Event-B Agent is an LLM agent that synthesizes, refines, and repairs Event-B formal models from natural language requirements via iterative verification feedback loops.
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MuMuTestUp is a mutation-guided multi-agent framework for updating test cases in evolving software that strengthens assertions via surviving mutants, targets specific coverage gaps, and uses semantic search instead of exact matching.
ImproBR combines a hybrid detector with GPT-4o mini and RAG to raise bug report structural completeness from 7.9% to 96.4% and executable steps from 28.8% to 67.6% on 139 Mojira reports.
LLM agents resolve fewer than half of issues while satisfying design constraints despite passing tests, as shown by a benchmark of 495 issues and 1787 constraints from six repositories.
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
PerfOrch is a four-agent multi-LLM system that uses offline profiling to build language-and-category rankings for routing tasks, achieving 97.19% and 95.83% pass@1 on HumanEval-X and EffiBench-X with generalization across benchmarks.
Only 0.4% of 1,000 Android apps show consistent alignment between their privacy policies and actual log contents, while 67.6% leak sensitive information not mentioned in policies.
Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.
citing papers explorer
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Event-B Agent: Towards LLM Agent for Formal Model Synthesis and Repair
Event-B Agent is an LLM agent that synthesizes, refines, and repairs Event-B formal models from natural language requirements via iterative verification feedback loops.
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MuMuTestUp: Mutation-based Multi-Agent Test Case Update
MuMuTestUp is a mutation-guided multi-agent framework for updating test cases in evolving software that strengthens assertions via surviving mutants, targets specific coverage gaps, and uses semantic search instead of exact matching.
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ImproBR: Bug Report Improver Using LLMs
ImproBR combines a hybrid detector with GPT-4o mini and RAG to raise bug report structural completeness from 7.9% to 96.4% and executable steps from 28.8% to 67.6% on 139 Mojira reports.
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Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution
LLM agents resolve fewer than half of issues while satisfying design constraints despite passing tests, as shown by a benchmark of 495 issues and 1787 constraints from six repositories.
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Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
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Multi-LLM Orchestration for High-Quality Code Generation: Exploiting Complementary Model Strengths
PerfOrch is a four-agent multi-LLM system that uses offline profiling to build language-and-category rankings for routing tasks, achieving 97.19% and 95.83% pass@1 on HumanEval-X and EffiBench-X with generalization across benchmarks.
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Do Privacy Policies Match with the Logs? An Empirical Study of Privacy Disclosure in Android Application Logs
Only 0.4% of 1,000 Android apps show consistent alignment between their privacy policies and actual log contents, while 67.6% leak sensitive information not mentioned in policies.
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A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflection
Vulnsage, a multi-agent framework, generates 34.64% more exploits than prior tools and verified 146 zero-day vulnerabilities in real-world open-source libraries.