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 30

Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs

arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-criti...

By Shenghong Dai, Shiva Kumar Pentyala, Yingchi Liu, Shubham Mehrotra, Suman Banerjee, James Zhu, Bin Bi, Sitaram Asur, Phil Mui

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Sep 30

BASE: Batch-Aware Selection of Experts Using Predicted Removal Error for Efficient MoE Decoding

arXiv:2609.36222v1 Announce Type: new Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferrin...

By Ali Abbasi, Justin Shi, Soheil Kolouri

llms efficiency

arXiv Machine Learning

Sep 30

Adapting Linear-Time Architectures for Tabular In-Context Learning

arXiv:2609.36337v1 Announce Type: new Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large d...

By David Schnurr, Felix Sarnthein, Thomas Hofmann, Imanol Schlag

llms

arXiv Machine Learning

Sep 30

Invariant Atoms: Sparse Coordinates of Local Semantic Geometry in Language Model Representations

arXiv:2609.36451v1 Announce Type: new Abstract: Large language models often preserve meaning despite substantial changes in wording, style, and syntax, while small semantic edits can systematically a...

By Muhammad Ahtesham, Xin Zhong

llms

arXiv Machine Learning

Sep 30

Emergent phases of superposition: from partial to full representation

arXiv:2609.36455v1 Announce Type: new Abstract: Large language models are thought to represent features by vectors in a hidden space of dimension given by the model's width. Superposition, in which m...

By Lihao Guo, Yizhou Liu, Jeff Gore

llms

arXiv Machine Learning

Sep 30

AdaptArena: Evaluating Test-Time Personalization of Web Agents

arXiv:2609.36488v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...

By Dongchan Shin, Xing Han L\`u, Jiaqi Deng, Jay Gala, Tom\'as Vergara Browne, Jaewon Moon, Fengyuan Liu, Alexandre Drouin, Siva Reddy, Alexandre Lacoste

llms agents nlp benchmarks

arXiv Machine Learning

Sep 30

Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters

arXiv:2609.36584v1 Announce Type: new Abstract: Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, s...

By Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe, T. Patrick Xiao, Tianyi Chen

llms efficiency

arXiv Machine Learning

Sep 30

On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training

arXiv:2609.36659v1 Announce Type: new Abstract: The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these st...

By Shufan Shen, Zhongni Hou, Junshu Sun, Yufei Zhang, Wei Lin, Guojun Yin, Qingming Huang, Shuhui Wang

llms fine-tuning

arXiv Machine Learning

Sep 30

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

arXiv:2609.36686v1 Announce Type: new Abstract: Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored...

By Hada Melino Muhammad, Luan Pham, Laure Barri\`ere, Sachin Shetty, Leonardo Pulga, Flora D. Salim

llms rag benchmarks safety

arXiv Machine Learning

Sep 30

CALIBUDGET: Calibration-Guided Source Allocation for Fixed-Budget Mixed-Reasoning Adaptation

arXiv:2609.36721v1 Announce Type: new Abstract: Fixed-budget adaptation from heterogeneous data sources requires deciding not only how much data to use, but how much exposure each source receives. Si...

By Yupeng Chang, Yuan Wu

llms fine-tuning

arXiv Machine Learning

Sep 30

cktFormer: Transformer-Based Approach for Automated Analog Circuit Design

arXiv:2609.36752v1 Announce Type: new Abstract: Circuit design is a complex and iterative process that requires expertise in electronic engineering. It involves selecting components while meeting per...

By Pasindu Dodampegama, Praveen Wijesinghe, Naveen Basnayake, Keshawa Jayasundara, Tharindu Bandaragoda

llms benchmarks

arXiv Machine Learning

Sep 30

When Can Prefixes Compile LoRA? Exact Resource-Capped Tests for Frozen Attention

arXiv:2609.36766v1 Announce Type: new Abstract: Can a fixed continuous prefix replace a given low-rank adapter while the attention head stays frozen? In this research, we show that the answer depends...

By Joyanta Jyoti Mondal, Ibne Farabi Shihab

llms fine-tuning

arXiv Machine Learning

Sep 30

CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning

arXiv:2609.36820v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing...

By Wenbin Hu, Huihao Jing, Haochen Shi, Yuxuan Liu, Haoran Li, Yangqiu Song

llms agents

arXiv Machine Learning

Sep 30

Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models

arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate...

By Kodai Kawamura, Kenji Kawaguchi, Anji Liu

llms rag diffusion benchmarks

arXiv Machine Learning

Sep 30

Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning

arXiv:2609.37432v1 Announce Type: new Abstract: Looped reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also sup...

By T. Konstantin Rusch, Tim Seyde, Jared Boyer, Zach J. Patterson, Daniela Rus

llms agents reinforcement-learning robotics

arXiv Machine Learning

Sep 30

REVO: Rollout-Efficient Off-Policy Distillation via Variance-Guided Reuse

arXiv:2609.37500v1 Announce Type: new Abstract: On-policy distillation (OPD) trains language models using dense token-level teacher supervision on student-generated trajectories. However, its relianc...

By Yuxiao Yang, Shangzhe Li, Tianrun Yu, Kaixiang Zhao, Taylor W. Killian, Weitong Zhang

llms efficiency benchmarks

arXiv Machine Learning

Sep 30

Physical Muon: Orthogonalization as an Equilibrium Computation

arXiv:2609.37525v1 Announce Type: new Abstract: Physical neural networks and analog in-memory computing could reduce the energy cost of neural network training. Realizing this potential, however, req...

By Yuren Hao

llms safety

arXiv Machine Learning

Sep 30

Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable

arXiv:2609.37616v1 Announce Type: new Abstract: Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on th...

By Abhinav Rajeev Kumar (Lossfunk), Paras Chopra (Lossfunk)

llms benchmarks safety

arXiv Machine Learning

Sep 30

Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space

arXiv:2609.37731v1 Announce Type: new Abstract: Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform...

By Tue M. Cao, Lisiane Pruinelli, My T. Thai

llms safety

arXiv Machine Learning

Sep 30

TabFM: A Zero-Shot Foundation Model for Tabular Data

arXiv:2609.37959v1 Announce Type: new Abstract: Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We p...

By Weihao Kong, Erez Louidor Ilan, Shuxin Nie, Taman Narayan, Rajat Sen, Yichen Zhou, Deqing Fu, Samet Oymak, Abhimanyu Das

llms benchmarks

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