Papers with APPS
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving (2024.acl-long)
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| Challenge: | Large language models (LLMs) have impressive proficiency in natural language processing, but performance in code generation tasks remains limited. |
| Approach: | They propose a framework that emulates the full cycle of program synthesis as observed in humans. |
| Outcome: | The proposed framework replicates the full cycle of program synthesis as observed in human developers. |
CodeSim: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have made significant strides in code generation and problem solving. |
| Approach: | They propose a multi-agent code generation framework that integrates human-like perception to address the stages of program synthesis. |
| Outcome: | The proposed framework achieves state-of-the-art (pass@1) results and shows potential for even greater enhancement when cascaded with external debuggers. |
SEK: Self-Explained Keywords Empower Large Language Models for Code Generation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved impressive performance in code generation. |
| Approach: | They propose a technique that extracts and explicates the key terms in the problem description with the LLM itself. |
| Outcome: | The proposed technique improves the Pass@1 of DeepSeek-Coder-V2-Instruct from 85.4% to 93.3% on the humaneval benchmark. |
MapCoder-Lite: Distilling Multi-Agent Coding into a Single Small LLM (2026.findings-eacl)
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| Challenge: | Existing large-scale (> 30 B) models are costly and collapse when downsized to small open-source models. |
| Approach: | They propose a framework for distilling large, multi-agent coding systems into a single 7B model. |
| Outcome: | The proposed framework doubles xCodeEval accuracy and reduces GPU memory and token generation time by 4 compared to a 32B model. |
SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution (2026.findings-acl)
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| Challenge: | State-of-the-art code generation frameworks rely on mental simulations to validate buggy code. |
| Approach: | They propose a mental-reality gap between mental simulation and actual execution . they propose sandboxed execution with a simple principle: don't imagine—execute . |
| Outcome: | The proposed framework achieves state-of-the-art pass@1 performance on humanEval, CodeContests and APPS. |
Alignment with Fill-In-the-Middle for Enhancing Code Generation (2025.emnlp-main)
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Houxing Ren, Zimu Lu, Weikang Shi, Haotian Hou, Yunqiao Yang, Ke Wang, Aojun Zhou, Junting Pan, Mingjie Zhan, Hongsheng Li
| Challenge: | Existing methods for generating test cases with limited training data are not reliable and may be counterproductive. |
| Approach: | They propose a method that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases. |
| Outcome: | The proposed approach shows significant improvements in code generation tasks on benchmark datasets such as HumanEval (+), MBPP (+), and APPS. |
Planning-Driven Programming: A Large Language Model Programming Workflow (2025.acl-long)
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| Challenge: | Recent research suggests continuous program refinements through visible tests to improve code generation accuracy in large language models (LLMs). |
| Approach: | They propose an LLM programming workflow to improve both initial code generation and subsequent refinements within a structured two-phase workflow. |
| Outcome: | The proposed workflow improves both initial code generation and subsequent refinements within a structured two-phase workflow. |
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)
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Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang
| Challenge: | Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution. |
| Approach: | They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems. |