TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery

TRACE is a transition‑aware residual control framework designed to improve multi‑objective materials discovery with large language model agents. It records each local refinement as a parent‑edit‑child transition, aggregates evidence to estimate reusable edit effects, and ranks future edits by their predicted ability to reduce remaining constraint violations while protecting already satisfied objectives. In a controlled comparison, TRACE outperforms the state‑of‑the‑art LLEMA baseline, raising the macro‑average hit rate from 18.13% to 25.96%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents
• benchmarks