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 13

STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU

arXiv:2607. 09385v1 Announce Type: cross Abstract: The growing adoption of large language model-based agents within operating system workflows has increased the importance of energy-efficient inference on laptop-class systems-on-chip (SoCs).

By Victor J. B. Jung, Gagandeep Singh, Joseph Melber, Kristof Denolf, Francesco Conti, Luca Benini

llms agents efficiency safety

arXiv AI

Jul 13

Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review

arXiv:2607. 09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.

By Jingbo Chen, He Wang, Wei Yuan, Yuqiao Lai, Zhenyan Lu

llms agents

arXiv AI

Jul 13

MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

arXiv:2607. 09142v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice.

By Runhan Shi, Quan Zhou, Yuqian Xu, Shuai Yang, Xin Wu, Zitong Zhou, Hui Liu, Bin Cha, Zheming Wang, Liya Li, Wei Wei, Haoyuan Hu, Jun Xu

llms multimodal benchmarks safety

arXiv AI

Jul 13

REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming

arXiv:2607. 07738v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows.

By Nicolas Koller, Andreas u. Schmidt

llms benchmarks safety

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

arXiv AI

Jul 13

Multimodal Reward Hacking in Reinforcement Learning

arXiv:2607. 09492v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to align multimodal large language models (MLLMs), but higher rewards do not always imply better task performance.

By Jiayu Yao, Yiwei Wang, Anmeng Zhang, Zhe Sun, Songsong Wang, Lingrui Mei, Yuyao Ge, Shenghua Liu

llms reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jul 13

Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices

arXiv:2607. 08786v1 Announce Type: cross Abstract: With the growing deployment of large language models (LLMs), LLM inference cost has become a key challenge.

By Tao Lu, Haoyu Wang, Zonghui Wang, Keshen Xiang, Jiaheng Zhang, Wenzhi Chen

llms efficiency

arXiv AI

Jul 13

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar

llms rag

arXiv AI

Jul 13

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.

By Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen

llms rag agents

arXiv Machine Learning

Jul 13

Sensitivity-Aware Thresholding and Token Routing for Activation Sparsification in Large Language Models

arXiv:2607. 08991v1 Announce Type: new Abstract: Efficient inference in Large Language Models (LLMs) requires deciding where computation can be reduced while preserving model quality.

By Bishmoy Paul, Youngmin Yi, Hoeseok Yang

llms efficiency

arXiv AI

Jul 13

Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge

arXiv:2511. 20297v2 Announce Type: replace Abstract: Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from experience: each new session rediscovers solutions from scratch.

By Shashank Kirtania, Param Biyani, Priyanshu Gupta, Yasharth Bajpai, Roshni Iyer, Sumit Gulwani, Gustavo Soares

llms rag agents robotics benchmarks

arXiv AI

Jul 13

NL-PAC: Specification Ambiguity and Certified Minimax Risk Floors in LLM-Mediated Supervision

arXiv:2607. 08961v1 Announce Type: cross Abstract: Large language models increasingly provide labels, evaluations, and feedback for tasks specified in natural language.

By Berkay Anahtarci

llms

arXiv AI

Jul 13

SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation

arXiv:2607. 08983v1 Announce Type: cross Abstract: While autonomous coding agents have significantly advanced automated test generation, they remain fundamentally limited by lazy generation, a phenomenon where agents prematurely terminate tasks and systematically avoid complex programmatic logic, resulting in inadequate code coverage.

By Sijia Gu, Noor Nashid, Ali Mesbah

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 13

A Sovereign, Open-Source Foundation Model for German and English

arXiv:2607. 09424v1 Announce Type: cross Abstract: We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English.

By The Soofi-Team, :, Benedikt Droste, David Fitzek, Ruben H\"arle, Lukas Helff, Maximilian Idahl, Alex Jude, Abbas Goher Khan, Maurice Kraus, Timm Ruland, Richard Rutmann, Sebastian Sztwiertnia, Markus Frey, Daniil Gurgurov, Jan Pfister, Tom R\"ohr, Sebastian von Rohrscheidt, J\"org Bienert, Nicolas Flores-Herr, Simon Gottschalk, Andreas Hotho, Kristian Kersting, Joachim K\"ohler, Alexander L\"oser, Wolfgang Nejdl, Simon Ostermann, Jan Plogsties, Patrick Putzky, Mehdi Ali, Michael Fromm, Max L\"ubbering

llms efficiency benchmarks

arXiv AI

Jul 13

PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers

arXiv:2607. 09590v1 Announce Type: cross Abstract: Precision industrial contact manipulation requires reliable robot policies under pose perturbations and contact-force constraints.

By Yujie Pang, Zudong Li

llms rag robotics fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 13

Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification

arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.

By Camila Piscioneri Magalh\~aes, Lucas Pascotti Valem

llms computer-vision efficiency multimodal

arXiv Machine Learning

Jul 13

Accelerating Large Language Model Inference with Self-Supervised Early Exits

arXiv:2407. 21082v3 Announce Type: replace-cross Abstract: This paper presents a modular approach to accelerate inference in large language models (LLMs) by adding early exit heads at intermediate transformer layers.

By Florian Valade

llms benchmarks

arXiv AI

Jul 13

Beyond Black-Box Obfuscation: Mechanistic Analysis and Defense of White-Box Monitors

arXiv:2505. 14300v2 Announce Type: replace Abstract: White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure safe model behavior.

By Maheep Chaudhary, Fazl Barez

llms agents robotics benchmarks

arXiv AI

Jul 13

REAL: REtrieval-reAsoning and Logic-constructed Attention Behaviors for Long-Context KV Cache Compression

arXiv:2508. 15806v2 Announce Type: replace-cross Abstract: The growing sequence length of large language models poses significant challenges for key-value (KV) caches.

By Mengjie Li, Yuan Feng, Xike Xie, William J. Song

llms efficiency benchmarks safety

arXiv AI

Jul 13

Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

arXiv:2607. 09316v1 Announce Type: cross Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process.

By Miguel Arana-Catania, Gillian Pink, Glenn Roe

llms fine-tuning

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