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

Jul 21

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.

By Jiarui Feng, Donghong Cai, Yixin Chen, Muhan Zhang

llms fine-tuning safety

arXiv AI

Jul 21

mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks

arXiv:2601. 02451v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test.

By Subhankar Mishra

llms benchmarks

arXiv AI

Jul 21

From Intent to Infrastructure: LLM-Driven Agent Compilers for ISAC Networks

arXiv:2607. 16269v1 Announce Type: cross Abstract: Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks.

By Lijie Zheng, Xudong Zhong, Baoquan Ren, Xiangwu Gong, Xinghui Zhu, Ji He

llms agents

arXiv AI

Jul 21

Autonomous mechanistic discovery of colorectal cancer vulnerabilities via multi-scale AI swarms

arXiv:2607. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.

By Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang, Simon McDade

llms agents safety

arXiv AI

Jul 21

An Agentic Interface for End-to-End Probabilistic Seismic Hazard and Risk Analysis

arXiv:2607. 16249v1 Announce Type: cross Abstract: Probabilistic seismic hazard and risk analyses are backbone to building codes, insurance pricing, and disaster management.

By Sreenath Vemula, Pierre Jehel, Fabrice Cotton, Filippo Gatti

llms agents

arXiv AI

Jul 21

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants

arXiv:2607. 16243v1 Announce Type: cross Abstract: Multimodal industrial anomaly inspection assistants are a critical component of next-generation smart factories, enabling interactive vision-language-based querying.

By Anushiya Arunan, Xin Li, Yan Qin, U-Xuan Tan, Nhu Khue Vuong, Xiaoli Li, Chau Yuen

llms multimodal benchmarks safety

arXiv AI

Jul 21

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

By Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Emmanuel Malherbe, C\'eline Hudelot, Pierre Colombo

llms

arXiv AI

Jul 21

Attentions Under the Microscope: A Comparative Study of Resource Utilization for Variants of Self-Attention

arXiv:2507. 07247v2 Announce Type: replace-cross Abstract: As large language models (LLMs) and visual language models (VLMs) grow in scale and application, attention mechanisms have become a central computational bottleneck due to their high memory and time complexity.

By Zhengyu Tian, Anantha Padmanaban Krishna Kumar, Hemant Krishnakumar, Reza Rawassizadeh

llms efficiency benchmarks

arXiv AI

Jul 21

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference

arXiv:2607. 17733v1 Announce Type: cross Abstract: 4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers.

By Simla Burcu Harma, Danila Mishin, Zhengyuan Su, Ayan Chakraborty, Elizaveta Kostenok, Dongho Ha, Babak Falsafi, Martin Jaggi, Yunho Oh, Amir Yazdanbakhsh

llms efficiency benchmarks

arXiv AI

Jul 21

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.

By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao

llms

arXiv AI

Jul 21

CIGPO: Contextual Information-Gain Policy Optimization for Multi-Turn Evidence-Reading LLM Agents

arXiv:2607. 16244v1 Announce Type: cross Abstract: Training multi-turn evidence-reading agents with outcome-only reinforcement learning is unstable because intermediate turns receive little direct credit.

By Hao Dou

llms agents reinforcement-learning

arXiv AI

Jul 21

Lomekwi: Resource-Bounded Tool Discovery in LLM Agents

arXiv:2607. 16961v1 Announce Type: new Abstract: Existing tool-use benchmarks report a single success rate for complex, multistep tasks.

By Roshan Klein-Seetharaman, Daniel Wang, Andrew Xu

llms agents benchmarks

arXiv AI

Jul 21

Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift

arXiv:2607. 17384v1 Announce Type: new Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM) ensembles.

By Junade Ali

llms agents benchmarks

arXiv AI

Jul 21

Constraint-Anchored Reasoning Traces

arXiv:2607. 16727v1 Announce Type: new Abstract: Autoregressive multimodal large language models (MLLMs) suffer from error snowballing: a single incorrect inference early in a chainof-thought (CoT) trace corrupts all downstream reasoning.

By Zehua Cheng, Wei Dai, Jiahao Sun

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 21

FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images

arXiv:2607. 16819v1 Announce Type: new Abstract: In recent years, large-scale vision-language models have been driving a paradigm shift in intelligent remote sensing image interpretation.

By Yi Yang, Xiaokun Zhang, Yuxuan Li, Ruyi Zhang, Xinpeng Zhou, Haipeng Wang

llms reinforcement-learning multimodal

arXiv AI

Jul 21

AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization

arXiv:2607. 17281v1 Announce Type: cross Abstract: Auto-bidding plays an essential role in online advertising, automatically adjusting bids for advertisers to optimize their commercial goals.

By Yuejia Dou, Hesong Wang, Xinyu Zhang, Tianyu Wang, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng, Qi Qi

llms agents efficiency safety

arXiv AI

Jul 21

Some Large Language Models Exhibit Consistent Risk Attitudes

arXiv:2607. 16197v1 Announce Type: new Abstract: As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action.

By Bowen Sun, Rui Min, Yuxi Wang, Brian Odegaard, Qi Wang, Jing Du

llms agents safety

arXiv AI

Jul 21

ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation

arXiv:2601. 07121v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging.

By Makoto Sato

llms agents

arXiv Machine Learning

Jul 21

LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats

arXiv:2607. 16227v1 Announce Type: new Abstract: LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks.

By Ruppikha Sree Shankar, Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan, Troy Paulus Asia, Saptarshi Sengupta

llms safety

arXiv AI

Jul 21

Distilled Reinforcement Learning for LLM Post-training

arXiv:2607. 17247v1 Announce Type: cross Abstract: Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment.

By Chen Wang, Zhaochun Li, Jionghao Bai, Yining Zhang, Hexuan Deng, Ge Lan, Yue Wang

llms reinforcement-learning efficiency safety

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