Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
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Benchmarking LLM Causal Reasoning with Scientifically Validated Relationships

TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics

Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge

Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation

Emotionally Vulnerable Subtype of Internet Gaming Disorder: Measuring and Exploring the Pathology of Problematic Generative AI Use

Explaining raw data complexity to improve satellite onboard processing

Foundations of LLM Knowledge Materialization: Termination, Reproducibility, Robustness

Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation

A Multimodal GUI Architecture for Interfacing with LLM-Based Conversational Assistants

Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models

DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization

High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training

Provable Speech Attributes Conversion via Latent Independence

Chronological Thinking in Full-Duplex Spoken Dialogue Language Models

Paper2Video: Automatic Video Generation from Scientific Papers

MacroBench: A Novel Testbed for Web Automation Scripts via Large Language Models

Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models

Generalized Orders of Magnitude for Scalable, Parallel, High-Dynamic-Range Computation

LogAction: Consistent Cross-system Anomaly Detection through Logs via Active Domain Adaptation

Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models

More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration

Learning to Reason for Hallucination Span Detection

Panorama: Fast-Track Nearest Neighbors

Feature Identification via the Empirical NTK

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

Real-time Noise Detection and Classification in Single-Channel EEG: A Lightweight Machine Learning Approach for EMG, White Noise, and EOG Artifacts

The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes

CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D

PARL-MT: Learning to Call Functions in Multi-Turn Conversation with Progress Awareness

Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment

Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning

InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models

MORPH: Shape-agnostic PDE Foundation Models

Spiffy: Multiplying Diffusion LLM Acceleration via Lossless Speculative Decoding

Evaluating LLM - Generated Versus Human-Authored Responses in Role-Play Dialogues

Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control

ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification

From Correction to Mastery: Reinforced Distillation of Large Language Model Agents

Reproducible workflow for online AI in digital health

HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation

A Survey of Reinforcement Learning for Large Reasoning Models

X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates

Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation

Scaling Performance of Large Language Model Pretraining

Towards Methane Detection Onboard Satellites

AEGIS: Automated Co-Evolutionary Framework for Guarding Prompt Injections Schema

Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning

Safe-Control: A Safety Patch for Mitigating Unsafe Content in Text-to-Image Generation Models

Condition Weaving Meets Expert Modulation: Towards Universal and Controllable Image Generation

Long Chain-of-Thought Reasoning Across Languages

MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debugging

MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols

MInDI-3D: Iterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography

MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling

CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy

Can Small-Scale Data Poisoning Exacerbate Dialect-Linked Biases in Large Language Models?

Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation

From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes

Controllable Hybrid Captioner for Improved Long-form Video Understanding

Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks

Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives

ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations

Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers

Truth, Trust, and Trouble: Medical AI on the Edge

LLMs on a Budget? Say HOLA

The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks

Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients

Rethinking Losses for Diffusion Bridge Samplers

Think With Videos For Agentic Long-Video Understanding

ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning

Intention-Conditioned Flow Occupancy Models

Product of Experts for Visual Generation

Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study

Tug-of-war between idioms' figurative and literal interpretations in LLMs

MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement

GL-PGENet: A Parameterized Generation Framework for Robust Document Image Enhancement

CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation

Trans-EnV: A Framework for Evaluating the Linguistic Robustness of LLMs Against English Varieties

The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

Inference-time Alignment in Continuous Space

STOPA: A Database of Systematic Variation Of DeePfake Audio for Open-Set Source Tracing and Attribution

Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty

Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning

LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression

FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation

Hakim: Farsi Text Embedding Model

Understanding In-context Learning of Addition via Activation Subspaces

Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection

T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning

Hallucination Detection in LLMs with Topological Divergence on Attention Graphs

Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks

Created by

Haebom

Author

Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao, Hou-I Liu, Ya-Hsin Yeh, Yu Kai Chan, Wen-Sheng Lien, Po-Yen Kuo, Philip S. Yu, Hong-Han Shuai

Outline

The increasing number of submissions in the peer review process, essential for maintaining academic quality, is placing an increasing burden on reviewers. Large-Scale Language Models (LLMs) can assist in this process, but their vulnerability to text-based adversarial attacks raises reliability concerns. This paper investigates the robustness of LLMs as automated reviewers exposed to such attacks. Key questions include: (1) how effectively LLMs generate reviews compared to human reviewers; (2) the impact of adversarial attacks on the reliability of LLM-generated reviews; and (3) the challenges of LLM-based reviewing and potential mitigation strategies. Our evaluation revealed a significant vulnerability: text manipulation can distort LLM evaluations. This study provides a comprehensive evaluation of LLM performance in automated peer review and analyzes its robustness against adversarial attacks.

Takeaways, Limitations

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Identifying the potential benefits of an LLM-based automated peer review system and its vulnerability to text manipulation.

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Demonstrating the negative impact of adversarial attacks on the credibility of LLM-generated reviews.

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Highlighting the need for further research and mitigation strategies to improve the robustness of LLM-based review systems.

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Lack of specific attack types and mitigation strategies presented in the paper

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Lack of specific scenarios and considerations for system application in real-world academic environments.

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Limited evaluation of specific LLM models and benchmark data

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