mlsanityData to 14 Oct 2026

From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer

arXiv 2609.40143 ↗First posted 30 Sep 2026cs.LG cs.NE q-bio.GNcited by 0

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Abstract (arXiv)

Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activity. Yet most model-based nucleic-acid designers optimize fixed-length sequences through substitutions; they do not ask which bases of an existing functional element can be removed while retaining predicted activity. We define the task of sequence slimming as selecting an exact-length, order-preserving subsequence while retaining activity. Modeled on the design benchmark NucleoBench, we propose a quantitative evaluation for slimming that balances sequence reduction with maintaining function. Each slimmer must return both the subsequence and its source indices, which can be used to verify that the slimmer obeyed task requirements. To our knowledge, this is the first dedicated benchmark of this deletion-only problem. The coding agent Empirical Research Assistant (ERA) then searched over executable designer programs. ERA received the task prompt and a successful substitution-only designer GrAdaBeam as a starting program, and it modified the designer to produce GRADASLIM. We report held-out evaluations for five transcription-factor binding targets, comparing random, greedy, and ERA-guided slimming at 400 and 100 bp. ERA has the highest mean in 9/10 settings. Paired bootstrap intervals for ERA minus greedy are above zero in all five 400-bp settings, below zero in one 100-bp setting, and overlap zero in the remaining four.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 30 Sep 2026.

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References · 4

  1. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 867
  2. 2020AdaLead: A simple and robust adaptive greedy search algorithm for sequence designcited by 65
  3. 2025An AI system to help scientists write expert-level empirical softwarecited by 60
  4. 2020Fast differentiable DNA and protein sequence optimization for molecular designcited by 11

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  8. 2026RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning