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

1d ago

FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining

arXiv:2610.06947v1 Announce Type: cross Abstract: Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mini...

By Zhuohan Wang, Carmine Ventre

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

1d ago

Evaluating Inference Compute for Generative AI: A Framework for Enterprise Workloads

arXiv:2610.07094v1 Announce Type: cross Abstract: LLM deployment is shifting from single-turn completion to agentic trajectories in which a model plans, calls tools, reads results and reasons at test...

By Abbas Raza Ali, Muhammad Ajmal Siddiqui, Moona Zahid

llms agents efficiency benchmarks

arXiv Machine Learning

1d ago

CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?

arXiv:2610.07576v1 Announce Type: cross Abstract: Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned fr...

By Kevin Lee

llms computer-vision

arXiv Machine Learning

1d ago

Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects

arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and aver...

By Tianxi Li, Jie Ding

llms benchmarks

arXiv Machine Learning

1d ago

Beyond the Leaderboard: Multi-Dimensional Evaluation of Dense and Mixture-of-Experts Models for Automated Program Repair

arXiv:2610.08173v1 Announce Type: cross Abstract: Automated Program Repair (APR) with language models is usually evaluated by whether a generated patch passes the test suite, which can hide differenc...

By Anvi Kalpesh Shah, Umamaheswara Sharma B

llms benchmarks

arXiv Machine Learning

1d ago

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models

arXiv:2610.08341v1 Announce Type: cross Abstract: Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redu...

By Shuo Yang, Changbai Li, Linlin Yang, Huobin Tan, Rongyu Chen, Tongfei Chen, Tian Wang, Sheng Xu, Baochang Zhang

llms efficiency multimodal benchmarks

arXiv Machine Learning

1d ago

Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents

arXiv:2610.08452v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring...

By Lasse B. Strand, Robert Jakob, Kevin O'Sullivan, Markus Kreft

llms rag agents benchmarks

arXiv Machine Learning

1d ago

When Forgetting is not Catastrophic: On the Mechanics of Spurious Forgetting

arXiv:2610.08718v1 Announce Type: cross Abstract: Knowledge that a language model appears to forget during finetuning often remains stored and can be recovered, a phenomenon called spurious forgettin...

By Vedant Palit, Florent Draye, Nicolas Zucchet, Zhijing Jin, Bernhard Sch\"olkopf

llms fine-tuning

arXiv Machine Learning

1d ago

Direct Regret Optimization in Bayesian Optimization

arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate h...

By Fengxue Zhang, Yuxin Chen

llms benchmarks

arXiv Machine Learning

1d ago

Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation

arXiv:2511.11500v3 Announce Type: replace Abstract: Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accurac...

By Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan Li

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

1d ago

Particle Monte Carlo Tree Search

arXiv:2605.08982v4 Announce Type: replace Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequenti...

By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier

llms reinforcement-learning benchmarks

arXiv Machine Learning

1d ago

SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval

arXiv:2605.23753v2 Announce Type: replace Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph e...

By Hamed Shirzad, Frederik Wenkel, Dominique Beaini, Danica J. Sutherland, Emmanuel Noutahi

llms rag agents reinforcement-learning fine-tuning

arXiv Machine Learning

1d ago

EvoHarness-RL: Learning Runtime Harness Coordination for Self-Evolving Agents

arXiv:2608.05446v2 Announce Type: replace Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experie...

By Xuying Ning, Dongqi Fu, Tianxin Wei, Yuanchen Bei, Xiyuan Yang, Wujiang Xu, Yueqi Song, Bingxuan Li, Zihao Li, Hanqing Zeng, Xiang Shen, Yajuan Wang, Yifan Wu, Qifan Wang, Jiayi Liu, Hong Li, Yinglong Xia, Xiangjun Fan, Hanghang Tong, Jingrui He

llms agents reinforcement-learning

arXiv Machine Learning

1d ago

LionMuon: Alternating Spectral and Sign Descent for Efficient Training

arXiv:2609.35297v3 Announce Type: replace Abstract: Pretraining a language model takes enormous compute, and the right optimizer can save a good part of it. Muon's spectral step gives a stronger dire...

By Arman Bolatov, Artem Riabinin, Nikita Kornilov, Andrey Veprikov, Samuel Horv\'ath, Martin Tak\'a\v{c}, Aleksandr Beznosikov

llms

arXiv Machine Learning

1d ago

Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer

arXiv:2610.04946v2 Announce Type: replace Abstract: Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, a...

By Qingyang Zhang

llms rag benchmarks

arXiv Machine Learning

1d ago

Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models m...

By Yuchen Lei, Yuexin Xiang, Rafael Dowsley, Tsz Hon Yuen, Andreas Deppeler, Jiangshan Yu, Qin Wang, Kim-Kwang Raymond Choo

llms

arXiv Machine Learning

1d ago

AEGIS: Runtime-Guided GPU Collocation for Multi-Tenant Deep Learning Training

arXiv:2508.19073v4 Announce Type: replace-cross Abstract: Deep learning training commonly runs on shared multi-tenant GPU servers, where exclusive allocation provides isolation but can leave resource...

By Ehsan Yousefzadeh-Asl-Miandoab, B\"u\c{s}ra Karatay Demiray, Florina M. Ciorba, Pamela Delgado, P{\i}nar T\"oz\"un

llms

arXiv Machine Learning

1d ago

Uncovering Cross-Objective Interference in Multi-Objective Alignment

arXiv:2602.06869v3 Announce Type: replace-cross Abstract: We study a persistent failure mode in multi-objective alignment for large language models (LLMs), in which scalarized training improves only...

By Yining Lu, Meng Jiang

llms fine-tuning safety

arXiv Machine Learning

1d ago

Generalizable Dense Reward for Long-Horizon Robotic Tasks

arXiv:2604.00055v2 Announce Type: replace-cross Abstract: Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilit...

By Silong Yong, Stephen Sheng, Carl Qi, Xiaojie Wang, Evan Sheehan, Anurag Shivaprasad, Yaqi Xie, Katia Sycara, Yesh Dattatreya

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Computation and Language

1d ago

Capacity, Responsiveness and Alignment: What Makes a Latent Structure Actionable

arXiv:2610.06897v1 Announce Type: new Abstract: Localizing latent structures in the activation space of language models (LMs) is central to understanding and controlling their behavior. Yet, localize...

By Or Shafran, Mor Geva

llms safety

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