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

Jul 31

Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents

arXiv:2607. 27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless.

By Jiwon Jang, Kisu Yang, Heuiseok Lim, Hyunwoo Park

llms agents efficiency benchmarks safety

arXiv Machine Learning

Jul 31

Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models

arXiv:2607. 27304v1 Announce Type: new Abstract: Medical vision-language models (VLMs) generate chain-of-thought (CoT) reasoning before answering clinical questions, but whether this reasoning causally influences predictions remains unclear.

By Supratik Bhowal, Subhrajyoti Basu, Aritra Gir Mahanta, Anik Pal Chowdhury

llms multimodal

arXiv Machine Learning

Jul 31

Looped Transformers with Source-Centered State Evolution

arXiv:2607. 27656v1 Announce Type: new Abstract: Looped Transformers create a useful train- and test-time compute axis by reusing the same Transformer block over recurrent depth, increasing effective depth at a fixed parameter count.

By Bum Jun Kim, Kohei Hayashi, Shunsuke Kamiya, Masanori Koyama, Yusuke Iwasawa, Yutaka Matsuo

llms benchmarks safety

arXiv Machine Learning

Jul 31

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

arXiv:2607. 27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions.

By Ka\u{g}an Akman, Naci Saldi, Serdar Y\"uksel

llms efficiency

arXiv Machine Learning

Jul 31

Flux-OPD: On-Policy Distillation with Evolving Contexts

arXiv:2607. 28022v1 Announce Type: new Abstract: Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision.

By Yuran Wang, Zekun Wang, Bohan Zeng, Ruixu Zhang, Wenxuan Liu, Liu Yang, Yifan Dai, Yang Shi, Bozhou Li, Chengzhuo Tong, Daili Hua, Yuanxing Zhang, Wentao Zhang

llms efficiency

arXiv Machine Learning

Jul 31

Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups

arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.

By Haran Shani-Narkiss, Michael Fire, Oren Tsur

llms safety

arXiv Machine Learning

Jul 31

Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models

arXiv:2607. 27421v1 Announce Type: cross Abstract: Intent classification is a core component of task-oriented dialogue systems, yet practitioners have limited systematic guidance for selecting deployable open-weight language models under compute, latency, and robustness constraints.

By Parishruthi Ganesh, Gerry Dozier, Cheryl Seals

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 31

HealthCAT: An Interpretable Encoder-only Transformer Framework for Health Indicator Prediction and Temporal Interpretation of Wearable Sensor Data

arXiv:2607. 27635v1 Announce Type: cross Abstract: Wearable sensors continuously capture fine-grained multivariate time-series data, providing opportunities to model behavioural patterns associated with health outcomes.

By Xiaotong Yu, Joshua Y. Kim, HaeJin Lee, Kalina Yacef

llms safety

arXiv Machine Learning

Jul 31

Albilich: Steerable Proof-State Orchestration for LLM-Based Mathematical Research with CAS Integration

arXiv:2607. 27705v1 Announce Type: cross Abstract: Large language models can contribute useful ideas to mathematical research, yet long-horizon proof attempts remain difficult to coordinate, evaluate, and reproduce.

By Ting Gong, Michael Ruofan Zeng, Yong Yang

llms agents benchmarks

arXiv Machine Learning

Jul 31

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

arXiv:2607. 27766v1 Announce Type: cross Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference.

By Xinyu Luo, Hui Liu, Yihua Shao, Junyi Yang, Arindam Basu, Haoliang Li

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 31

Uncertainty quantification for trustworthy deep learning: Methods and measures

arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.

By H. Martin Gillis, Thomas Trappenberg

llms safety

arXiv Machine Learning

Jul 31

Learning to Trace Seiberg Dualities

arXiv:2607. 28628v1 Announce Type: cross Abstract: Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems.

By Jonathan J. Heckman, Shani Meynet, Alessandro Mininno, Gary Shiu

llms benchmarks

arXiv Machine Learning

Jul 31

Efficient LLMs with AMP: Attention Heads and MLP Pruning

arXiv:2504. 21174v2 Announce Type: replace Abstract: Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems.

By Leandro Giusti Mugnaini, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara, Victor Zacarias, Edson Bollis, Lucas Pellicer, Anna Helena Reali Costa, Artur Jordao

llms efficiency benchmarks

arXiv Machine Learning

Jul 31

Critical attention scaling in long-context transformers

arXiv:2510. 05554v2 Announce Type: replace Abstract: As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity as context length $n$ increases, causing tokens to cluster excessively, a phenomenon known as rank-collapse.

By Shi Chen, Zhengjiang Lin, Yury Polyanskiy, Philippe Rigollet

llms

arXiv Machine Learning

Jul 31

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

arXiv:2410. 06458v2 Announce Type: replace-cross Abstract: Instruction following is a key capability for LLMs.

By Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng

llms benchmarks

arXiv AI

Jul 31

MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning

arXiv:2607. 26465v1 Announce Type: new Abstract: Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied.

By Kawai Chung, Chunkit Chan, Yauwai Yim, Yuxuan Liu, Haochen Shi, Weiqi Wang, Qing Zong, Tianshi Zheng, Yixuan Fu, Kai Chung Wong, Hao Liang, Yifan Gao, Xi Yang, Janet Hui-wen Hsiao, Yangqiu Song

llms multimodal benchmarks

arXiv AI

Jul 31

Aligning LLM-Simulated and Human Examinees for Psychometric Calibration: A Cognitive Diagnostic Profiling Approach

arXiv:2607. 26317v1 Announce Type: cross Abstract: Psychometric calibration for educational tests typically requires costly human response data.

By Wenjie Zhou, Yunting Liu, Renjiao Tang, Mark Wilson

llms safety

arXiv AI

Jul 31

ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science

arXiv:2607. 26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured-table reasoning, or generic scientific repositories.

By Yuan Zhu, Ethan B. Liu, Frank Nie, Jindong Han

llms agents nlp multimodal benchmarks

arXiv AI

Jul 31

A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models

arXiv:2607. 26102v1 Announce Type: cross Abstract: Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference.

By Vivek Shukla, Varun Shukla, Atul, Divya Mishra, Mehul Kumar Das

llms

arXiv AI

Jul 31

ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models

arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.

By Ruxi Gu, Zhenliang Zhang, Wei Wang

llms benchmarks

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