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 Machine Learning

Jun 10

Influence Dynamics and Stagewise Data Attribution

arXiv:2510. 12071v2 Announce Type: replace Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stages that exhibit changing patterns of influence.

By Jin Hwa Lee, Matthew Smith, Maxwell Adam, Jesse Hoogland

llms

arXiv Machine Learning

Jun 10

Time series forecasting from partial observations via Non-negative Matrix Factorization

arXiv:2102. 05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values.

By Yohann de Castro (ICJ, PSPM, CERMICS UMR 9032, ECL, IUF), Luca Mencarelli (CERMICS UMR 9032)

llms benchmarks

arXiv AI

Jun 10

PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks

arXiv:2606. 09872v1 Announce Type: cross Abstract: Traffic forecasting is a fundamental component of intelligent transportation systems, yet remains challenging in real-world settings due to irregular sensor distributions and the high computational cost of modeling large-scale spatiotemporal dependencies.

By Jichao Li, Xuanming Shi

llms benchmarks

arXiv AI

Jun 10

Why Does Reasoning Length Converge? Unveiling the Underfitting-Overfitting Trade-off in Chain-of-Thought

arXiv:2509. 04027v4 Announce Type: replace Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a pivotal paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs).

By Zeyu Gan, Hao Yi, Yong Liu

llms reinforcement-learning

arXiv Machine Learning

Jun 10

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv:2606. 11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation.

By Yidan Shen, Neville Mathew, Maham Rahimi, Deependra Dhakal, George Zouridakis, Xin Fu, Renjie Hu

llms

arXiv AI

Jun 10

ASyMOB: Algebraic Symbolic Mathematical Operations Benchmark

arXiv:2505. 23851v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization with genuine reasoning.

By Michael Shalyt, Rotem Elimelech, Ido Kaminer

llms benchmarks

arXiv AI

Jun 10

Frontier Coding Agents Use Metaprogramming to Adapt to Unfamiliar Programming Languages

arXiv:2606. 10933v1 Announce Type: new Abstract: LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories.

By Aman Sharma, Sushrut Thorat, Paras Chopra

llms agents benchmarks

arXiv AI

Jun 10

Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models

arXiv:2606. 10046v1 Announce Type: cross Abstract: Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque.

By Yuxuan Chen, Haoyuan Xu, Peize He

llms computer-vision

arXiv AI

Jun 10

Constructing coherent spatial memory in LLM agents through graph rectification

arXiv:2510. 04195v2 Announce Type: replace Abstract: Given a map description through global traversal navigation instructions, an LLM can often infer the implicit spatial layout and answer user queries by providing shortest paths.

By Puzhen Zhang, Xuyang Chen, Yu Feng, Yuhan Jiang, Liqiu Meng

llms agents benchmarks

arXiv AI

Jun 10

ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics

arXiv:2606. 10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight.

By Shunkai Zhang, Haoran Zhang, Yun Luo, Qianjia Cheng, Haodi Lei, Yizhuo Li, Runzhe Zhan, Zhilin Wang, Bangjie Xu, Yucheng Su, Xinmiao Han, Xiaoye Qu, Dongrui Liu, Zhouchen Lin, Yu Qiao, Ning Ding, Yafu Li, Yu Cheng

llms benchmarks

arXiv AI

Jun 10

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504. 03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles.

By Ziteng Wei, Qiang He, Bing Li, Feifei Chen, Hai Jin, Yun Yang

llms efficiency benchmarks

arXiv Machine Learning

Jun 10

A Navigable Manifold of Hypothesized Consciousness-Spectrum States in Language Model Representations

arXiv:2606. 09894v1 Announce Type: new Abstract: Across contemplative, philosophical, and psychological accounts, human consciousness is often described along a similar spectrum, ranging from reactive and self-focused patterns to more integrative and coherent ones.

By Sophie Zhao

llms rag safety

arXiv AI

Jun 10

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.

By Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 10

When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models

arXiv:2512. 06343v3 Announce Type: replace-cross Abstract: Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF.

By Tong Xie, Andrew Bai, Yuanhao Ban, Yunqi Hong, Haoyu Li, Cho-Jui Hsieh

llms reinforcement-learning safety

arXiv AI

Jun 10

MMClima: A Framework for Multimodal Climate Science Data and Evaluation

arXiv:2606. 10194v1 Announce Type: cross Abstract: Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, mostly textual, and cover a narrow range of models.

By Muhammad Umer Sheikh, Hassan Abid, Khawar Shehzad, Ufaq Khan, Muhammad Haris Khan

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 10

From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs

arXiv:2606. 10298v1 Announce Type: new Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck.

By Runze Jiang, Taiqiang Wu, Yan Wang, Bingyu Zhu, Longtao Huang

llms

arXiv AI

Jun 10

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.

By Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah

llms agents benchmarks safety

arXiv AI

Jun 10

Decoupling Thought from Speech: Knowledge-Grounded Counterfactual Reasoning for Resilient Multi-Agent Argumentation

arXiv:2606. 10475v1 Announce Type: cross Abstract: Multi-agent debate frameworks have been shown to improve large language model performance in convergent tasks, but they are currently optimized in a way that heavily favors final output accuracy rather than stability of the process.

By Jakub Mas{\l}owski, Jaros{\l}aw A. Chudziak

llms rag agents benchmarks safety

arXiv AI

Jun 10

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience

arXiv:2606. 10359v1 Announce Type: new Abstract: AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforcement learning (RL) optimizes flows but is semantically blind to unstructured constraints.

By Jia Luo

llms agents diffusion reinforcement-learning benchmarks safety

arXiv AI

Jun 10

AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies

arXiv:2606. 10752v1 Announce Type: new Abstract: Numerical solvers for partial differential equations (PDEs) are core computational tools in science and engineering.

By Huanshuo Dong, Keyao Zhang, Hong Wang, Zhezheng Hao, Zhiwei Zhuang, Ziyan Liu, Jiacong Wang, Gengyuan Liu, Xin Jin

llms agents

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