Daily Arxiv

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Teaching the Teacher: Improving Neural Network Distillability for Symbolic Regression via Jacobian Regularization

Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models

Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items

SourceSplice: Source Selection for Machine Learning Tasks

OneShield -- the Next Generation of LLM Guardrails

RecPS: Privacy Risk Scoring for Recommender Systems

HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain

Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback

A Segmented Robot Grasping Perception Neural Network for Edge AI

Binarizing Physics-Inspired GNNs for Combinatorial Optimization

Disentangling Neural Disjunctive Normal Form Models

The Second Machine Turn: From Checking Proofs to Creating Concepts

EmissionNet: Air Quality Pollution Forecasting for Agriculture

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

Evaluating LLMs on Real-World Forecasting Against Human Superforecasters

Sign Spotting Disambiguation using Large Language Models

RAG-R1: Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence

MR-CLIP: Efficient Metadata-Guided Learning of MRI Contrast Representations

Curious Causality - Seeking Agents Learn Meta Causal World

Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models

Private GPTs for LLM-driven testing in software development and machine learning

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora

Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

Are Sparse Autoencoders Useful for Java Function Bug Detection?

Credible Plan-Driven RAG Method for Multi-Hop Question Answering

Debunking with Dialogue? Exploring AI-Generated Counterspeech to Challenge Conspiracy Theories

E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking

Dominated Actions in Imperfect-Information Games

FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection

Simultaneous Motion And Noise Estimation with Event Cameras

Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation

Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review

ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning

A Survey on Post-training of Large Language Models

Do Large Language Models Know How Much They Know?

Better Embeddings with Couple Adam

Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks

An Investigation into Value Misalignment in LLM-Generated Texts for Cultural Heritage

Embracing Large Language Models in Traffic Flow Forecasting

A Large Sensor Foundation Model Pretrained on Continuous Glucose Monitor Data for Diabetes Management

FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait

Un-mixing Test-time Adaptation under Heterogeneous Data Streams

PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series

Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics

Cobblestone: Iterative Automation for Formal Verification

Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots

Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors

AttnMod: Attention-Based New Art Styles

Loss Landscape Degeneracy and Stagewise Development in Transformers

Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspective

Gradient Leakage Defense with Key-Lock Module for Federated Learning

Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving

Semantic Chain-of-Trust: Autonomous Trust Orchestration for Collaborator Selection via Hypergraph-Aided Agentic AI

How Far Are AI Scientists from Changing the World?

A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence

EARTH: Structuring Creative Evolution through Model Error in Generative AI

On Gradual Semantics for Assumption-Based Argumentation

Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations

Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team

ORFS-agent: Tool-Using Agents for Chip Design Optimization

World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks

The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation

BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking

OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM

Causal Explanations for Image Classifiers

BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination

Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity

Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence

Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models

SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation

Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations

A Simple and Effective Method for Uncertainty Quantification and OOD Detection

Harnessing the Power of Interleaving and Counterfactual Evaluation for Airbnb Search Ranking

Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos

Agentic large language models improve retrieval-based radiology question answering

Out-of-Context Abduction: LLMs Make Inferences About Procedural Data Leveraging Declarative Facts in Earlier Training Data

How LLMs are Shaping the Future of Virtual Reality

Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning

JSON-Bag: A generic game trajectory representation

NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System

Efficient Solution and Learning of Robust Factored MDPs

D3: Training-Free AI-Generated Video Detection Using Second-Order Features

On-Device Diffusion Transformer Policy for Efficient Robot Manipulation

Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries

Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications

Advancing Quantum Information Science Pre-College Education: The Case for Learning Sciences Collaboration

Backdoor Attacks on Deep Learning Face Detection

Prompting Science Report 3: I'll pay you or I'll kill you -- but will you care?

Composable OS Kernel Architectures for Autonomous Intelligence

LeakSealer: A Semisupervised Defense for LLMs Against Prompt Injection and Leakage Attacks

Wukong Framework for Not Safe For Work Detection in Text-to-Image systems

OmniUnet: A Multimodal Network for Unstructured Terrain Segmentation on Planetary Rovers Using RGB, Depth, and Thermal Imagery

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks

Created by

Haebom

Author

Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu

Outline

In this paper, we present GRAPHIA, a novel approach for generating accurate call graphs of JavaScript programs. Existing static analysis techniques have limitations in generating incomplete or inaccurate call graphs due to the complex language features of JavaScript. GRAPHIA predicts missing call edges by predicting link pairs based on graph neural networks (GNNs) using inaccurate static call information and dynamic execution information. We construct program graphs containing various types of edges and conduct experiments on 50 popular JavaScript libraries. As a result, we show that the correct target function is a top candidate in more than 42% of outstanding call cases and is ranked within the top five candidates in more than 72% of cases, thereby enhancing the efficiency of static analysis. This is the first attempt to apply GNN-based link prediction to the entire program graph.

Takeaways, Limitations

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Takeaways:

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We demonstrate that GNN-based link prediction can be used to improve the recall of JavaScript call graph generation.

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Suggests the possibility of generating more accurate call graphs by leveraging inaccurate static/dynamic information.

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Validation of GRAPHIA's effectiveness through experimental results on large-scale JavaScript libraries.

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May contribute to improving the accuracy of static analysis tools.

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Limitations:

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It is difficult to compare with general machine learning performance evaluations, as the evaluation metric is the proportion of correct target functions included in the top k candidates, rather than the ROC curve.

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Additional generalization performance verification is needed for different JavaScript code styles and complexities.

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The training and inference processes of GNN models can be computationally expensive.

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