ADI equips AI debugging agents with function-level interaction via a new execution trace structure, raising SWE-bench Verified resolution to 63.8% at $1.28 per task and delivering 6-18% gains when added to existing agents.
Sk2decompile: Llm-based two-phase binary decompilation from skele- ton to skin
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.SE 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A constraint-guided multi-agent system turns raw decompiler output into re-executable code at 84-97% success rates, outperforming prior LLM decompilation methods on real binaries.
CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.
citing papers explorer
-
Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis
ADI equips AI debugging agents with function-level interaction via a new execution trace structure, raising SWE-bench Verified resolution to 63.8% at $1.28 per task and delivering 6-18% gains when added to existing agents.
-
Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery
A constraint-guided multi-agent system turns raw decompiler output into re-executable code at 84-97% success rates, outperforming prior LLM decompilation methods on real binaries.
-
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference
CoDe-R refines LLM decompiler output via rationale-guided semantic injection and dynamic fallback inference, making a 1.3B model the first to exceed 50% average re-executability on HumanEval-Decompile.