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 11

Harness In-Context Operator Learning with Chain of Operators

arXiv:2606. 12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining.

By Minghui Yang, Ling Guo, Liu Yang

llms fine-tuning

arXiv AI

Jun 11

Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation

arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.

By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu

llms benchmarks

arXiv AI

Jun 11

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

arXiv:2506. 20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer analyses to miss this cross-layer structure.

By Ankur Garg, Xuemin Yu, Hassan Sajjad, Samira Ebrahimi Kahou

llms diffusion efficiency

arXiv AI

Jun 11

Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning

arXiv:2601. 00791v2 Announce Type: replace-cross Abstract: Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and output-based heuristics are brittle.

By Valentin No\"el

llms rag benchmarks

arXiv Machine Learning

Jun 11

Re-evaluating Confidence Remasking in Masked Diffusion Language Models

arXiv:2606. 12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel token generation.

By Stipe Frkovic, Metod Jazbec, Dan Zhang, Christian A. Naesseth, Ilija Bogunovic, Eric Nalisnick

llms diffusion

arXiv Machine Learning

Jun 11

Measuring Semantic Progress in Multi-turn Dialogue via Information Gain

arXiv:2606. 12332v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses.

By Paul He, Shiva Kasiviswanathan, Dominik Janzing

llms rag safety

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments

arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.

By Lei Chu, Yuning Zhang, Omer Gokalp Serbetci, Anushka Katiyar, Bassel Abou Ali Modad, Andreas F. Molisch

llms rag nlp benchmarks

arXiv Machine Learning

Jun 11

Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification

arXiv:2606. 11661v1 Announce Type: cross Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation.

By Dong-Woo Kim, Tae-Kyun Kim

llms rag multimodal benchmarks safety

arXiv AI

Jun 11

APPO: Agentic Procedural Policy Optimization

arXiv:2606. 12384v1 Announce Type: cross Abstract: Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents.

By Xucong Wang, Ziyu Ma, Yong Wang, Yuxiang Ji, Shidong Yang, Guanhua Chen, Pengkun Wang, Xiangxiang Chu

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 11

BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts

arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.

By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 11

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

llms benchmarks

arXiv AI

Jun 11

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).

By Weishuo Ma, Yanbo Wang, Xiyuan Wang, Lei Zou, Muhan Zhang

llms

arXiv AI

Jun 11

SpikeDecoder: Realizing the GPT Architecture with Spiking Neural Networks

arXiv:2606. 12287v1 Announce Type: cross Abstract: The Transformer architecture is widely regarded as the most powerful tool for natural language processing, but due to a high number of complex operations, it inherently faces the issue of high energy consumption.

By Claas Beger, Florian Walter, Alois Knoll

llms rag computer-vision nlp

arXiv AI

Jun 11

Hubs or Fringes: Pretraining Data Selection via Web Graph Centrality

arXiv:2606. 11499v1 Announce Type: cross Abstract: The performance of modern language models depends critically on pretraining data composition.

By Vedant Badoni, Danqi Chen, Xinyi Wang

llms

arXiv AI

Jun 11

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

arXiv:2509. 11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer.

By Ching Chang, Yidan Shi, Defu Cao, Wei Yang, Jeehyun Hwang, Haixin Wang, Jiacheng Pang, Wei Wang, Yan Liu, Wen-Chih Peng, Tien-Fu Chen

llms agents benchmarks safety

arXiv AI

Jun 11

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

arXiv:2606. 11926v1 Announce Type: cross Abstract: Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction.

By Jiajie Jin, Yuyang Hu, Kai Qiu, Qi Dai, Chong Luo, Guanting Dong, Xiaoxi Li, Tong Zhao, Xiaolong Ma, Gongrui Zhang, Zhirong Wu, Bei Liu, Zhengyuan Yang, Linjie Li, Lijuan Wang, Hongjin Qian, Yutao Zhu, Zhicheng Dou

llms agents

arXiv AI

Jun 11

PermDoRA -- Understanding Adapter Interference in Language Models: Limits of Parameter-Space Geometry

arXiv:2606. 11262v1 Announce Type: cross Abstract: Access control in large language models (LLMs) requires modular mechanisms to enable domain-specific behavior without retraining or cross-domain interference.

By Gowtham Sivaramakrishnan, Sarvesha Kumar Kombaiah Seetha, Kishan Gupta Balaji, Santhosh Baradwaj Vaduvur Ranganathan

llms fine-tuning benchmarks safety

arXiv AI

Jun 11

Dual-Stance Evaluation of Sycophancy: The Structure of Agreement and the Limits of Intervention

arXiv:2606. 11205v1 Announce Type: cross Abstract: Activation steering can shift LLM behaviour, but standard evaluations do not typically test whether a sycophancy-reduction direction also suppresses agreement with factually correct statements.

By Matthew James Buchan

llms

arXiv AI

Jun 11

SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning

arXiv:2606. 11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.

By Chao Lei, Yanbei Jiang, Markus Hiller, Zhijian Zhou, Xunye Tian, Krista A. Ehinger, Nir Lipovetzky

llms reinforcement-learning multimodal benchmarks

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