arXiv AI
Jul 31
The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty
arXiv:2607. 26067v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 31
IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations
arXiv:2607. 26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines.
arXiv AI
Jul 31
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
arXiv:2607. 26201v1 Announce Type: cross Abstract: Security operations centers rely on anomaly detection systems to flag suspicious events.
arXiv AI
Jul 31
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
arXiv:2607. 26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
arXiv AI
Jul 31
StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents
arXiv:2607. 26314v1 Announce Type: cross Abstract: Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates sophisticated operators from detectable ones.
arXiv AI
Jul 31
When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
arXiv AI
Jul 31
Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.
arXiv AI
Jul 31
Misalignment Has a Personality: A Big Five Account of Emergent Misalignment
arXiv:2607. 26389v1 Announce Type: cross Abstract: Fine-tuning a language model on data containing a narrow flaw, such as insecure code or incorrect mathematical answers, can cause broad misalignment through a mechanism that remains debated.
arXiv AI
Jul 31
Mergeable Model-Side Aggregation States for Long-Context Language Models
arXiv:2607. 26448v1 Announce Type: cross Abstract: A known limitation of long-context language models is their increasingly unreliable performance in non-additive, set-based aggregation as context length grows.
arXiv AI
Jul 31
A Graph-Native Bitemporal Memory Store for Conversational AI Agents
arXiv:2607. 26520v1 Announce Type: cross Abstract: Conversational AI agents commonly lack persistent memory across sessions.
arXiv AI
Jul 31
WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
arXiv:2607. 26621v2 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs).
arXiv AI
Jul 31
Automated Multilabel Mpox Research Classification with Explainable Transformer Models
arXiv:2607. 26700v1 Announce Type: cross Abstract: The Mpox outbreak remains a serious public health issue, with the WHO (World Health Organization) reporting increasing cases in some regions.
arXiv AI
Jul 31
See2Think: Do Multimodal Models Really Use Intermediate Visual States?
arXiv:2607. 26769v1 Announce Type: cross Abstract: Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these visual states.
arXiv AI
Jul 31
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
arXiv:2607. 26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities.
arXiv AI
Jul 31
Progressive Multimodal Alignment for Continual Instruction Tuning
arXiv:2607. 26947v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cross-modal understanding.
arXiv AI
Jul 31
ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
arXiv:2607. 27065v2 Announce Type: cross Abstract: While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging.
arXiv AI
Jul 31
SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence
arXiv:2607. 27066v1 Announce Type: cross Abstract: Scientific figure assessment in peer review differs fundamentally from general image quality evaluation: a figure must be visually legible, faithfully support the manuscript's claims, and communicate evidence with a clear visual hierarchy.
arXiv AI
Jul 31
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
arXiv:2607. 27084v1 Announce Type: cross Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers.
arXiv AI
Jul 31
MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
arXiv:2607. 27109v2 Announce Type: cross Abstract: With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions.
arXiv AI
Jul 31
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.