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

Jun 30

Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS

arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.

By Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge, Brook Byrns, Heather L. Wilson, Sureesh Tikoo, Steven Rayan, Gordon Broderick

llms

arXiv Machine Learning

Jun 30

RadarTwin: Scene-Specific mmWave Radar Simulation and Learning for Mobile Indoor Perception

arXiv:2606. 28396v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects, environments, and sensing trajectories.

By Emily Bejerano, Federico Tondolo, Devang Gupta, Aaron Mano Cherian, Taeyoo Kim, Ayaan Qayyum, Xiaofan Yu, Xiaofan Jiang

llms robotics multimodal

arXiv Machine Learning

Jun 30

One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

arXiv:2606. 30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bubbles, wasting computational resources.

By Philip Zmushko, Egor Petrov, Nursultan Abdullaev, Mikhail Khrushchev, Samuel Horv\'ath

llms efficiency

arXiv Machine Learning

Jun 30

SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions

arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.

By Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal, Yunzhong He

llms agents benchmarks

arXiv Machine Learning

Jun 30

HSAP: A Hierachical Sequence-aware Parallelism for Hybrid-Context Generative Models

arXiv:2606. 30460v1 Announce Type: new Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly compute causal attention on the hybrid-context packed sequences, in a stronger sequence parallelism framework.

By Songxin Zhang, Zejian Xie, Zhuoyang Song, Cong lin, Junyu Lu, Jiaxing Zhang, Bingyi Jing

llms fine-tuning

arXiv Machine Learning

Jun 30

From Failure Taxonomy to Intervention: A Diagnostic Methodology for Industry-Scale AVLM in Video and Live-Streaming Platform Moderation

arXiv:2606. 30059v1 Announce Type: new Abstract: Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external APIs, including adaptation to platform-specific data distributions, policy-specific objectives, and product-level safety constraints.

By Shuchang Ye, Jinqiang Yu, Zhujun Xiao, Yajing Kong, Yist Y. Lin, Yang Ma, Jiaxi Liu, Xiaolei Xu, Zheng Yu

llms multimodal benchmarks safety

arXiv AI

Jun 30

Can LLMs Rank? A Tale of Triads and Triage

arXiv:2606. 30412v1 Announce Type: cross Abstract: From housing allocation for households experiencing homelessness to triage in emergency departments, LLMs are increasingly being considered as judges of consequential decisions that require ranking people for scarce resources.

By Gaurab Pokharel, Shafkat Farabi, Patrick J. Fowler, Sanmay Das

llms

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

arXiv Machine Learning

Jun 30

Theory of Continual Learning Against Data Poisoning Attacks

arXiv:2606. 29841v1 Announce Type: new Abstract: Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being adopted across key fields such as large language models and image recognition, yet it remains highly vulnerable to data poisoning that triggers learning divergence or severe excess risk.

By Yiting Hu, Lingjie Duan

llms computer-vision safety

arXiv AI

Jun 30

Selective Memory Retention for Long-Horizon LLM Agents

arXiv:2606. 29178v1 Announce Type: new Abstract: When does retention matter for memory-augmented LLM agents?

By Pranath Reddy

llms rag agents benchmarks

arXiv AI

Jun 30

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

arXiv:2606. 29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces.

By Xiaohao Xu, Feng Xue, Xiang Li, Haowei Li, Shusheng Yang, Tianyi Zhang, Matthew Johnson-Roberson, Xiaonan Huang

llms computer-vision benchmarks

arXiv Machine Learning

Jun 30

STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.

By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Jun 30

PolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM Agents

arXiv:2606. 29225v1 Announce Type: new Abstract: LLM agents handle user requests on behalf of organizations through tool calls and must follow the company policies stated in their system prompts.

By Seongjae Kang, Taehyung Yu, Sung Ju Hwang

llms agents

arXiv Machine Learning

Jun 30

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605. 26343v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to explain a model's behaviour by finding its circuit: the sparse subgraph of the model's computation that is causally responsible for it.

By Barsat Khadka

llms agents reinforcement-learning safety

arXiv Machine Learning

Jun 30

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

arXiv:2606. 29161v1 Announce Type: new Abstract: Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines.

By Rui-Xi Wang, Runzhong Wang, Connor W. Coley

llms computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 30

How Token Influence Decays with Distance: A Green-Function View of Trained Language Models

arXiv:2606. 29139v1 Announce Type: new Abstract: We study how the next-token prediction of an autoregressive Transformer language model changes under small perturbations of earlier input token embeddings.

By Matthias Br\"andel, Stephan K\"ohler, Oliver Rheinbach

llms rag

arXiv Machine Learning

Jun 30

Inexact calculus of variations on the hyperspherical tangent bundle with connections to the attention mechanism

arXiv:2507. 15431v4 Announce Type: replace Abstract: We offer a theoretical mathematical background through Lagrangian optimization on the unit hyperspherical manifold and its tangential structure.

By Andrew Gracyk

llms

arXiv Machine Learning

Jun 30

The Contagion Tensor: A Framework for Measuring Output-Distribution Coupling in Multi-Agent LLM Systems -- and Auditing the Claims It Enables

arXiv:2606. 28839v1 Announce Type: new Abstract: We introduce the Contagion Tensor, a measurement framework for quantifying how large language model (LLM) output distributions couple across modalities, agents, and time steps.

By Zewen Liu

llms agents benchmarks

arXiv AI

Jun 30

How to Train Your Long-Context Visual Document Model

arXiv:2602. 15257v3 Announce Type: replace-cross Abstract: We present the first comprehensive, large-scale study of training long-context vision language models up to 344K context, targeting long-document visual question answering with measured transfer to long-context text.

By Austin Veselka

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 30

Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair

arXiv:2606. 28620v1 Announce Type: cross Abstract: Fayyazi et al.

By Oscar Mir\'o L\'opez-Feliu, Daimy van Loo, Xanthos Kekkos, Mikel Blom, Clara Rus

llms efficiency safety

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