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

Certified Mechanistic Interpretability: Lifting Single-Input Findings to Bounded Neighbourhoods

arXiv:2609.26112v1 Announce Type: new Abstract: Mechanistic interpretability reverse-engineers transformer circuits one input at a time, leaving observed mechanisms without guarantees over bounded in...

By Zhen Zhang, Yanliang Huang, Peng Xie, Wenyuan Wu, Amr Alanwar

llms safety

arXiv Machine Learning

Sep 23

Block-Level Weight-Space Structure Persists Under Post-Training: An Empirical Study Across LLM Families

arXiv:2609.26147v1 Announce Type: new Abstract: Modern LLMs are deployed as families of post-trained variants (base, instruct, chat, code) derived from a shared set of pre-trained weights. We present...

By Zhaohui Wang

llms benchmarks

arXiv Machine Learning

Sep 23

MSA-CITE: A Co-Adapted LoRA Specialist Ecology for Fixed-Budget Small-Model Inference

arXiv:2609.26217v1 Announce Type: new Abstract: Compact language models are typically deployed by retaining a single post-training checkpoint and sampling it repeatedly. In this work, we challenge th...

By Ruitong Li, Binjie Guo, Aisheng Mo, Guowei Su, Jie Li, Ru Zhang

llms rag fine-tuning

arXiv Machine Learning

Sep 23

Disaggregated Quantization: Specializing LLM Prefill and Decode

arXiv:2609.26333v1 Announce Type: new Abstract: Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce me...

By Andrei Panferov, Maximilian Kleinegger, Sweta Priyadarshi, Tijmen Blankevoort, Dan Alistarh

llms efficiency benchmarks

arXiv Machine Learning

Sep 23

PACT: From Credit Assignment to Critic Alignment

arXiv:2609.26355v1 Announce Type: new Abstract: Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted ma...

By Jiayan Fu, Hang Xu, Yong Zhang, Zhaokai Luo, Yao Hu, Dongyan Zhao, Mu Chuan

llms agents reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Sep 23

EquivSVA: A Formally Verified Dataset of Behavioral Assertions Across Equivalent RTL Implementations

arXiv:2609.26751v1 Announce Type: new Abstract: Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level des...

By FNU Aditi

llms benchmarks

arXiv Machine Learning

Sep 23

How Strongly Should Task State Influence an LLM Agent?

arXiv:2609.25686v1 Announce Type: cross Abstract: Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent...

By Chenyu Zhang, Wonbin Kweon, Jiawei Han

llms agents

arXiv Machine Learning

Sep 23

One Domain, Many Tongues: Composing Domain and Language LoRAs for Cross-Lingual Remote-Sensing MLLMs without Paired Data

arXiv:2609.26097v1 Announce Type: cross Abstract: Remote-sensing (RS) multimodal large language models (MLLMs) are trained and evaluated only in English, while text-only instruction data covers over...

By Xuechen Li

llms fine-tuning multimodal

arXiv Machine Learning

Sep 23

On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

arXiv:2609.26388v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in...

By Xin Shen (Nanjing University, Nanjing, China), San-Zhuo Xi (Nanjing University, Nanjing, China), Yali Du (Nanjing University, Nanjing, China), Ming Li (Nanjing University, Nanjing, China)

llms fine-tuning

arXiv Machine Learning

Sep 23

Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models

arXiv:2609.26598v1 Announce Type: cross Abstract: The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challen...

By Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du, Li Sun, Zhu Han

llms diffusion

arXiv Machine Learning

Sep 23

Discovery-Driven Integration of Disjoint Tables via Text

arXiv:2609.26658v1 Announce Type: cross Abstract: Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit att...

By Md Ataur Rahman, Dimitris Sacharidis, Oscar Romero, Sergi Nadal

llms multimodal benchmarks

arXiv Machine Learning

Sep 23

CAffNet: Hard Constraint-Affine Neural Networks

arXiv:2605.24437v2 Announce Type: replace Abstract: We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural net...

By Yang Zhao, Jungeun Lee, Jeong hwan Jeon, Sze Zheng Yong

llms rag

arXiv Machine Learning

Sep 23

Intrinsic Interaction Geometry Controls the Low-Rank Complexity of Softmax Attention

arXiv:2608.28150v2 Announce Type: replace Abstract: How much matrix rank is required to preserve every bounded value output of normalized softmax attention? We study the unrestricted maximum-row-\(\e...

By Yuhe Sui, Jianing Zhang, Yingzhi Tang

llms

arXiv Machine Learning

Sep 23

The Challenge of Identifying the Origin of Black-Box Large Language Models

arXiv:2503.04332v2 Announce Type: replace-cross Abstract: The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we...

By Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, Yang Zhang

llms rag benchmarks safety

arXiv Machine Learning

Sep 23

Text-only adaptation in LLM-based ASR through text denoising

arXiv:2601.20900v4 Announce Type: replace-cross Abstract: Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet...

By Andr\'es Carofilis, Sergio Burdisso, Esa\'u Villatoro-Tello, Shashi Kumar, Kadri Hacioglu, Srikanth Madikeri, Pradeep Rangappa, Manjunath K E, Petr Motlicek, Shankar Venkatesan, Andreas Stolcke

llms fine-tuning multimodal benchmarks safety