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 AI

Oct 2

MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration

arXiv:2606.22826v2 Announce Type: replace Abstract: Evaluating LLMs across many model variants---quantized, fine-tuned, or deployment-specific---requires running large benchmarks repeatedly, a proces...

By Devleena Das, Rajeev Patwari, Vikram Kumar Bukka, Nithin Kumar Guggilla, Elliott Delaye, Ashish Sirasao

llms fine-tuning efficiency benchmarks

arXiv AI

Oct 2

Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs

arXiv:2608.28421v2 Announce Type: replace Abstract: Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capab...

By Vishvesh Bhat

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Oct 2

Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning

arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how...

By Hongbo Chen, Guohua Lu, Ting Dang, Hong Jia

llms fine-tuning

arXiv AI

Oct 2

In Dialogue with Intelligence: Toward Insightful Co-Augmentation

arXiv:2505.22767v4 Announce Type: replace-cross Abstract: Dialogue with a large language model can lead a person to insight: a sudden change in how they understand a problem. This perspective asks ho...

By Eleni Vasilaki

llms

arXiv AI

Oct 2

GenGait: A Transformer-Based Model for Human Gait Anomaly Detection and Normative Twin Generation

arXiv:2604.01997v2 Announce Type: replace Abstract: Gait analysis provides an objective characterization of locomotor function and is widely used to support diagnosis and rehabilitation monitoring ac...

By Elisa Motta, Marta Lorenzini, Clara Mouawad, Alberto Ranavolo, Mariano Serrao, Arash Ajoudani

llms

arXiv AI

Oct 2

ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipation

arXiv:2512.03068v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) systems increasingly shape consequential decisions, creating value but also potential harms for individuals, soc...

By Nicoleta Tantalaki, Sophia Vei, Athena Vakali

llms safety

arXiv AI

Oct 2

Measuring Iterative Temporal Reasoning with Time Puzzles

arXiv:2601.07148v4 Announce Type: replace-cross Abstract: Tool use, such as web search, has become a standard capability even in freely available large language models (LLMs). However, existing bench...

By Zhengxiang Wang, Zeyu Dong

llms agents benchmarks

arXiv AI

Oct 2

MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation

arXiv:2601.09879v2 Announce Type: replace-cross Abstract: Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report gen...

By Yang Xing, Jiong Wu, Savas Ozdemir, Ying Zhang, Yang Yang, Wei Shao, Kuang Gong

llms rag computer-vision nlp multimodal benchmarks

arXiv AI

Oct 2

InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation

arXiv:2602.20294v2 Announce Type: replace-cross Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approa...

By Yu Li, Pranav Narayanan Venkit, Yada Pruksachatkun, Chien-Sheng Wu

llms rag safety

arXiv AI

Oct 2

Large Reward Models: Generalizable Online Robot Reward Generation with Vision-Language Models

arXiv:2603.16065v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has shown strong potential for improving robotic manipulation policies, yet its practical use remains bottlenecke...

By Yanru Wu, Weiduo Yuan, Esteban Martinez Licon, Ang Qi, Vitor Guizilini, Jiageng Mao, Yue Wang

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Oct 2

High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination

arXiv:2604.02578v2 Announce Type: replace-cross Abstract: Humans exhibit remarkable abilities to coordinate in groups. As large language models (LLMs) become more capable, it remains an open question...

By Sahaj Singh Maini, Robert L. Goldstone, Zoran Tiganj

llms safety

arXiv AI

Oct 2

Verbal tics in frontier language models: A critical review of current releases, research evidence, and public discussion

arXiv:2604.19139v4 Announce Type: replace-cross Abstract: Repeated praise, canned reassurance, familiar contrasts, and conspicuous vocabulary are recurring subjects in discussions of large language m...

By Shuai Wu, Xue Li, Zhijun Wang, Bolun Liu, Weilin Cai, Zihao Su, Ran Wang

llms benchmarks

arXiv AI

Oct 2

What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review

arXiv:2609.33153v2 Announce Type: replace-cross Abstract: Reported improvements from tools and reusable skills in large language model agents refer to different comparisons. This critical narrative r...

By Shuyang Zhang

llms agents benchmarks

arXiv AI

Oct 2

Unifying Video Tasks via Spatiotemporal Analogy

arXiv:2609.33935v2 Announce Type: replace-cross Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free...

By Chia-Hsiang Kao, Belinda Zeng, Bharath Hariharan, Menglin Jia

llms fine-tuning

arXiv AI

Oct 2

Talked Out of the Truth: Sycophancy in the Reasoning Chains of Multimodal Models

arXiv:2608.28623v2 Announce Type: replace-cross Abstract: Large multimodal reasoning models (LMRMs) are increasingly capable, largely through generating explicit chain-of-thought reasoning before ans...

By Mahir Numayeer Islam, Gakuto Okuyama, Nikolaus Siauw, Shivank Garg, Madhur Panwar, Vasu Sharma

llms multimodal benchmarks

arXiv Machine Learning

Oct 2

Role-guided Speaker Deletion Verification in Clinical Psychiatry Speech Recordings with Audio Language Models

arXiv:2609.38491v1 Announce Type: new Abstract: Clinical research in psychiatry increasingly relies on large scale collection of spoken language data to identify acoustic and linguistic biomarkers. Y...

By Joseph T Colonel, Daniel Katzman, Kelsey Kirker, Adam N Davidson, Shalaila S Haas, Cheryl Corcoran, Ren\'{e} S Kahn, Guillermo Checci, Baihan Lin

llms

arXiv Machine Learning

Oct 2

Lasting Effects of Abstract Pretraining Beyond Perplexity

arXiv:2609.38764v1 Announce Type: new Abstract: Language models are typically pretrained from random initialization. Recent work challenges this convention, showing that a brief warm-up on abstract,...

By Zachary Shinnick, Hemanth Saratchandran, Damien Teney, Anton van den Hengel

llms nlp robotics

arXiv Machine Learning

Oct 2

SCORE-LM: State-Space Radar Representations with Language Models for Fault Diagnosis

arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE...

By Mainak Mallick, Seung-Kyum Choi

llms benchmarks

arXiv Machine Learning

Oct 2

SparseEngine: Sparse-First Inference Engine

arXiv:2609.39068v1 Announce Type: new Abstract: Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation. Although sparse attention reduces these...

By Jitai Hao, Quansheng Gu, Qiang Huang, Jun Yu

llms agents efficiency benchmarks

arXiv Machine Learning

Oct 2

The Row Normalization Puzzle in Muon

arXiv:2609.39114v1 Announce Type: new Abstract: This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performanc...

By Jiayu Zhang, Tianyi Lin

llms

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