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

Cybersecurity is the True Frontier for Generative AI Success or Failure

arXiv:2606. 28929v1 Announce Type: cross Abstract: Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language Models (LLMs) to automate processes as ``agents.

By Edward Raff, Maor Ashkenazi, Sagar Samtani, David J. Elkind, Sven Krasser

llms agents computer-vision

arXiv AI

Jun 30

RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation

arXiv:2606. 28410v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding.

By Shanwen Wang, Xin Sun, Sirui Wang, Xiao Xiang Zhu

llms computer-vision benchmarks safety

arXiv AI

Jun 30

Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation

arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).

By Yinjie Cheng, Paul Youssef, Christin Seifert, J\"org Schl\"otterer, Zhixue Zhao

llms fine-tuning benchmarks safety

arXiv AI

Jun 30

An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations

arXiv:2606. 28467v1 Announce Type: cross Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use.

By Dihia Falouz, Aida Douaibia, Amine Bechar, Youssef Elmir, Abbes Amira, Adel Oulefki

llms rag 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 AI

Jun 30

HEARTS: Benchmarking LLM Reasoning on Health Time Series

arXiv:2603. 06638v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning.

By Sirui Li, Shuhan Xiao, Mihir Joshi, Ahmed Metwally, Daniel McDuff, Wei Wang, Yuzhe Yang

llms agents benchmarks

arXiv AI

Jun 30

SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows

arXiv:2606. 29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making.

By Jian Zhu, Yuzheng Zhang, Zeyao Ma, Bohan Zhang, Armin Schoepf, Daniel Woloch, Peter Yiliu Wang, Guangyu Robert Yang, Samuel Jacob, Siddharth Nagisetty, Abhiram Chundru, Jean Lin, Spencer Mateega, Jing Zhang

llms agents benchmarks

arXiv AI

Jun 30

Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification

arXiv:2606. 28409v1 Announce Type: cross Abstract: Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level synthesis (HLS) toolchain -- compilation, C simulation (CSim), synthesis, and C/RTL co-simulation (CoSim) -- because HLS accepts only a synthesizable subset of C (HLS-C).

By Zhe Zhao, Hongbing Lang, Zhihan Xiao, Luke Ztz Hu, John Imoleayo Adebisi, Songping Mai

llms rag agents benchmarks

arXiv AI

Jun 30

Digitizing Coaching Intelligence: An Agentic Framework for Holistic Athlete Profiling using VLM and RAG

arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.

By Deep Ghosal, Ishani Sen, Wazib Ansar, Amlan Chakrabarti

llms rag agents computer-vision multimodal safety data-engineering

arXiv AI

Jun 30

Feature-level Interaction Explanations in Multimodal Transformers

arXiv:2603. 13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision.

By Yeji Kim, Housam Khalifa Bashier Babiker, Mi-Young Kim, Randy Goebel

llms multimodal benchmarks

arXiv AI

Jun 30

Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense

arXiv:2606. 29441v1 Announce Type: cross Abstract: Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists.

By Subhadip Mitra

llms benchmarks safety

arXiv Machine Learning

Jun 30

FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks

arXiv:2606. 30336v1 Announce Type: new Abstract: We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decoders.

By Marek Polewczyk, Maximilian Schambach, Marco Spinaci, Sam Thelin, Johannes H\"ohne

llms rag benchmarks

arXiv AI

Jun 30

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

By Yuxuan Jiang, Francis Ferraro

llms agents reinforcement-learning benchmarks

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 AI

Jun 30

Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection

arXiv:2606. 30587v1 Announce Type: cross Abstract: Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection.

By Asif Shahriar, Hongyu Cai, Hadjer Benkraouda, Gang Wang, Z. Berkay Celik

llms safety

arXiv AI

Jun 30

MCP Server Architecture Patterns for LLM-Integrated Applications

arXiv:2606. 30317v1 Announce Type: cross Abstract: The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services.

By Carson Rodrigues, Oysturn Vas

llms agents

arXiv Machine Learning

Jun 30

Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems

arXiv:2510. 20640v2 Announce Type: replace Abstract: In this paper, we present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft.

By Fiza Husain, Anson Bastos, Anjaly Parayil, Ayush Choure, Chetan Bansal, Rujia Wang, Saravan Rajmohan

llms efficiency benchmarks

arXiv AI

Jun 30

SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance

arXiv:2606. 30113v1 Announce Type: cross Abstract: Discrete action tokenization provides a compact interface for autoregressive VLA policies, but accurately recovering continuous robot actions from discrete codes remains challenging.

By Tengyue Jiang, Chunpu Xu, Jiayue Kang, Yao Mu

llms nlp robotics multimodal

arXiv AI

Jun 30

The Complexity Ceiling Benchmark: A Multi-Domain Evaluation of Sequential Reasoning Under Depth Scaling

arXiv:2606. 29278v1 Announce Type: new Abstract: We introduce the Complexity Ceiling Benchmark (CCB), a controlled evaluation of how language-model reasoning decays as the number of required sequential steps grows.

By Shubh Chapra, Dhruv Kumar, Murari Mandal, Yash Sinha

llms robotics benchmarks

arXiv AI

Jun 30

A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis

arXiv:2606. 29193v1 Announce Type: cross Abstract: LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data.

By Yuanhong Cai, Xiaohui Nie, Kanglin Yin, Changhua Pei, Yongqian Sun, Shenglin Zhang, Haibin Liu, Guiyang Liu, Xidao Wen, Fang Situ, Dan Pei

llms agents multimodal benchmarks

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