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

Sep 23

HABILIS Brain 0: Geometry-Change Supervision for Vision-Language-Action and Residual Flow Recovery

arXiv:2609.25558v1 Announce Type: cross Abstract: Vision-language-action policies benefit from geometric supervision, but current-frame geometry alone does not explicitly describe the changes associa...

By Jinu Pahk, Jesoon Kang, Taegeon Park, Jisu An, Soo Min Kimm, Jaejoon Kim, Byoung-Tak Zhang

llms robotics multimodal safety

arXiv Machine Learning

Sep 23

Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study

arXiv:2609.25978v1 Announce Type: cross Abstract: Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect...

By Peachapong Poolpol, Henrik H. J. Detjen, Eike Petersen

llms computer-vision benchmarks

arXiv Machine Learning

Sep 23

Identifying Intelligent Processes via Online Sequential Testing

arXiv:2609.26193v1 Announce Type: cross Abstract: Active sequential hypothesis testing studies how to identify an unknown hypothesis with a given set of sensing actions. We study this in the setting...

By Aritra Das, Debayan Gupta

llms

arXiv Machine Learning

Sep 23

PatchKV: Efficient KV Cache Recovery for Dynamically Edited LLM Contexts

arXiv:2609.26219v1 Announce Type: cross Abstract: Long-running LLM agent workflows often revise interior context spans while retaining long suffixes. Although suffix tokens remain unchanged, altered...

By Guotao Yang, Rui Guo, Siwei He, Sheng Chen, Yitao Hu, Keqiu Li

llms agents efficiency

arXiv Machine Learning

Sep 23

Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors

arXiv:2504.20106v4 Announce Type: replace Abstract: Ensuring that large language models (LLMs) are both helpful and harmless is a critical challenge, as overly strict constraints can lead to excessiv...

By Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun

llms reinforcement-learning safety

arXiv Machine Learning

Sep 23

Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint

arXiv:2508.20443v3 Announce Type: replace Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership...

By Zhihao Liu, Jian Lou, Yuke Hu, Xiaochen Li, Yitian Chen, Tailun Chen, Zhizhen Qin, Kui Ren, Zhan Qin

llms benchmarks safety

arXiv Machine Learning

Sep 23

Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy

arXiv:2509.00961v3 Announce Type: replace-cross Abstract: Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that tra...

By Lun Ai, Johannes Langer, Ute Schmid, Stephen Muggleton

llms

arXiv Machine Learning

Sep 23

VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean

arXiv:2602.18307v2 Announce Type: replace-cross Abstract: Large language models have achieved striking results in interactive theorem proving, particularly in Lean. However, most benchmarks for LLM-b...

By Yutong Xin, Qiaochu Chen, Greg Durrett, I\c{s}il Dillig

llms benchmarks

arXiv Machine Learning

Sep 23

Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction

arXiv:2603.29529v2 Announce Type: replace-cross Abstract: Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample...

By L. Ghiringhelli, A. Zambon, G. Tiana

llms rag

arXiv Machine Learning

Sep 23

LLM Ghostbusters: Surgical Package Hallucination Suppression via Adaptive Unlearning

arXiv:2605.01047v2 Announce Type: replace-cross Abstract: Hallucinations remain an unsolved problem for LLMs, and package hallucinations are a particularly dangerous instance of this phenomenon. Pack...

By Joseph Spracklen, Pedram Aghazadeh, Farinaz Koushanfar, Murtuza Jadliwala

llms agents benchmarks safety