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

Jul 30

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607. 26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications.

By Hongqiang Lin, Chao Liu, Xiaofan Bai, Xuan Jin, Yuhong Li, Nenggan Zheng, Xipeng Cao

llms agents benchmarks

arXiv Machine Learning

Jul 30

The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy

arXiv:2607. 26648v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates.

By Zeyu Wang

llms efficiency

arXiv Machine Learning

Jul 30

An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI

arXiv:2607. 26746v1 Announce Type: cross Abstract: Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies inherent in functional brain connectivity, which limit the effectiveness of traditional approaches based on handcrafted connectivity features or conventional machine learning models.

By Harshiddhi Pathak, Gowtham Reddy N, Mrinal Acharya, Manjunatha Mahadevappa

llms

arXiv Machine Learning

Jul 30

Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents

arXiv:2607. 26865v1 Announce Type: cross Abstract: LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems.

By Amirmohammad Farzaneh, Osvaldo Simeone

llms agents nlp robotics benchmarks

arXiv Machine Learning

Jul 30

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

arXiv:2607. 27030v1 Announce Type: cross Abstract: LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs across 78 projects, including OpenSSL, curl, and GnuTLS.

By Petr Simecek, Elnaz Babayeva, Jiri Balhar, Michal Bida, Michal Buran, Vaclav Cadek, Luigino Camastra, Tomas Dulka, Michal Janocko, Tomas Klohna, Pavel Kohout, Ondrej Kokes, Adam Krivka, Jakub Kubik, Patrik Mada, Igor Morgenstern, Marek Pavelka, Joshua Rogers, Petr Stastny, Jan Tattermusch, Dmitrijs Trizna, Martin Votruba, Guido Vranken, Jakub Zikl, Evelina Gabasova, Stanislav Fort

llms benchmarks

arXiv Machine Learning

Jul 30

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities

arXiv:2505. 01043v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive performance across various domains.

By Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo, Han Hu, Guoxia Wang, Dianhai Yu, Yonggang Wen, Dacheng Tao

llms efficiency

arXiv Machine Learning

Jul 30

Transformers Can Learn Rules They've Never Seen: Proof of Computation Beyond Interpolation

arXiv:2603. 17019v2 Announce Type: replace Abstract: A central question in the debate over large language models is whether transformers can learn rules they have never seen, or whether they can only interpolate: predict new cases from their similarity to training examples.

By Andy Gray

llms benchmarks

arXiv Machine Learning

Jul 30

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs

arXiv:2602. 18600v5 Announce Type: replace Abstract: Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI).

By Ziqiao Shang, Ling-Yue Ge, Zian Xu, Zi-Jian Cheng, Shi-Yu Tian, Zhenyu Huang, Wenbo Fu, Weiming Wu, Yang Chen, Xiangwen Zhang, Yulan Hu, Bin Liu, Lan-Zhe Guo

llms multimodal benchmarks

arXiv Machine Learning

Jul 30

Towards Verifiable Transformers: Solver-Checkable Circuit Explanations

arXiv:2605. 24033v2 Announce Type: replace Abstract: Mechanistic interpretability typically discovers circuits and then argues what they do from examples and ablations.

By Neel Somani

llms rag safety

arXiv Machine Learning

Jul 30

Optimal Causal Annotations: An Application to Casenotes in Social Services

arXiv:2502. 10605v4 Announce Type: replace-cross Abstract: Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain.

By Ezinne Nwankwo, Lauri Goldkind, Angela Zhou

llms safety

arXiv Machine Learning

Jul 30

Adaptively Robust LLM Monitoring via Activation Watermarking

arXiv:2603. 23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent.

By Toluwani Aremu, Daniil Ognev, Samuele Poppi, Nils Lukas

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 30

Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT

arXiv:2604. 10094v2 Announce Type: replace-cross Abstract: Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies.

By Vishal V. Batchu, Michelangelo Conserva, Alex Wilson, Anna M. Michalak, Varun Gulshan, Philip G. Brodrick, Andrew K. Thorpe, Christopher V. Arsdale

llms computer-vision efficiency benchmarks safety

arXiv Machine Learning

Jul 30

ClockRoPE: Random Fourier Rotations for Temporal Routine Modeling

arXiv:2607. 26369v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) has been widely adopted in transformer-based large language models.

By Yiwen Chen, Joshua Ainslie, Krzysztof Choromanski, Xiang Gao, Su-Lin Wu, Yiping Yuan, Qian Sun

llms rag

arXiv Machine Learning

Jul 30

Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models

arXiv:2607. 26173v1 Announce Type: new Abstract: Alignment training, model organisms, and toy models are usually treated as separate research areas.

By Anton de la Fuente, Arthur Conmy

llms rag fine-tuning safety

arXiv Machine Learning

Jul 30

DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning

arXiv:2607. 26457v1 Announce Type: new Abstract: Reinforcement learning is a natural post-training paradigm for code-oriented large language models because generated programs can be evaluated through parsing, execution, unit tests, and structural analysis.

By Shuhang Wang, Ziming Li, Hui Cheng

llms reinforcement-learning

arXiv Machine Learning

Jul 30

From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models

arXiv:2607. 26504v1 Announce Type: new Abstract: Many discrete reasoning tasks, such as code generation, are inherently non-causal: programmers move between high-level structure and local details, a process we call any-order inference.

By Seunggeun Kim, Jaeyeon Kim, Taekyun Lee, Yuyuan Chen, Yilun Du, Sham Kakade, Sitan Chen

llms diffusion

arXiv Machine Learning

Jul 30

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models

arXiv:2607. 26515v1 Announce Type: new Abstract: We present, to our knowledge, the first end-to-end FP4 RL post-training, in which both the rollout and training policies, including their forward and backward passes, operate at 4-bit precision.

By Hei Yi Mak, Shadan Golestan, Hoang Le, Mehran Taghian Jazi, Yunke Peng, Yaoyuan Wang, Yao Wang, Junsong Wang, Tianchi Hu, Fengchen He, Guipeng Hu, Tanzila Rahman, Anandharaju Durai Raju

llms reinforcement-learning efficiency

arXiv Machine Learning

Jul 30

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

arXiv:2607. 26599v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine.

By Jialu Xu, Mengkun Liang, Guannan Liu, Xiaojie Mao, Junjie Wu

llms benchmarks

arXiv Machine Learning

Jul 30

Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions

arXiv:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.

By Truong Giang Vu, Li Yang, Richard W. Pazzi

llms benchmarks

arXiv Machine Learning

Jul 30

Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models

arXiv:2607. 26845v1 Announce Type: new Abstract: Inference-time thinking improves the performance of large language models, but aggregate outcomes do not reveal whether models use available evidence more effectively or seek information that could improve future decisions.

By Hua-Dong Xiong, Xinyuan Yan, Ji-An Li, Jingming Xue, Marcelo G. Mattar, Robert C. Wilson

llms reinforcement-learning

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