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 22

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining

arXiv:2505.19893v2 Announce Type: replace Abstract: Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introduce E...

By Melis Ilayda Bal, Volkan Cevher, Michael Muehlebach

llms efficiency

arXiv Machine Learning

Sep 22

Disassociating performance from compositional feature learning

arXiv:2505.09716v4 Announce Type: replace Abstract: Out-of-distribution (OOD) generalisation through composition requires a system to discover invariant properties from input-output associations and...

By George Dimitriadis, Spyridon Samothrakis

llms benchmarks safety

arXiv Machine Learning

Sep 22

Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization

arXiv:2512.07419v3 Announce Type: replace Abstract: Mixed-Precision Quantization (MPQ) liberates Deep Neural Networks (DNNs) from the Out-Of-Memory (OOM) bottleneck and has garnered increasing resear...

By Haidong Kang, Jun Du, Guo Yu

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 22

Gradient-Gated DPO: Stabilizing Preference Optimization in Language Models

arXiv:2605.02626v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) improves relative preference by increasing the margin between chosen and rejected responses, but this objectiv...

By Inoussa Mouiche, Abderaouf Bahi

llms reinforcement-learning

arXiv Machine Learning

Sep 22

When Calibration Depends on the Scoring Rule: Quantized Biomedical LLM Classification

arXiv:2608.03854v4 Announce Type: replace Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...

By Anton Rasmussen, Hong Qin

llms efficiency

arXiv Machine Learning

Sep 22

MMS-VPR: A Fine-Grained Multimodal Street-Level Visual Place Recognition Dataset and Evaluation Benchmark for Dense Pedestrian Environments

arXiv:2505.12254v3 Announce Type: replace-cross Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...

By Yiwei Ou, Xiaobin Ren, Ronggui Sun, Guansong Gao, Kaiqi Zhao, Manfredo Manfredini

llms multimodal benchmarks

arXiv Machine Learning

Sep 22

TempCore: Are Video QA Benchmarks Temporally Grounded?

arXiv:2509.01167v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current...

By Hyunjong Ok, Jaeho Lee

llms multimodal benchmarks

arXiv Machine Learning

Sep 22

IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu

arXiv:2603.01590v2 Announce Type: replace-cross Abstract: Content-driven platforms such as Xiaohongshu often leverage click-through rate (CTR) prediction models for recommendation. However, these mod...

By Yubin Zhang, Haiming Xu, Guillaume Salha-Galvan, Ruiyan Han, Feiyang Xiao, Yanhua Huang, Li Lin, Yang Luo, Yao Hu

llms rag multimodal

arXiv Machine Learning

Sep 22

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

arXiv:2605.27690v2 Announce Type: replace-cross Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate st...

By Jiaqian Li, Yanshu Li, Boxuan Zhang, Ruixiang Tang, Kuan-Hao Huang

llms agents benchmarks safety