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

PatchKV: Weight-Space Compensation of KV Cache

arXiv:2609.39329v1 Announce Type: new Abstract: Long-context inference with Large Language Models (LLMs) is bottlenecked by the linearly growing memory of the key-value (KV) cache. Existing compressi...

By Chanryeol Lee, Chanhyuk Lee, Yeonwoo Choi, Donggyun Kim, Seunghoon Hong

llms efficiency benchmarks

arXiv Machine Learning

Oct 2

CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data

arXiv:2609.39124v1 Announce Type: new Abstract: Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many spe...

By Mohamed Amine Ketata, Maximilian Schambach, Stephan G\"unnemann

llms diffusion

arXiv Machine Learning

Oct 2

Learning Reliable GUI Agents under Imperfect Priors

arXiv:2609.39547v1 Announce Type: new Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GU...

By Bo Han, Qianyi Wang, Shuai Liu, Xiong Zifan, Changqiao Wu, Yuanfa Li, Pengzhi Gao, Wei Liu, Jian Luan, Heng Qu, Yunpeng Song, Zhongmin Cai

llms rag agents multimodal benchmarks

arXiv Machine Learning

Oct 2

Self-Repulsive Sampling for Diffusion Language Models

arXiv:2609.39560v1 Announce Type: new Abstract: Sampling several responses and voting over their answers can improve a language model's accuracy, but repeated answers limit the benefit of additional...

By Michael Helcig, Martin Jaggi

llms diffusion benchmarks

arXiv Machine Learning

Oct 2

A library for differentiable signal processing and machine learning on the sphere

arXiv:2609.39737v1 Announce Type: new Abstract: The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, i...

By Thorsten Kurth, Max Rietmann, Mauro Bisson, Andrea Paris, Alberto Carpentieri, Jean Kossaifi, Anima Anandkumar, Christian Hundt, Boris Bonev

llms benchmarks

arXiv Machine Learning

Oct 2

Stable Transformers for Graph Generation

arXiv:2609.39739v1 Announce Type: new Abstract: Graph generative models increasingly rely on Graph Transformers (GT) to capture complex dependencies among nodes and edges. While deeper architectures...

By Luca Miglior, Alessio Gravina, Davide Bacciu

llms benchmarks

arXiv Machine Learning

Oct 2

GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning

arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge, Qiaoyu Tan

llms agents benchmarks

arXiv Machine Learning

Oct 2

Beyond Accuracy: Prefix-Invariant Realizations of Low-Precision Fast Matrix Multiplication

arXiv:2609.39816v1 Announce Type: new Abstract: Fast matrix multiplication saves multiplications through exact cancellation, but rounding sums that mix token rows can leave contributions from later t...

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

llms efficiency

arXiv Machine Learning

Oct 2

Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining

arXiv:2609.40089v1 Announce Type: new Abstract: Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabiliti...

By Lukas Thede, Shengzhuang Chen, Stefan Winzeck, Matthias Bethge, Zeynep Akata, Jonathan Richard Schwarz

llms

arXiv Machine Learning

Oct 2

Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?

arXiv:2609.40335v1 Announce Type: new Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). M...

By Razan El Mais, Ali Chehab, Ibrahim Issa, Razane Tajeddine

llms rag fine-tuning safety

arXiv Machine Learning

Oct 2

SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision

arXiv:2609.39679v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD...

By Rong Wan, Wei Xie, Jiaxi Li, Wenwu Wang, Lu Yin, Yiliao Song, Xilu Wang

llms fine-tuning

arXiv Machine Learning

Oct 2

PassGPT+: Leveraging Linguistic Priors for Password Modeling

arXiv:2609.39880v1 Announce Type: cross Abstract: Passwords remain the dominant online authentication mechanism, and understanding how humans choose them is essential for defensive strength estimatio...

By Rajneesh Anand, Neeraj Lakshmanan, Masoud Yari

llms diffusion nlp benchmarks

arXiv Machine Learning

Oct 2

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD

arXiv:2507.12133v2 Announce Type: replace Abstract: Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fing...

By Hanwen Liu, Yuhe Huang, Yifeng Gong, Yanjie Zhai, Jiaxuan Lu

llms efficiency benchmarks

arXiv Machine Learning

Oct 2

Contrastive Representation Shaping for LLM Unlearning

arXiv:2601.22028v2 Announce Type: replace Abstract: Most LLM unlearning methods aim to approximate retrain-from-scratch behaviors with minimal distribution shift, often via alignment-style objectives...

By Haoran Tang, Rajiv Khanna

llms rag benchmarks safety

arXiv Machine Learning

Oct 2

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantiz...

By Maximilian Kleinegger, Elvir Crn\v{c}evi\'c, Dan Alistarh

llms efficiency benchmarks

arXiv Machine Learning

Oct 2

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm,...

By Haokai Ma, Lee Yan Zhen, Gang Yang, Yunxiang Chen, Yunshan Ma, Tat-Seng Chua, Ee-Chien Chang

llms reinforcement-learning efficiency benchmarks

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

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