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

SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

arXiv:2602. 23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world.

By Simon Roschmann, Paul Krzakala, Sonia Mazelet, Quentin Bouniot, Zeynep Akata

llms rag multimodal safety

arXiv AI

Jun 30

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

arXiv:2606. 29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image.

By Yichen Guo, Kai Tang, Fenglai Lin, Yiding Sun, Dongshuo Zhang, Wenya Wang, Lin William Cong, Shanghang Zhang

llms multimodal benchmarks safety

arXiv AI

Jun 30

AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes

arXiv:2606. 29871v1 Announce Type: new Abstract: We present the AI Training Manager, a bounded LLM-based supervisory controller for adaptive machine learning training.

By Anjali Rao, Nikhil Kamalkumar Advani

llms agents reinforcement-learning robotics

arXiv AI

Jun 30

LAMP: Lean-based Agentic framework with MCP and Proof Repair

arXiv:2606. 28841v1 Announce Type: cross Abstract: Large language models are increasingly capable of mathematical reasoning, but the proofs they generate are often unreliable and hard to verify.

By Santhana Srinivasan R, Maithilee Patawar

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF

arXiv:2606. 29758v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) for Large Language Models increasingly relies on critic-free methods as a practical alternative to actor--critic training.

By Doo Hwan Hwang, Kee-Eung Kim

llms reinforcement-learning benchmarks

arXiv AI

Jun 30

An AI agent for treatment reasoning over a biomedical tool universe

arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.

By Shanghua Gao, Ayush Noori, Richard Zhu, Curtis Ginder, Zhenglun Kong, Xiaorui Su, Justin Kauffman, Benjamin S. Glicksberg, Joshua Lampert, Ankit Sakhuja, Ashwin Sawant, ATHENA-R1 Evaluation Consortium, David A. Clifton, Noa Dagan, Ran Balicer, Marinka Zitnik

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

arXiv:2511. 06090v3 Announce Type: replace-cross Abstract: Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness.

By Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh, Kevin Swersky, Ofir Press, Enhui Li, Vijay Janapa Reddi, Parthasarathy Ranganathan

llms agents benchmarks data-engineering

arXiv AI

Jun 30

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

arXiv:2606. 28998v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards.

By Gias Uddin, Sanjeepan Sivapiran

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 30

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?

By Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han, Taehee Jung, Dongyeop Kang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation

arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).

By Yinjie Cheng, Paul Youssef, Christin Seifert, J\"org Schl\"otterer, Zhixue Zhao

llms fine-tuning benchmarks safety

arXiv AI

Jun 30

EVAF: A Test-Retest Protocol for Selective Parametric Consolidation

arXiv:2606. 29916v1 Announce Type: cross Abstract: Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone.

By Haoliang Han

llms agents fine-tuning

arXiv AI

Jun 30

PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agents

arXiv:2606. 29225v1 Announce Type: new Abstract: LLM agents handle user requests on behalf of organizations through tool calls and must follow the company policies stated in their system prompts.

By Seongjae Kang, Taehyung Yu, Sung Ju Hwang

llms agents

arXiv AI

Jun 30

5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control

arXiv:2606. 28737v1 Announce Type: cross Abstract: We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn Retrieval Augmented Generation (RAG) systems.

By Thien-Qua-T-Nguyen, Chi Hoang, Nguyen Tran, Tri Le, Khanh Truong, Chinh Trong Nguyen

llms rag safety

arXiv AI

Jun 30

EfficientUICoder: A Bidirectional Token Compression Framework for Efficient MLLM-Based UI Code Generation

arXiv:2509. 12159v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency.

By Jingyu Xiao, Zhongyi Zhang, Yuxuan Wan, Yintong Huo, Yang Liu, Michael R. Lyu

llms multimodal

arXiv Machine Learning

Jun 30

Generalization Analysis of Transformers in Distribution Regression

arXiv:2606. 29256v1 Announce Type: cross Abstract: In recent years, models based on the Transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning.

By Peilin Liu, Ding-Xuan Zhou

llms nlp fine-tuning

arXiv Machine Learning

Jun 30

MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training

arXiv:2606. 30406v1 Announce Type: cross Abstract: Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabilities into one model remains hard.

By Wenhan Ma, Jianyu Wei, Liang Zhao, Hailin Zhang, Bangjun Xiao, Lei Li, Qibin Yang, Bofei Gao, Yudong Wang, Rang Li, Jinhao Dong, Zhifang Sui, Fuli Luo

llms reinforcement-learning efficiency safety

arXiv AI

Jun 30

Multi-Class Human/Object Detection on Robot Manipulators using Proprioceptive Sensing

arXiv:2508. 02425v2 Announce Type: replace-cross Abstract: In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments.

By Justin Hehli, Marco Heiniger, Maryam Rezayati, Hans Wernher van de Venn

llms computer-vision robotics safety

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

C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders

arXiv:2606. 30609v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionaries exposes fundamental challenges.

By Haoran Jin, Xiting Wang, Shijie Ren, Hong Xie, Defu Lian

llms safety

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

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