{"id":"9d1dbe14-3ae0-4875-bd60-16722da5623a","arxiv_id":"2509.24575","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A natural-language team command is distilled into a small recurrent network that encodes the task as an automaton, while a graph-neural-network policy executes it in a decentralized, real-time manner.","lead":"The paper turns a plain-language team command into a small neural program that runs on each robot: a language model first decomposes the task into a state-by-state plan, a recurrent network learns that plan, and a graph-network policy tells each robot what to do. The payoff is onboard, real-time coordination without needing a live connection to a large language model.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-04T13:52:08.626021+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}