Nested-Refinement Metamorphosis: Reflective Evolution for Efficient Optimization of Networking Problems (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in network algorithm design but suffer from inefficient iterative coding and high computational costs. |
| Approach: | They propose a method to iteratively refine task descriptions and metamorphosis on algorithms to generate more effective solutions. |
| Outcome: | Experimental results show that Nested-Refinement Metamorphosis outperforms state-of-the-art approaches in performance and efficiency. |
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AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)
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| Challenge: | Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. |
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities in understanding and generating codes. |
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Nature-Inspired Population-Based Evolution of Large Language Models (2026.acl-long)
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| Challenge: | a new framework for population-based evolution of large language models is emerging . a population-driven evolution of LLMs is a key component of evolution, authors say . |
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| Challenge: | Data synthesis is a key research area in large language models (LLMs). |
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| Challenge: | Existing approaches to subjective assessment are inconsistent and inconsistent due to inconsistent scales and inherent preference biases. |
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| Challenge: | Existing methods for recursive reasoning are limited due to lack of expert-curated data. |
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PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)
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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu
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