arXiv AI
Jun 3
Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questions
arXiv:2606. 03331v1 Announce Type: cross Abstract: Consumer device repair is an important but underexplored testbed for large language models (LLMs).
Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questions
Consumer device repair is an important but underexplored testbed for large language models (LLMs). Repair tasks require reasoning over incomplete problem descriptions, hardware-specific diagnostics, actionable troubleshooting, and safety-critical decisions, where incorrect advice can cause device damage, battery hazards, or permanent data loss.
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arXiv AI
Jun 16
UXBench: Measuring the Actionability of LLM-Generated UX Critiques
arXiv:2606. 16262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as UX judges that inspect interfaces, diagnose usability problems, and propose repairs.
arXiv AI
23h ago
LLM-Only PDDL Domain Repair with Open-Weight Models
arXiv:2608. 17341v1 Announce Type: new Abstract: AI planning is concerned with finding a sequence of actions that achieves a specified goal.
arXiv AI
Jun 30
Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
arXiv:2606. 29377v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile.
arXiv AI
Jun 30
Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models
arXiv:2601. 05366v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls.
arXiv AI
Jul 29
How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
arXiv:2607. 25873v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent.
arXiv AI
Jul 21
Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents
arXiv:2607. 17641v1 Announce Type: new Abstract: Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use.
Hugging Face Trending Papers
Jul 20
Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents
Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance keeps rising while true validity falls, so existing methods lack a principled basis for deciding when repair should stop.
arXiv AI
Jun 15
FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories
arXiv:2606. 14119v1 Announce Type: new Abstract: Fault diagnostics and recovery in smart factories is challenging because critical information is dispersed across manuals of multiple machines which are interconnected through the manufacturing process.
arXiv Machine Learning
Jun 5
Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation
arXiv:2606. 05792v1 Announce Type: cross Abstract: TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language still requires time and expertise, which limits adoption.
arXiv AI
2d ago
Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency
arXiv:2608. 14065v1 Announce Type: cross Abstract: Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques.
arXiv AI
Jun 12
Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programming
arXiv:2606. 12864v1 Announce Type: cross Abstract: Despite strong performance in competitive programming, the role of Large Language Models (LLMs) in supporting human learning in the same setting remains largely unexplored.