small Code Models

arXiv:2606. 22248v2 Announce Type: replace Abstract: Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine-tuning on shifting curriculum distributions.

By Samat Zharassov

llms fine-tuning

arXiv AI

Jun 30

Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformers

arXiv:2510. 25013v2 Announce Type: replace-cross Abstract: Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits.

By Rabin Adhikari

llms rag benchmarks safety

arXiv AI

Jun 30

RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines

arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.

By Xindian Ma, Xinyu Long, Yefei Zhang, Yanchen Liu, Xianghao Li, Yufu Wen, Yike Hu, Yuedong Zhu, Zeyang Ma, Wen Qin, Yikun Wang, Peng Yang, Monan Wang, Teng Yu

llms multimodal

arXiv AI

Jun 30

Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts

arXiv:2606. 30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish.

By Huanping Xiao, Yingji Li

llms rag safety

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