Copertina

Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hu, Boyue Caroline, Gopinath, Divya, Pasareanu, Corina S., Narodytska, Nina, Mangal, Ravi, Jha, Susmit
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Concept-based Analysis of Neural Networks via Vision-Language Models
di: Mangal, Ravi, et al.
Pubblicazione: (2024)

Prophecy: Inferring Formal Properties from Neuron Activations
di: Gopinath, Divya, et al.
Pubblicazione: (2025)

Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
di: Koh, Daniel, et al.
Pubblicazione: (2025)

Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
di: Canizales, Ronaldo, et al.
Pubblicazione: (2026)

Agentic AI Software Engineers: Programming with Trust
di: Roychoudhury, Abhik, et al.
Pubblicazione: (2025)

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs
di: Adnan, Muntasir, et al.
Pubblicazione: (2025)

SecCodePRM: A Process Reward Model for Code Security
di: Yu, Weichen, et al.
Pubblicazione: (2026)

Rethinking Diversity in Deep Neural Network Testing
di: Wang, Zi, et al.
Pubblicazione: (2023)

Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
di: Garg, Spandan, et al.
Pubblicazione: (2026)

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models
di: Liu, Jingjing, et al.
Pubblicazione: (2025)

Towards Adaptive Software Agents for Debugging
di: Majdoub, Yacine, et al.
Pubblicazione: (2025)

Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step
di: Zhong, Li, et al.
Pubblicazione: (2024)

Fighting AI with AI: Leveraging Foundation Models for Assuring AI-Enabled Safety-Critical Systems
di: Mavridou, Anastasia, et al.
Pubblicazione: (2025)

DREAM: Debugging and Repairing AutoML Pipelines
di: Zhang, Xiaoyu, et al.
Pubblicazione: (2023)

Verify-Gated Completion as Admission Control in a Governed Multi-Agent Runtime: A Bounded Architecture Case Study
di: Nguyen, Hai-Duong, et al.
Pubblicazione: (2026)

VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction
di: Wang, Ning, et al.
Pubblicazione: (2025)

On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
di: Hannan, Md Abdul, et al.
Pubblicazione: (2025)

RuntimeSlicer: Towards Generalizable Unified Runtime State Representation for Failure Management
di: Zhang, Lingzhe, et al.
Pubblicazione: (2026)

BugSpotter: Automated Generation of Code Debugging Exercises
di: Pădurean, Victor-Alexandru, et al.
Pubblicazione: (2024)

Post-hoc LLM-Supported Debugging of Distributed Processes
di: Schiese, Dennis, et al.
Pubblicazione: (2025)

AgentStepper: Interactive Debugging of Software Development Agents
di: Hutter, Robert, et al.
Pubblicazione: (2026)

DebugBench: Evaluating Debugging Capability of Large Language Models
di: Tian, Runchu, et al.
Pubblicazione: (2024)

Viverra: Text-to-Code with Guarantees
di: Wu, Haoze, et al.
Pubblicazione: (2026)

Revisit Self-Debugging with Self-Generated Tests for Code Generation
di: Chen, Xiancai, et al.
Pubblicazione: (2025)

Large Language Model Guided Self-Debugging Code Generation
di: Adnan, Muntasir, et al.
Pubblicazione: (2025)

Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2
di: Qiu, Yu-Ning, et al.
Pubblicazione: (2026)

LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns
di: Nandal, Deeksha, et al.
Pubblicazione: (2026)

MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
di: Huang, Jinyang, et al.
Pubblicazione: (2025)

Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
di: Zhao, Chenyu, et al.
Pubblicazione: (2026)

Automated Multi-Source Debugging and Natural Language Error Explanation for Dashboard Applications
di: Tata, Devendra, et al.
Pubblicazione: (2026)

DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems
di: Ma, Ming, et al.
Pubblicazione: (2025)

Learning to Debug: LLM-Organized Knowledge Trees for Solving RTL Assertion Failures
di: Bai, Yunsheng, et al.
Pubblicazione: (2025)

AgentGuard: Runtime Verification of AI Agents
di: Koohestani, Roham
Pubblicazione: (2025)

Enhancing Debugging Skills with AI-Powered Assistance: A Real-Time Tool for Debugging Support
di: Artser, Elizaveta, et al.
Pubblicazione: (2026)

CaveAgent: Transforming LLMs into Stateful Runtime Operators
di: Ran, Maohao, et al.
Pubblicazione: (2026)

Runtime-Structured Task Decomposition for Agentic Coding Systems
di: Asthana, Shubhi, et al.
Pubblicazione: (2026)

COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
di: Yang, Weiqing, et al.
Pubblicazione: (2024)

REDO: Execution-Free Runtime Error Detection for COding Agents
di: Li, Shou, et al.
Pubblicazione: (2024)

ProbGuard: Probabilistic Runtime Monitoring for LLM Agent Safety
di: Wang, Haoyu, et al.
Pubblicazione: (2025)

SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
di: Xu, Duling, et al.
Pubblicazione: (2026)