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 31

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge

arXiv:2603. 07131v4 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis.

By Shuai Lu, Meng Wang, Jia Guo, Jiawei Du, Bo Liu, Shengzhu Yang, Weihang Zhang, Huazhu Fu, Huiqi Li

llms rag nlp benchmarks safety

arXiv AI

Jul 31

MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue

arXiv:2603. 06194v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn interaction remains challenging due to sparse rewards and poor per-turn credit assignment.

By Naifan Zhang, Ruihan Sun, Jinwei Su, Hengjie Yang, Zhengyuan Pan, Zhaohan Chen, Xiaofan Zhang

llms reinforcement-learning

arXiv AI

Jul 31

VistaHop: Benchmarking Long-Horizon Visual DeepSearch

arXiv:2606. 03273v2 Announce Type: replace-cross Abstract: Visual DeepSearch tasks require multimodal large language models (MLLMs) to resolve complex visual queries by repeatedly inspecting image regions, grounding reasoning in visual evidence, and connecting fine-grained clues across multiple steps.

By Hang He, Chuhuai Yue, Chengqi Dong, Chengcheng Wan, Ting Su, Haiying Sun, Jiajun Chai, Xiaohan Wang, Guojun Yin

llms agents multimodal benchmarks

arXiv AI

Jul 31

Adversarial Pragmatics for AI Safety Evaluation: A Diagnostic Framework and Seed Benchmark for Language-Mediated Control

arXiv:2607. 01153v3 Announce Type: replace-cross Abstract: Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model followed an instruction, refused appropriately, complied with a policy, or misreported progress in an agentic task.

By Brett Reynolds

llms agents benchmarks safety

arXiv Machine Learning

Jul 31

Beyond KV Reconstruction: Functional Reconstruction for MLA Draft Models in Speculative Decoding

arXiv:2607. 27269v1 Announce Type: new Abstract: Multi-head latent attention (MLA) is increasingly important for long-context LLM inference because compact latent states replace the growing key-value (KV) cache and reduce decoding memory traffic.

By Weiye Shi, Fanxu Meng, Muhan Zhang

llms

arXiv Machine Learning

Jul 31

SDO: Structure-Aware Data Organization for Efficient LLM Post-Training

arXiv:2607. 27273v1 Announce Type: new Abstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing training schedules.

By Jinliang Gao, Ning Yang, Hai Wang, Baili Xiao, Pin Lyu

llms rag reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 31

The Kinetics of Training: A Driven-Nucleation Rate Law for Emergence, Plasticity Loss, and Circuit Control in Language Models

arXiv:2607. 27281v1 Announce Type: new Abstract: A capability appears in a language model when the last parts of its circuit align in one stochastic attempt, and getting all but one right is worth nothing.

By Lei Dong

llms safety

arXiv Machine Learning

Jul 31

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.

By Lei Zan, Keli Zhang, Shifeng Xie, Jiale Zheng, Zehao Xiao, Zhiwei Dong, Ke Zhang, Ruichu Cai, Malik Tiomoko, Lujia Pan

llms benchmarks safety

arXiv Machine Learning

Jul 31

Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models

arXiv:2607. 27350v1 Announce Type: new Abstract: Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance.

By Micha{\l} Bartnicki, Jaros{\l}aw A. Chudziak

llms

arXiv Machine Learning

Jul 31

Context-Informed Ship Trajectory Prediction via Conditional Attention

arXiv:2607. 27418v1 Announce Type: new Abstract: Long-term ship trajectory prediction is a fundamental capability for maritime safety and autonomous navigation.

By Yuan Guan, Chandler Squires, Timothy Hu, Pradeep Ravikumar

llms agents multimodal safety

arXiv Machine Learning

Jul 31

Latent-Kernel Discrete Flow Maps for Few-Step Generation

arXiv:2607. 27529v1 Announce Type: new Abstract: Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token independently.

By Mansoor Ahmed, Yue-Tsz Fan, Hemanth Venkateswara, Murray Patterson

llms diffusion benchmarks

arXiv Machine Learning

Jul 31

Policy Gradient Steering: Interventions from Behavioral Objectives

arXiv:2607. 27574v1 Announce Type: new Abstract: Activation steering has emerged in large language models as a lightweight alternative for dynamically changing a model's behavior at inference time.

By Yoann Poupart, Aur\'elie Beynier, Nicolas Maudet

llms reinforcement-learning

arXiv Machine Learning

Jul 31

Back from the Future: Key-Value Cache Management by Counter-Causal Surprise

arXiv:2607. 27600v1 Announce Type: new Abstract: Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.

By Stephen Gould, Anton van den Hengel

llms efficiency multimodal benchmarks

arXiv Machine Learning

Jul 31

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning

arXiv:2607. 27610v1 Announce Type: new Abstract: Reinforcement learning (RL) finetuning significantly enhances the reasoning capabilities of large language models (LLMs), yet its effectiveness critically depends on selecting prompts of appropriate difficulty for the current policy.

By Haodong Zhu, Yangyang Ren, Yanjing Li, Sheng Xu, Haiguang Liu, Linlin Yang, Baochang Zhang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 31

VESTIGE: A Knowledge-Guided Masking Strategy for Corruption-Aware Fine-Tuning of Genomic Transformers, Validated on Ancient DNA Reconstruction

arXiv:2607. 27712v1 Announce Type: new Abstract: Standard masked-language-model fine-tuning applies a uniform masking probability across every token position, assuming reconstruction difficulty is position-agnostic.

By Angshuman Chakravertty, Rahul Maheshwari

llms fine-tuning

arXiv Machine Learning

Jul 31

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

arXiv:2607. 27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 31

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement

arXiv:2607. 27940v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) enables collaborative training without exposing raw data.

By Cheng Wei (Honor Device Co., Ltd., Shenzhen, China)

llms fine-tuning safety

arXiv Machine Learning

Jul 31

Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning

arXiv:2607. 27968v1 Announce Type: new Abstract: Machine unlearning seeks to selectively remove specific knowledge from trained language models without full retraining, a growing necessity under privacy regulations such as GDPR and the EU AI Act.

By Efstratios Zaradoukas, Davide Gabrielli, Bardh Prenkaj, Gjergji Kasneci

llms reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Jul 31

TAPO: Transition-Aware Policy Optimization for LLM Agents

arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.

By Cong Li, Peixi Peng, Yisen Zhao, Xinyu Hu, Shudong Liu, Zhan Su, Zhuojian Li

llms agents reinforcement-learning

arXiv Machine Learning

Jul 31

Building a User Foundation Model for the Open Web

arXiv:2607. 28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments where user identity is stable and persistent.

By Solal Vernier, Ivan Can Arisoy, Merwan Barlier, Bla\v{z} \v{S}krlj

llms fine-tuning safety

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