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 30

Position: Evaluation Scores Are Perishable Knowledge Claims

arXiv:2607. 26191v1 Announce Type: cross Abstract: Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and benchmark suite results.

By Sankalp Gilda, Shlok Gilda

llms agents benchmarks

arXiv Machine Learning

Jul 30

Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States

arXiv:2607. 26929v1 Announce Type: cross Abstract: The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or identifying assumptions.

By Weiyi Kong, Zhuoran Li

llms benchmarks

arXiv Machine Learning

Jul 30

Using large language models to probe the limits of atom-centered structural descriptors

arXiv:2607. 26984v1 Announce Type: cross Abstract: Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling.

By Michelangelo Domina, Michele Ceriotti

llms

arXiv Machine Learning

Jul 30

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

arXiv:2607. 27081v1 Announce Type: cross Abstract: Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers can embed harmful behaviors into downstream corpora, creating models that retain professional skills while violating human values on demand.

By Yongjian Guo, Wanlun Ma, Lingyu Shen, Xi Xiao, Sheng Wen

llms fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jul 30

SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval

arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.

By Akshaj Murhekar, Christina Liu, Abhijit Mishra, Shounak Roychowdhury, Jacek Gwizdka

llms rag fine-tuning safety

arXiv Machine Learning

Jul 30

MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis

arXiv:2607. 27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation.

By Yihao Chen, Shi Chang, Khaled Chawa, Feng Lin, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jul 30

ToxScreen: Detecting Whether an LLM Has Been Poisoned

arXiv:2607. 26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time.

By Anthony Hughes, Nicole Xing, Collin Francel, Andy Kim, Andrew Draganov

llms benchmarks

arXiv Machine Learning

Jul 30

A Compositional Theory of Causally Masked Transformers

arXiv:2607. 26988v1 Announce Type: cross Abstract: What types of decision problems can a causally masked, finite-precision transformer solve for inputs of arbitrary length?

By Franz Nowak, Ryan Cotterell, Reda Boumasmoud

llms rag

arXiv Machine Learning

Jul 30

InferScale: GPU-Native KV Injection for Personalized LLM Serving

arXiv:2607. 27090v1 Announce Type: cross Abstract: Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histories, that is shared across a user's many requests.

By Peter Li, Prashant Pandey

llms rag fine-tuning efficiency

arXiv Machine Learning

Jul 30

Interpretable GOHR Agents via Sparse Autoencoders

arXiv:2607. 25132v2 Announce Type: replace Abstract: A central challenge in interpreting learned decision-making systems is to determine whether their internal representations contain concepts that help explain their behavior.

By Shiwei Tan, Yusong Zhao, Weiyi Qin, Wentian Wang, Jacob Feldman, Lazaros K. Gallos, Paul B. Kantor, Vladimir Menkov, Hao Wang

llms rag agents safety

arXiv Machine Learning

Jul 30

Weak-to-Strong On-Policy Distillation

arXiv:2607. 26246v1 Announce Type: new Abstract: On-policy distillation (OPD), which aligns a student with the teacher's token-level distribution on the student's own rollouts, is an effective paradigm for transferring capabilities across LLMs.

By Fangxu Yu, Zinan Lin, Xiaodong Liu, Weijia Xu, Michael Xu, Tianyi Zhou, Jianfeng Gao

llms reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 30

Dynamic Parameterization Is Not Dynamic Inference

arXiv:2607. 26192v1 Announce Type: new Abstract: Input-dependent controller coefficients are often treated as evidence of dynamic inference or computational savings.

By Zongfei Li, Yuan-yih Shang, Guozhong Luo

llms

arXiv Machine Learning

Jul 30

Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation

arXiv:2607. 26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs.

By Ethan J. Mick, Campbell A. Sweet, Matthias J. Young, Derek T. Anderson

llms safety

arXiv Machine Learning

Jul 30

Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents

arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.

By Yicheng Feng, Yan Zhang, Yan Cheng, Wei Qi

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jul 30

Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

arXiv:2607. 27106v1 Announce Type: new Abstract: Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits.

By Filipa Lino, B\'arbara Tavares, Carlos Santiago, Cl\'audia Soares, Manuel Marques

llms

arXiv Machine Learning

Jul 30

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes

arXiv:2607. 27188v1 Announce Type: new Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density.

By Lennon J. Shikhman, Michael Galarnyk, Aadi Dash, Nicholas A. Welsh

llms benchmarks safety

arXiv Machine Learning

Jul 30

GPT-Red: Automated Red Teaming via Self-Play at Scale

arXiv:2607. 26115v1 Announce Type: cross Abstract: We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs.

By Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal, Sam Toyer, Dylan Hunn, Stephanie Lin, Yuxin Wen, Xiangyu Qi, Christopher Wolff, Zizhao Wang, Milad Nasr, Sicheng Zhu, Chuan Guo, Juan Felipe Cer\'on Uribe, Kaiwen Wang, Aiden Low, Kai Xiao, Kai Chen

llms agents reinforcement-learning safety

arXiv Machine Learning

Jul 30

Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning

arXiv:2607. 26358v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy.

By Keegan Harris, Brian W. Lee, Ian Waudby-Smith, Philip Amortila, Nika Haghtalab, Michael I. Jordan

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 30

The Art of Not Forgetting A Local Learning Architecture for Continual Learning

arXiv:2607. 26523v1 Announce Type: new Abstract: We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?

By Ashmith Atmuri, Yashaswini Rao Bhogarajula

llms benchmarks

arXiv Machine Learning

Jul 30

Understanding Context Sampling in TabPFN on Small Tabular Datasets

arXiv:2607. 26628v1 Announce Type: new Abstract: TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates.

By Mohammed Abdullah

llms

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