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

Jun 11

RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark

arXiv:2606. 11260v1 Announce Type: cross Abstract: Humans process rich auditory environments through tightly integrated cognitive capabilities such as audio perception, audio reasoning, and memory.

By Hongyu Jin, Siyi Wang, Yang Xiao, Jiaheng Dong, Shihong Tan, Kaiyuan peng, Georgiana Juravle, Shanquan Chen, Gongping Huang, Hong Jia, Eun-Jung Holden, James Bailey, Ting Dang

llms multimodal benchmarks

arXiv AI

Jun 11

Task-Aligned Stability Analysis of Vision-Language Models for Autonomous Driving Hazard Detection

arXiv:2606. 11889v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used for scene understanding in autonomous driving, but robustness analysis often relies on task-agnostic embedding stability alone.

By Everett Richards

llms rag agents robotics multimodal benchmarks

arXiv AI

Jun 11

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

arXiv:2606. 11817v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code.

By Yitong Zhang, Shiteng Lu, Jia Li

llms benchmarks safety

arXiv Machine Learning

Jun 11

Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

arXiv:2511. 14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception.

By Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni, Georgia Chalvatzaki

llms rag agents reinforcement-learning robotics multimodal

arXiv AI

Jun 11

Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity

arXiv:2509. 09794v5 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven analysis across building and urban energy systems.

By Jackson Eshbaugh, Chetan Tiwari, Jorge Silveyra

llms multimodal safety

arXiv AI

Jun 11

Exploration Structure in LLM Agents for Multi-File Change Localization

arXiv:2606. 11976v1 Announce Type: cross Abstract: Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue.

By Akeela Darryl Fattha, Kia Ying Chua, Lingxiao Jiang, Laura Wynter

llms agents benchmarks

arXiv Machine Learning

Jun 11

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

arXiv:2605. 04893v2 Announce Type: replace Abstract: When a language model processes a hallucinated response, its attention routing tends to fail in one of two shapes: over-concentrating on a narrow set of positions, or spreading so diffusely that relevance is diluted, and the shape of the failure carries diagnostic signal.

By Dominik Dahlem, Diego Maniloff, Mac Misiura

llms benchmarks

arXiv Machine Learning

Jun 11

Apertus LLM Family Expansion via Distillation and Quantization

arXiv:2605. 29128v2 Announce Type: replace Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating the need for the models to satisfy certain budget and hardware constraints.

By Andrei Panferov, Davit Melikidze, Martin Jaggi, Dan Alistarh

llms efficiency

arXiv Machine Learning

Jun 11

Mechanisms of Introspective Awareness

arXiv:2603. 21396v5 Announce Type: replace Abstract: Recent work has shown that LLMs can sometimes detect when steering vectors are injected into their residual stream and identify the injected concept -- a phenomenon termed "introspective awareness.

By Uzay Macar, Li Yang, Atticus Wang, Peter Wallich, Emmanuel Ameisen, Jack Lindsey

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 11

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking

arXiv:2602. 10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limit expressivity and robust state tracking.

By Vaisakh Shaj, Cameron Barker, Aidan Scannell, Andras Szecsenyi, Elliot J. Crowley, Amos Storkey

llms robotics benchmarks

arXiv AI

Jun 11

Learning to Inject: Automated Prompt Injection via Reinforcement Learning

arXiv:2602. 05746v2 Announce Type: replace-cross Abstract: Prompt injection is a critical vulnerability in LLM agents, yet the strongest methods still rely on human red-teamers and hand-crafted prompts.

By Xin Chen, Jie Zhang, Florian Tram\`er

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 11

When Probing Accuracy Saturates, Fragility Resolves: A Complementary Metric for LLM Pre-Training Analysis

arXiv:2606. 11375v1 Announce Type: cross Abstract: Standard linear probing declares a property "encoded" when a classifier on hidden states achieves high accuracy.

By Orion Reblitz-Richardson

llms fine-tuning

arXiv AI

Jun 11

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.

By Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.

By C\'esar Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Mart\'in Carballo-Pacheco

llms agents fine-tuning benchmarks

arXiv AI

Jun 11

"That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments

arXiv:2606. 12073v1 Announce Type: cross Abstract: Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking.

By Jason Miklian, John E. Katsos

llms

arXiv Machine Learning

Jun 11

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

arXiv:2606. 11257v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generation.

By Zhiyuan Cheng, Longying Lai

llms rag efficiency benchmarks safety

arXiv Machine Learning

Jun 11

A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks

arXiv:2606. 11236v1 Announce Type: cross Abstract: Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients.

By Yechan Kang, Yongjin Kweon, Mingyeong Seo, Sohee Park, Yeonguk Jeon, Jongkil Park, Hyun Jae Jang, Jaewook Kim, YeonJoo Jeong, Suyoun Lee, Seongsik Park

llms computer-vision

arXiv AI

Jun 11

Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models

arXiv:2606. 11211v1 Announce Type: cross Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment.

By Prakul Sunil Hiremath, Harshit R. Hiremath

llms

arXiv Machine Learning

Jun 11

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.

By Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu, Lixin Fan, Yi Yang

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 11

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching

arXiv:2606. 11583v1 Announce Type: new Abstract: Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce.

By Zhuoyi Peng, Hanlin Gu, Lixin Fan, Yi Yang

llms reinforcement-learning benchmarks

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