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 25

Flexible Gravitational-Wave Parameter Estimation with Transformers

arXiv:2512. 02968v2 Announce Type: replace-cross Abstract: Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge.

By Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Sch\"olkopf

llms

arXiv Machine Learning

Jun 25

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model

arXiv:2507. 09052v3 Announce Type: replace-cross Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes.

By Fang Chen, Alex Villa, Gongbo Liang, Fuxing Li, Xiaoyi Lu, Meng Tang

llms diffusion safety

arXiv Machine Learning

Jun 25

The 4/$\delta$ Bound: Designing Predictable LLM-Verifier Systems for Formal Method Guarantee

arXiv:2512. 02080v3 Announce Type: replace-cross Abstract: The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows.

By Pierre Dantas, Lucas Cordeiro, Youcheng Sun, Waldir Junior

llms safety

arXiv Machine Learning

Jun 25

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511. 11421v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.

By Lan Li, Tao Hu, Da-Wei Zhou, Jia-Qi Yang, Han-Jia Ye, De-Chuan Zhan

llms multimodal benchmarks

arXiv Machine Learning

Jun 25

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

arXiv:2501. 02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias is stable or an artifact of inference settings has only been studied in single proprietary models.

By Messi H. J. Lee

llms safety

arXiv Machine Learning

Jun 25

InvestPhilBench: A Multi-Layer Dynamic Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

arXiv:2606. 25984v1 Announce Type: cross Abstract: Large language models are increasingly deployed as investment research assistants, yet no benchmark tests whether they can accurately reconstruct and apply the specific procedural decision frameworks of expert investors.

By Mingguang Chen, Bo Qu

llms benchmarks

arXiv Machine Learning

Jun 25

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.

By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 25

Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure

arXiv:2403. 11425v4 Announce Type: replace Abstract: Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship.

By Ziyi Chen, Mengyuan Zhang, Mustafa Mohammed Ahmed, Yi Guo, Thomas J. George, Jiang Bian, Yonghui Wu

llms safety

arXiv Machine Learning

Jun 25

The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order

arXiv:2606. 24993v1 Announce Type: new Abstract: Sequential learning is order-dependent: from Pile-style next-token domain adaptation to instruction-SFT and DPO, N candidate sources induce N!

By John Sweeney

llms diffusion reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

arXiv:2505. 12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences.

By Kangwen Zhao, Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Dongyun Xue, Wengang Zhou, Li Li, Houqiang Li

llms reinforcement-learning safety

arXiv Machine Learning

Jun 25

Multi-Stream Temporal Fusion for Financial Fraud Detection

arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.

By Mohammadamin Dashti Moghaddam, Nick Sciarrilli

llms benchmarks safety

arXiv Machine Learning

Jun 25

Do Thinking Tokens Help with Safety?

arXiv:2606. 25013v1 Announce Type: new Abstract: Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts.

By Narutatsu Ri, Abhishek Panigrahi, Sanjeev Arora

llms benchmarks safety

arXiv Machine Learning

Jun 25

Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory

arXiv:2606. 25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights.

By Beining Wu, Zihao Ding, Jun Huang, Yanxiao Zhao

llms agents robotics benchmarks

arXiv Machine Learning

Jun 25

An iterative energy-based multimodal transformer for joint retrieval of wheat soil moisture, leaf area index, and plant height from Sentinel-1 and Sentinel-2 time series

arXiv:2606. 25174v1 Announce Type: new Abstract: Field-scale retrieval of surface soil moisture (SM), leaf area index (LAI), and plant height (PH) is essential for precision agriculture, yet it remains an ill-posed inverse problem.

By Shubham Kumar Singh, Peilei Fan, Suraj A. Yadav, Rajendra Prasad, Prashant K Srivastava

llms multimodal

arXiv Machine Learning

Jun 25

Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It

arXiv:2606. 26027v1 Announce Type: cross Abstract: Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities.

By Yupu Hao, Zhuoran Jin, Huanxuan Liao, Kang Liu, Jun Zhao

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 25

LLM Performance on a Real, Double-Marked GCSE Benchmark

arXiv:2606. 24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work.

By Malachy Fox, Kavi Samra, Paul Jung

llms benchmarks

arXiv Machine Learning

Jun 25

Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics

arXiv:2605. 05097v3 Announce Type: replace Abstract: LLMs are trained once, then deployed into a world that never stops changing.

By Andreas Pattichis, Constantine Dovrolis

llms

arXiv Machine Learning

Jun 25

Weave of Formal Thought

arXiv:2606. 25987v1 Announce Type: cross Abstract: Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language.

By Alexandre Bouayad

llms nlp fine-tuning

arXiv Machine Learning

Jun 25

Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory

arXiv:2606. 23195v2 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence.

By Zewen Liu

llms agents safety

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