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

CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

arXiv:2609.36689v1 Announce Type: new Abstract: Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that v...

By Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo

llms benchmarks safety

arXiv Machine Learning

Sep 30

Learned Queries and Keys Are All You Need: Replacing the Value Projection with Structured Transforms

arXiv:2609.36698v1 Announce Type: new Abstract: To reduce the number of parameters and cache memory requirements of transformers we introduce dual-headed transformers instead of three heads. We studi...

By Ene Meco, Emadeldeen Hamdan, A. Enis Cetin

llms

arXiv Machine Learning

Sep 30

EasyPPO: Stabilizing the Critic Is Key

arXiv:2609.36802v1 Announce Type: new Abstract: A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning...

By Xuanyi Zhou, Qiuyang Mang, Huanzhi Mao, Dacheng Li, Wenhao Chai, Mayank Mishra, Yichuan Wang, Karthik Narasimhan, Alvin Cheung, Joseph E. Gonzalez

llms reinforcement-learning

arXiv Machine Learning

Sep 30

Architecture Alignment With Sparse Priors in Tabular Foundation Models

arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without...

By Tianqi Zhao, Tianyi Zhuang, Shuo Duan, Guanyang Wang, Yan Shuo Tan, Qiong Zhang

llms safety

arXiv Machine Learning

Sep 30

Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...

By Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo

llms

arXiv Machine Learning

Sep 30

Trident: Unifying Guarded Dispatch and Host Execution for PyTorch Triton Workloads

arXiv:2609.37241v1 Announce Type: new Abstract: User-written Triton kernels enable high-performance GPU computation within PyTorch, but their end-to-end latency can remain dominated by host-side orch...

By Jinjie Liu, Xiaoyan Liu, Shuhan Zhang, Wenjia Sun, Ruilin Yang, Chunlei Men, Yonghua Lin, Shaohua Li

llms agents

arXiv Machine Learning

Sep 30

Scaling Full Conformal Image Classifiers

arXiv:2609.37298v1 Announce Type: new Abstract: Conformal prediction provides set-valued predictions with distribution-free coverage guarantees, making it attractive for high-stakes image classificat...

By Julio Silva-Rodr\'iguez, Ender Konukoglu

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Sep 30

Scale Sensitivity in Low-Bit Post-Training Quantization: Curvature of the Quantization Error Landscape

arXiv:2609.37416v1 Announce Type: new Abstract: Post-training quantization (PTQ) methods in the GPTQ family minimize a layer-wise reconstruction error on a uniform grid whose scale must be chosen; th...

By Jonas von Berg, Massimiliano Datres, Carlo Knei{\ss}l, Gitta Kutyniok

llms efficiency

arXiv Machine Learning

Sep 30

Procedural Core: A Compact Recurrent Initialization for Vision Transformers

arXiv:2609.37631v1 Announce Type: new Abstract: Transformers are typically trained from random initialization, requiring all their capabilities to emerge from large-scale optimization. Recent work sh...

By Zachary Shinnick, Christian Intern\`o, Hemanth Saratchandran, Anton van den Hengel, Damien Teney

llms computer-vision

arXiv Machine Learning

Sep 30

LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling

arXiv:2609.37675v1 Announce Type: new Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and str...

By Biswajit Banerjee, Claudia Alvarez Carreno, Anton S. Petrov

llms rag nlp benchmarks

arXiv Machine Learning

Sep 30

Behavioral Capacity Certificates for Quantized Language Models

arXiv:2609.37887v1 Announce Type: new Abstract: Activation and key-value cache precision change what a quantized language model computes without altering its stored weights. Direct weight-code bounds...

By Arian Eamaz, Mojtaba Soltanalian

llms efficiency

arXiv Machine Learning

Sep 30

Time-Anchored Diffusion Language Models: Latent-Space Caching for Fast Generation

arXiv:2609.37924v1 Announce Type: new Abstract: Recent work on anchored diffusion language models improves denoising by shaping an intermediate latent space with supervised important-token targets. I...

By Joel Anto Paul, Litu Rout, Aditya Akella, Sanjay Shakkottai

llms diffusion efficiency benchmarks

arXiv Machine Learning

Sep 30

Alpha Diffusion Language Models: Factorization Alone Is Not the Problem

arXiv:2609.38066v1 Announce Type: new Abstract: Discrete diffusion language models can generate multiple tokens in parallel, but reducing the number of denoising steps can lead to inconsistent predic...

By Nikita Gushchin, Dmitry Baranchuk, Alexander Korotin

llms diffusion benchmarks

arXiv Machine Learning

Sep 30

AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks

arXiv:2609.37310v1 Announce Type: cross Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Ye...

By Thibaud Gloaguen, Robin Staab, Martin Vechev

llms agents benchmarks

arXiv Machine Learning

Sep 30

Are In-Context Images Worth 10 Dimensions?

arXiv:2609.37659v1 Announce Type: cross Abstract: There has been significant work on understanding the In-Context Learning capabilities of Large Language Models, especially on the induction circuit....

By Adhemar de Senneville, Xavier Bou, J\'er\'emy Anger, Rafael Grompone, Gabriele Facciolo

llms computer-vision

arXiv Machine Learning

Sep 30

Retrieval Capacity of Self-Attention Under Competition

arXiv:2609.37879v1 Announce Type: cross Abstract: How many tokens from its context does a language model actually use, and what determines that number? We study this question through self-attention....

By Timur Mudarisov, Mikhail Burtsev, Radu State

llms

arXiv Machine Learning

Sep 30

Learning What to Remember: Long-horizon Counterfactual Memory Optimization

arXiv:2609.37930v1 Announce Type: cross Abstract: Persistent textual memory allows language models to carry information across long interactions, but learning what to remember is fundamentally a cred...

By Jiaming Tang, Mingyan Liu, Armin Sarabi

llms

arXiv Machine Learning

Sep 30

Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning

arXiv:2602.02427v3 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant breakthroughs across various domains, but they can still produce unreliable or misleading ou...

By Qihao Wen, Jiahao Wang, Yang Nan, Pengfei He, Ravi Tandon, Han Xu

llms rag

arXiv Machine Learning

Sep 30

Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression

arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD...

By Ali Abbasi, Chayne Thrash, Haoran Qin, Shansita Sharma, Sepehr Seifi, Soheil Kolouri

llms benchmarks

arXiv AI

Sep 30

DScale: Scaling Block-Diffusion Speculative Decoding with Adaptive Verification

arXiv:2609.37532v1 Announce Type: cross Abstract: Growing large language model applications demand efficient inference. At high concurrency, block-diffusion speculative decoding suffers from verifica...

By Rongjian Chen, Minxian Xu, Zhengxin Fang, Kejiang Ye, Chengzhong Xu

llms diffusion efficiency

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