PulseAugur
EN
LIVE 08:09:23

LLM-guided search finds optimal deletion-correcting codes

Researchers have utilized an LLM-guided evolutionary search method called FunSearch to discover functions for constructing deletion-correcting codes. This approach successfully identified a function that generates the conjectured-optimal Varshamov-Tenengolts code for single deletions. For multiple deletions and quaternary edit codes, the discovered functions offer improvements over existing methods but are empirical heuristics. The study also explored design choices for LLM-guided search, finding that prioritizing sampling more functions over longer reasoning traces per function was more effective, and that deduplicating logically identical functions was crucial for search diversity. AI

IMPACT Demonstrates LLM-guided search for novel code design, potentially accelerating discovery in information theory.

RANK_REASON The cluster contains an academic paper detailing a new research method and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Franziska Weindel, Reinhard Heckel ·

    LLM-Guided Search for Deletion-Correcting Codes

    arXiv:2504.00613v2 Announce Type: replace Abstract: Finding deletion-correcting codes of maximum size has been an open problem for over 70 years, even for a single deletion. We adapt FunSearch, a large language model (LLM)-guided evolutionary search, to discover functions that co…