Compiler-Guided Program Synthesis

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Echo: Learning-based Matching Decompilation using Trusted Back Translation
Sep 16, 2026Jun Bi, Xiangxin Fang, Aarsh Chaube +3Software Reverse Engineering Program Synthesis
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Edinburgh
Agentic Synthesis against Counterexample-Supplemented Sketches
Jul 17, 2026Muness Castle, Eric RubeckCoding Agents LLM-Based Program Synthesis
Independent
Knowledge-Centric Agents for Workflow Generation in ComfyUI
Jul 17, 2026Zhendong Li, Lei Sun, Ruibo Ming +4Agentic Workflows LLM-Based Program Synthesis
INSAIT, Sofia University “St. Kliment Ohridski” · Adobe Research
Minimalist Genetic Programming
Jun 8, 2026Leonardo TrujilloGenetic Programming Program Synthesis
Tecnol´ogico Nacional de M´exico/IT de Tijuana,Tijuana, BC, Me xico · LASIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming
May 23, 2026Peisong Wang, Bowen Liu, Zehua Li +4Code Generation LLM-Based Program Synthesis
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems
May 22, 2026Shubham Agarwal, Alexander Krentsel, Shu Liu +10AI Coding Agents Program Synthesis
1UC Berkeley · 2Google · 3UC Santa Cruz
Property-Guided LLM Program Synthesis for Planning
May 15, 2026André G. Pereira, Augusto B. Corrêa, Jendrik SeippSymbolic Planning Counterexample-Guided Refinement
Federal University of Rio Grande do Sul · Brazil · University of Oxford +3
Decaf: Improving Neural Decompilation with Automatic Feedback and Search
May 12, 2026Alexander Shypula, Osbert Bastani, Edward SchwartzSoftware Reverse Engineering Compiler-Guided Program Synthesis
University of Pennsylvania Philadelphia, Pennsylvania 19104 · Carnegie Mellon University Pittsburgh, Pennsylvania 15213

Sep 16, 2026, cs.CRJ/K move · Enter open · S save

Echo: Learning-based Matching Decompilation using Trusted Back Translation

Jun Bi, Xiangxin Fang, Aarsh Chaube, José Wesley De Souza Magalhães+2

State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences · University of Edinburgh

Software Reverse Engineering Program Synthesis Compiler-Guided Program Synthesis

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Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompilation addresses this problem by searching for source code whose recompiled assembly exactly matches the target, providing stronger evidence of correctness. However, exact matching remains challenging for optimized binaries under unknown compilation configurations. We present Echo, a matching decompilation system based on trusted back-translation. Our key insight is to use compilation not only for verification, but also as trusted feedback to guide iterative search. Echo first uses a domain-specific model to generate candidate programs and compilation configurations. It recompiles these candidates, measures assembly-level similarity, and synthesizes promising code-configuration pairs. Remaining mismatches are then progressively repaired using rule-based rewriting, neural refinement, and reasoning-based refinement. We evaluate Echo on function-level benchmarks and the Mirai malware binary. Compared with the strongest baseline, Echo produces 2.43x more exact matches on average and achieves the highest structural similarity to ground-truth source code. On Mirai, Echo matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively.

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