Large language models

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

28,678 stories · RSS feed

arXiv AI

Jun 15

The Coin Flip Judge? Reliability and Bias in LLM-as-a-Judge Evaluation

arXiv:2606. 13685v1 Announce Type: cross Abstract: LLM-as-a-Judge is now widely used to rank model outputs, train reward models, and populate public leaderboards, but its run-to-run reliability remains under-characterized.

By Abel Yagubyan

llms safety

arXiv AI

Jun 15

Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression

arXiv:2505. 23277v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts.

By Yong Zhang, Heng Li, Yanwen Huang, Ning Cheng, Yang Guo, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao

llms rag

arXiv Machine Learning

Jun 15

Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models

arXiv:2606. 14222v1 Announce Type: new Abstract: The rapid evolution of Time Series Foundation Models (TSFMs) has advanced zero-shot forecasting across diverse domains.

By Xilin Dai, Yiding Liu, Hongjie Xia, Yifan Hu, Zewei Dong, Jiang-Ming Yang, Qiang Xu

llms fine-tuning benchmarks

arXiv AI

Jun 15

Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities

arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.

By Rafayel Mkrtchyan, Armen Manukyan, Hrant Khachatrian, Theofanis P. Raptis

llms computer-vision

arXiv AI

Jun 15

Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost

arXiv:2602. 03120v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune.

By Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen, Xin Qiu

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 15

Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions

arXiv:2605. 07984v2 Announce Type: replace-cross Abstract: We study planning site formation in language models -- where internal representations of structurally-constrained future tokens form during the forward pass, and whether they causally drive generation.

By Nicole Ma, Nick Rui

llms

arXiv Machine Learning

Jun 15

Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study

arXiv:2606. 12881v2 Announce Type: replace-cross Abstract: We present an approach to fine-tuning large language models using Direct Preference Optimization (DPO), a reinforcement learning technique.

By Dezhi Yu, Yvonne Qiu, ShuoJia Fu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 15

NeST: Neuron Selective Tuning for LLM Safety

arXiv:2602. 16835v2 Announce Type: replace-cross Abstract: Safety alignment is essential for the responsible deployment of Large Language Models (LLMs).

By Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami, Ahmad-Reza Sadeghi

llms fine-tuning multimodal safety

arXiv AI

Jun 15

SAFformer:Improving Spiking Transformer via Active Predictive Filtering

arXiv:2605. 08270v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers.

By Zequan Xie, Weiming Zeng, Yunhua Chen, Sichang Ling, Tongyang Chen, Jinsheng Xiao

llms benchmarks

arXiv Machine Learning

Jun 15

Persona-Pruner: Sculpting Lightweight Models for Role-Playing

arXiv:2606. 14695v1 Announce Type: new Abstract: Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona.

By Jinsu Kim, Jihoon Tack, Noah Lee, Jongheon Jeong

llms efficiency benchmarks

arXiv Machine Learning

Jun 15

When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing

arXiv:2606. 14668v1 Announce Type: new Abstract: Knowledge editing systems must update selected facts while preserving nearby but irrelevant behavior.

By Yining Huang

llms rag fine-tuning benchmarks

arXiv AI

Jun 15

Fractured Chain-of-Thought Reasoning

arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.

By Baohao Liao, Hanze Dong, Yuhui Xu, Doyen Sahoo, Christof Monz, Junnan Li, Caiming Xiong

llms benchmarks

arXiv AI

Jun 15

When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks

arXiv:2606. 14629v1 Announce Type: cross Abstract: Verifier-driven self-DPO is a common recipe for self-improving production visual-language models.

By Jianzhe Lin

llms reinforcement-learning

arXiv AI

Jun 15

SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing

arXiv:2606. 14239v1 Announce Type: new Abstract: Agent skills are structured procedural packages that guide frozen LLM agents in specialized workflows.

By Haowen Gao, Haoran Chen, Can Wang, Shasha Guo, Liang Pang, Zhaoyang Liu, Huawei Shen, Xueqi Cheng

llms agents

arXiv AI

Jun 15

A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models

arXiv:2606. 14117v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated for bias using adaptations of human psychological paradigms, yet methodological limitations-particularly the conflation of refusal behavior with task performance-have hindered clear interpretation.

By Achraf Cohen, Andrew Kincaid

llms safety

arXiv AI

Jun 15

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602. 23638v3 Announce Type: replace-cross Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data.

By Haoran Zhang, Dongjun Kim, Seohyeon Cha, Haris Vikalo

llms fine-tuning safety

arXiv AI

Jun 15

When Should Agent Trust Be Conditional? Characterizing and Attacking Skill-Conditional Reputation in Agent Swarms

arXiv:2606. 14200v1 Announce Type: new Abstract: Open platforms increasingly route tasks among heterogeneous LLM agents--differing in base model, scaffold, and tool stack--whose competence varies sharply by skill: an agent excellent at one skill may be useless at another.

By Yihan Xia, Taotao Wang

llms agents benchmarks

arXiv AI

Jun 15

Order Is Not Control: Driven-Dissipative Response Laws Across Artificial and Biological Systems

arXiv:2606. 12923v2 Announce Type: replace-cross Abstract: AI alignment, interpretability, steering, and neural perturbation studies identify order-inducing objects.

By Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi, Jeffrey Molendijk, Tim Elson

llms safety

arXiv AI

Jun 15

WorkBench Revisited: Workplace Agents Two Years On

arXiv:2606. 13715v1 Announce Type: new Abstract: The best agent on WorkBench in March 2024, GPT-4, completed 43% of tasks and took an unintended harmful action, such as emailing the wrong person, on 26% of them.

By Olly Styles

llms agents benchmarks safety

arXiv Machine Learning

Jun 15

Gaze Heads: How VLMs Look at What They Describe

arXiv:2606. 14703v1 Announce Type: cross Abstract: How a vision-language model internally solves the task of describing an image is far from obvious.

By Rohit Gandikota, David Bau

llms multimodal

Get the daily briefing