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New BaSE method optimizes LLM call allocation in evolutionary search

By PulseAugur Editorial · [4 sources] ·

Researchers have developed a new method called BaSE (Bandit-based Self-Evolving) to optimize the allocation of large language model (LLM) calls in evolutionary search systems. This approach addresses the issue of inconsistent results in LLM-guided searches by dynamically allocating LLM calls across parallel trajectories. BaSE demonstrated a 12.3% improvement in mean fitness over existing baselines, particularly enhancing reliability in high-variance scenarios without altering the underlying models or prompts. AI

IMPACT Optimizes LLM resource allocation, potentially improving efficiency and reliability in complex search tasks.

RANK_REASON The cluster contains a research paper detailing a new method for optimizing LLM usage in evolutionary search.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New BaSE method optimizes LLM call allocation in evolutionary search

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COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Sixue Xing, Haoyu He, Kerui Wu, Zhuo Yang, Haozheng Luo, Tianfan Fu, Aarthy Nagarajan ·

    Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits

    arXiv:2605.29268v1 Announce Type: cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocu…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Aarthy Nagarajan ·

    Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits

    LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits

    LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…

  4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Aarthy Nagarajan ·

    Compute Allocation in Evolutionary Search: From Depth-Breadth to Multi-Armed Bandits

    LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing systems report only the best of many runs and leave the run-to-run distribution undocumented. We ask how a fixed budget of LLM calls sho…

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