Search: “gpt”

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs

arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quanti

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Model Releases

EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context

DGX agent

arXiv:2604.08556v1 Announce Type: cross Abstract: What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent

model-releasesarxiv-cs-ai

13 Apr 2026

Facet-Level Tracing of Evidence Uncertainty and Hallucination in RAG

arXiv:2604.09174v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common eve

Kill-Chain Canaries: Stage-Level Tracking of Prompt Injection Across Attack Surfaces and Model Safety Tiers

arXiv:2603.28013v3 Announce Type: replace-cross Abstract: Multi-agent LLM systems are entering production -- processing documents, managing workflows, acting on behalf of users -- yet their resilience

Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation

arXiv:2604.08797v1 Announce Type: cross Abstract: Stories are key to transmitting values across cultures, but their interpretation varies across linguistic and cultural contexts. Thus, we introduce mu

Tutorials

Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym

arXiv:2604.09338v1 Announce Type: new Abstract: Spatial reasoning is central to navigation and robotics, yet measuring model capabilities on these tasks remains difficult. Existing benchmarks evaluate

tutorialsarxiv-cs-ai

Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory

arXiv:2602.15313v2 Announce Type: replace Abstract: AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet e

NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression

arXiv:2604.08923v1 Announce Type: new Abstract: Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regre

Safety

Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines

arXiv:2604.08608v1 Announce Type: cross Abstract: We introduce Semantic Intent Fragmentation (SIF), an attack class against LLM orchestration systems where a single, legitimately phrased request cause

safetyarxiv-cs-ai

Sentiment Classification of Gaza War Headlines: A Comparative Analysis of Large Language Models and Arabic Fine-Tuned BERT Models

arXiv:2604.08566v1 Announce Type: new Abstract: This study examines how different artificial intelligence architectures interpret sentiment in conflict-related media discourse, using the 2023 Gaza War

SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation

arXiv:2604.09212v1 Announce Type: new Abstract: Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling, where reliability depends on preservin

STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle

VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering

arXiv:2604.08549v1 Announce Type: cross Abstract: We introduce VerifAI, an open-source expert system for biomedical question answering that integrates retrieval-augmented generation (RAG) with a novel

A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection

arXiv:2604.08381v1 Announce Type: new Abstract: Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony,

researcharxiv-cs-cl

Action Without Interaction: Probing the Physical Foundations of Video LMMs via Contact-Release Detection

model-releasesarxiv-cs-cv

arXiv:2511.20162v2 Announce Type: replace Abstract: Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given

AdaBoost Does Not Always Cycle: A Computer-Assisted Counterexample

model-releasesarxiv-cs-lg

arXiv:2604.07055v1 Announce Type: new Abstract: We give a computer-assisted counterexample to the open question, posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost a

Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest

arXiv:2604.08525v1 Announce Type: cross Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginni

An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications

arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder

Before We Trust Them: Decision-Making Failures in Navigation of Foundation Models

arXiv:2601.05529v5 Announce Type: replace Abstract: High success rates on navigation-related tasks do not necessarily translate into reliable decision making by foundation models. To examine this gap,

Blind Refusal: Language Models Refuse to Help Users Evade Unjust, Absurd, and Illegitimate Rules

arXiv:2604.06233v1 Announce Type: new Abstract: Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help eva

Break Me If You Can: Self-Jailbreaking of Aligned LLMs via Lexical Insertion Prompting

arXiv:2601.02670v2 Announce Type: replace Abstract: We introduce self-jailbreaking, a threat model in which an aligned LLM guides its own compromise. Unlike most jailbreak techniques, which oft

Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code

arXiv:2604.05292v2 Announce Type: replace-cross Abstract: AI coding assistants are now used to generate production code in security-sensitive domains, yet the exploitability of their outputs remains u

Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research

arXiv:2602.15889v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used in research as both tools and objects of study. Much of this work assumes that LLM performa

researcharxiv-cs-ai

Distributed Multi-Layer Editing for Rule-Level Knowledge in Large Language Models

arXiv:2604.08284v1 Announce Type: new Abstract: Large language models store not only isolated facts but also rules that support reasoning across symbolic expressions, natural language explanations, an

Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation

arXiv:2602.11635v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical sp

DosimeTron: Automating Personalized Monte Carlo Radiation Dosimetry in PET/CT with Agentic AI

arXiv:2604.06280v1 Announce Type: cross Abstract: Purpose: To develop and evaluate DosimeTron, an agentic AI system for automated patient-specific MC internal radiation dosimetry in PET/CT examination

Draw-In-Mind: Rebalancing Designer-Painter Roles in Unified Multimodal Models Benefits Image Editing

arXiv:2509.01986v4 Announce Type: replace-cross Abstract: In recent years, integrating multimodal understanding and generation into a single unified model has emerged as a promising paradigm. While th

Dynamic Context Evolution for Scalable Synthetic Data Generation

arXiv:2604.07147v1 Announce Type: cross Abstract: Large language models produce repetitive output when prompted independently across many batches, a phenomenon we term cross-batch mode collapse: the p

Efficient Provably Secure Linguistic Steganography via Range Coding

arXiv:2604.08052v1 Announce Type: new Abstract: Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is

researcharxiv-cs-cl

FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts

arXiv:2604.06403v1 Announce Type: cross Abstract: The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHab

Hallucination as output-boundary misclassification: a composite abstention architecture for language models

arXiv:2604.06195v1 Announce Type: cross Abstract: Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally generated

researcharxiv-cs-ai

HiCI: Hierarchical Construction-Integration for Long-Context Attention

arXiv:2603.20843v2 Announce Type: replace Abstract: Long-context language modeling is commonly framed as a scalability challenge of token-level attention, yet local-to-global information structuring r

Safety

How Independent are Large Language Models? A Statistical Framework for Auditing Behavioral Entanglement and Reweighting Verifier Ensembles

arXiv:2604.07650v1 Announce Type: cross Abstract: The rapid growth of the large language model (LLM) ecosystem raises a critical question: are seemingly diverse models truly independent? Shared pretra

safetyarxiv-cs-cl

Safety

Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents

arXiv:2510.07809v4 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) empower autonomous mobile agents, yet their security under realistic mobile deployment constraints remain

safetyarxiv-cs-ai

KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance

arXiv:2510.05524v2 Announce Type: replace Abstract: We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in s

Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

arXiv:2511.10354v1 Announce Type: cross Abstract: Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse in

Applications

LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs

arXiv:2604.08126v1 Announce Type: new Abstract: Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students' clinical and communication skills through str

applicationsarxiv-cs-cl

LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces

arXiv:2604.06188v1 Announce Type: cross Abstract: People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such

Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation

model-releasesarxiv-cs-cv

arXiv:2604.06950v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a critic

Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework

model-releasesarxiv-cs-cv

arXiv:2509.23322v2 Announce Type: replace Abstract: With the continuous expansion of Large Language Models (LLMs) and advances in reinforcement learning, LLMs have demonstrated exceptional reasoning c

Agents

MolmoWeb: Open Visual Web Agent and Open Data for the Open Web

arXiv:2604.08516v1 Announce Type: new Abstract: Web agents--autonomous systems that navigate and execute tasks on the web on behalf of users--have the potential to transform how people interact with t

agentsarxiv-cs-cv

Multi-modal user interface control detection using cross-attention

arXiv:2604.06934v1 Announce Type: cross Abstract: Detecting user interface (UI) controls from software screenshots is a critical task for automated testing, accessibility, and software analytics, yet

researcharxiv-cs-ai

On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning

model-releasesarxiv-cs-ro

arXiv:2604.07944v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction a

Rethinking Data Mixing from the Perspective of Large Language Models

arXiv:2604.07963v1 Announce Type: new Abstract: Data mixing strategy is essential for large language model (LLM) training. Empirical evidence shows that inappropriate strategies can significantly redu

researcharxiv-cs-cl

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

arXiv:2506.01062v4 Announce Type: replace Abstract: We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields con

Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

arXiv:2604.07054v2 Announce Type: replace Abstract: Sales dialogues require multi-turn, goal-directed persuasion under asymmetric incentives, which makes them a challenging setting for large language

SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems

arXiv:2604.06811v1 Announce Type: cross Abstract: Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamin

Small Vision-Language Models are Smart Compressors for Long Video Understanding

arXiv:2604.08120v1 Announce Type: cross Abstract: Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets