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

Oct 2

Measuring Structure in Graph Benchmark Datasets Using Graph Invariants

arXiv:2605.06462v2 Announce Type: replace Abstract: Progress in graph learning is hindered by benchmark practices that conflate the contributions of node features and graph structure, making it hard...

By Richard von Moos, Mathieu Alain, Bastian Rieck

llms benchmarks

arXiv Machine Learning

Oct 2

Prototype Guided Post-pretraining for Single-Cell Representation Learning

arXiv:2605.07938v2 Announce Type: replace Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...

By Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price, Jacqueline Isaacs

llms rag fine-tuning

arXiv Machine Learning

Oct 2

VIP-COP: Context Optimization for Tabular Foundation Models

arXiv:2605.12904v2 Announce Type: replace Abstract: Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new...

By Yilong Chen, Xueying Ding, Leman Akoglu

llms efficiency

arXiv Machine Learning

Oct 2

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders

arXiv:2605.16339v2 Announce Type: replace Abstract: Preference learning in large language models relies on reward models as proxies for human judgment. However, these models frequently exhibit prefer...

By Shunchang Liu, Xin Chen, Belen Martin Urcelay, Francesco Croce

llms diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Oct 2

Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series

arXiv:2605.20292v2 Announce Type: replace Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns s...

By Kwanhyung Lee, Juhwan Choi, Jongheon Kim, Joohyung Lee, Hyeongwon Jang, Jeonguk Lee, Jisoo Jung, Eunho Yang

llms benchmarks

arXiv Machine Learning

Oct 2

$\textit{BlockFormer}$ : Transformer-based inference from genomic contact maps

arXiv:2605.21617v3 Announce Type: replace Abstract: Locating genomic features from genomic contact maps, such as centromere identification from genome-wide chromosome conformation capture techniques,...

By Elo\"ise Touron, Pedro L. C. Rodrigues, Julyan Arbel, Nelle Varoquaux, Michael Arbel

llms safety

arXiv Machine Learning

Oct 2

One QK Channel, Many Sources: Tracing Low-Precision Attention Collapse

arXiv:2608.02091v2 Announce Type: replace Abstract: A bfloat16 transformer can train normally, then collapse abruptly. Prior work links collapse to structured attention errors and shows QK normalizat...

By Shuxiao Xie, Shuyang Xie, Yuan Cao, Dezhi Ran, Wei Yang, Tao Xie

llms

arXiv Machine Learning

Oct 2

RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting

arXiv:2609.34004v2 Announce Type: replace Abstract: Equity-relevant news evolves through temporally dependent corporate events, making historical information useful only when event continuity, inform...

By Tong Liu, Lanmiao Liu, Xiang Hu

llms agents

arXiv Machine Learning

Oct 2

Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts

arXiv:2605.12890v2 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-writte...

By Luxu Liang, Xiang Li

llms safety

arXiv Machine Learning

Oct 2

GeoFP8: Geometry-Aware FP8 Gradient Compression for Distributed LLM Training

arXiv:2607.07494v2 Announce Type: replace-cross Abstract: Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision fo...

By Jieying Wang, Zizhong Wang, Fangru Linghu, Shuyuan Fan, Jiajia Li, Zhao Zhang

llms efficiency

arXiv Computer Vision

Oct 2

Query Independent Variable Rate Visual Token Coding

arXiv:2610.00204v1 Announce Type: new Abstract: Visual-token compression for vision--language models is posed almost entirely as a selection problem: decide which tokens to keep and discard the rest....

By Hongbo Zhang, Zihao Yang, Liuyang Song, Daqian Yang, Haoyang Yao, Yan Wen, Zhengtao Yao

llms efficiency benchmarks

arXiv Computer Vision

Oct 2

Memorizon: Training World Models Beyond Their Context Window

arXiv:2610.00544v1 Announce Type: new Abstract: Streaming world models should render a place consistently across repeated visits. Directly supervising such revisits requires training samples that cap...

By Tingting Liao, Xuezhi Liang, Hao Li, Guangyi Liu

llms

arXiv Computer Vision

Oct 2

FORTE: Adaptive Scoring and Exact Keyframe Selection for Long-Video Question Answering

arXiv:2610.00573v1 Announce Type: new Abstract: Query-aware keyframe selection enables multimodal large language models (MLLMs) to process long videos using only a small set of question-relevant fram...

By Haifeng Huang, Biyin Xu, Chunsheng Xin, Yang Li

llms nlp multimodal benchmarks

arXiv Computer Vision

Oct 2

Paying for Too Many Tokens? Valid and Cost-Efficient Multimodal LLM Annotation with Simple Heuristics

arXiv:2610.00809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one fr...

By Zhixi Zhu, Kristina Gligoric

llms multimodal

arXiv Computer Vision

Oct 2

Machine Translation for Sign Languages

arXiv:2610.00881v1 Announce Type: new Abstract: Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation...

By Ozge Mercanoglu Sincan, Anton Pelykh, Edward Fish, Harry Walsh, JianHe Low, Karahan Sahin, Oline Ranum, Sobhan Asasi, Steven Emery, Richard Bowden

llms computer-vision nlp

arXiv Computer Vision

Oct 2

Video-Index: A Curated Meta-Benchmark for Video Understanding

arXiv:2610.00960v1 Announce Type: new Abstract: A video benchmark should reward the capability it claims to measure, yet models can exploit answer options, question text, or partial visual evidence....

By Enxin Song, Yinuo Xu, Shusheng Yang, Wenhao Chai, Jiatao Gu

llms agents benchmarks

arXiv Computer Vision

Oct 2

VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce V...

By Xianda Du, Max Ku, Weiming Ren, Zhi Rui Tam, Chunlin Ren, Ping Nie, Min-Hung Chen, Wenhu Chen

llms diffusion fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 2

Towards Automatic Video Annotation with ASH: Zero-Shot Open-Vocabulary Multi-Object Tracking and Segmentation

arXiv:2610.01022v1 Announce Type: new Abstract: Memory-attention-based Video Instance Segmentation (VIS) methods have demonstrated strong zero-shot tracking capability, yet their substantial memory r...

By Arash Rocky, Q. M. Jonathan Wu

llms computer-vision benchmarks

arXiv Computer Vision

Oct 2

HAWK: Rethinking Multimodal Drafting for Speculative Decoding

arXiv:2610.00623v1 Announce Type: new Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where li...

By Wenhan Yang, Anirudh Rao, Ashwin Chandra

llms efficiency multimodal benchmarks

arXiv Computer Vision

Oct 2

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently str...

By Yuliang Cai, Mohammad Rostami, Jesse Thomason

llms nlp multimodal benchmarks

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