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. |
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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. |
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PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation (2026.findings-acl)
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| Challenge: | Existing multi agent frameworks for large language models are brittle on code generation tasks. |
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
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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. |
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| Challenge: | Excessive refinement can cause over-correction and reduce performance, authors say . they say MAgICoRe is a framework for multi-agent iteration for coarse-to-fine refinement . |
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. |
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A Strategic Coordination Framework of Small LMs Matches Large LMs in Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models suffer from high computational costs and environmental inefficiency . smaller LMs are more accessible and sustainable, but their individual capabilities often fall short . a collaborative framework for small LM combines specialized roles to iterative refinement and quality control . |
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023.findings-acl)
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister
| Challenge: | Deploying large language models (LLMs) is difficult because they are memory inefficient and compute-intensive for practical applications. |
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