Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
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(R)evolution of Programming: Vibe Coding as a Post-Coding Paradigm

Y-shaped Generative Flows

Trustworthy Retrosynthesis: Eliminating Hallucinations with a Diverse Ensemble of Reaction Scorers

The Algorithmic Regulator

Ctrl-World: A Controllable Generative World Model for Robot Manipulation

SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning

H1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning

SafeGuider: Robust and Practical Content Safety Control for Text-to-Image Models

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model

Detecting Distillation Data from Reasoning Models

Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning

SAGE-Music: Low-Latency Symbolic Music Generation via Attribute-Specialized Key-Value Head Sharing

On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

Learning Inter-Atomic Potentials without Explicit Equivariance

Towards A Universally Transferable Acceleration Method for Density Functional Theory

Functional Critic Modeling for Provably Convergent Off-Policy Actor-Critic

Variational Reasoning for Language Models

Learning Equivariant Functions via Quadratic Forms

Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning

Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry

LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition

Self-Evolving LLMs via Continual Instruction Tuning

Can an Individual Manipulate the Collective Decisions of Multi-Agents?

CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction

Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection

Visible Yet Unreadable: A Systematic Blind Spot of Vision Language Models Across Writing Systems

Towards Methane Detection Onboard Satellites

EO-1: Interleaved Vision-Text-Action Pretraining for General Robot Control

GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs

Reliable generation of isomorphic physics problems using Generative AI with prompt-chaining and tool use

Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models

Geometry-Aware Global Feature Aggregation for Real-Time Indirect Illumination

Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approaches

Your AI, Not Your View: The Bias of LLMs in Investment Analysis

DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning

Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations

Early Signs of Steganographic Capabilities in Frontier LLMs

Orthogonal Finetuning Made Scalable

LLM Probability Concentration: How Alignment Shrinks the Generative Horizon

A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders

Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles

PAL: Probing Audio Encoders via LLMs - Audio Information Transfer into LLMs

Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information

Superior Molecular Representations from Intermediate Encoder Layers

FLEX: A Largescale Multimodal, Multiview Dataset for Learning Structured Representations for Fitness Action Quality Assessment

The quest for the GRAph Level autoEncoder (GRALE)

Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding

Multi-Scale Probabilistic Generation Theory: A Unified Information-Theoretic Framework for Hierarchical Structure in Large Language Models

ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models

R$^2$ec: Towards Large Recommender Models with Reasoning

Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning

Flattening Hierarchies with Policy Bootstrapping

FineScope: Precision Pruning for Domain-Specialized Large Language Models Using SAE-Guided Self-Data Cultivation

MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance

Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models

TMT: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

A Personalized Data-Driven Generative Model of Human Repetitive Motion

Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations

Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model

Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process

BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery

CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing

SoundnessBench: A Soundness Benchmark for Neural Network Verifiers

Semantically Guided Action Anticipation

On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse

Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation

A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom

Optimal Quantization for Matrix Multiplication

Temporal-Difference Variational Continual Learning

Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments

Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning

Extreme Compression of Adaptive Neural Images

A Comprehensive Survey on Data Augmentation

Do LLM Agents Have Regrets? A Case Study in Online Learning and Games

MULTI: Multimodal Understanding Leaderboard with Text and Images

Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs

HardcoreLogic: Challenging Large Reasoning Models with Long-tail Logic Puzzle Games

Tensor Logic: The Language of AI

Do Large Language Models Respect Contracts? Evaluating and Enforcing Contract-Adherence in Code Generation

Benchmarking is Broken -- Don't Let AI be its Own Judge

A Tale of LLMs and Induced Small Proxies: Scalable Agents for Knowledge Mining

Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline

LLM/Agent-as-Data-Analyst: A Survey

SafeSearch: Automated Red-Teaming for the Safety of LLM-Based Search Agents

Coordination Requires Simplification: Thermodynamic Bounds on Multi-Objective Compromise in Natural and Artificial Intelligence

FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games

HealthProcessAI: A Technical Framework and Proof-of-Concept for LLM-Enhanced Healthcare Process Mining

TASER: Table Agents for Schema-guided Extraction and Recommendation

SoundnessBench: A Soundness Benchmark for Neural Network Verifiers

Created by

Haebom

Author

Xingjian Zhou, Keyi Shen, Andy Xu, Hongji Xu, Cho-Jui Hsieh, Huan Zhang, Zhouxing Shi

Outline

This paper develops "SoundnessBench," a novel benchmark for testing the soundness of neural network (NN) validation. This benchmark focuses on evaluating the soundness of a validator in challenging cases, such as instances containing deliberately hidden counterexamples that are difficult to find and that are difficult for existing validators to handle. SoundnessBench encompasses a variety of model architectures, activation functions, and input data, and successfully identifies bugs in state-of-the-art NN validators.

Takeaways, Limitations

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Takeaways:

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We propose a new benchmark to evaluate the soundness of NN verifiers, addressing the limitations of existing benchmarks.

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We develop a training method that generates instances containing hidden counterexamples to effectively identify errors in the verifier.

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SoundnessBench supports a wide range of validation scenarios, including a variety of model architectures, activation functions, and input data.

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We successfully identify bugs in state-of-the-art NN verifiers, demonstrating their practical applicability.

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Limitations:

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The paper may lack detailed descriptions of how to create and characterize specific benchmark instances.

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Validation is needed to determine whether SoundnessBench can catch all types of NN validation errors.

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It is possible that the way hidden counterexamples are generated is biased towards certain types of neural networks.

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