{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/thermal-forge","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/8733609e-ab03-4fd9-b213-096d9ba5578e","json_url":"https://onlylabs.fyi/signals/8733609e-ab03-4fd9-b213-096d9ba5578e/signal.json","generated_at":"2026-06-27T00:48:00.601Z","evidence_latest_fetched_at":"2026-06-17T07:02:56.179739+00:00","signal_first_seen_at":"2026-06-17T07:01:21.192218+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/8733609e-ab03-4fd9-b213-096d9ba5578e","signal_json":"https://onlylabs.fyi/signals/8733609e-ab03-4fd9-b213-096d9ba5578e/signal.json","source":"https://github.com/amazon-science/thermal-forge","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/thermal-forge (Python). 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Specifically, ThermalForge implements a bi-level optimization approach where an LLM proposes model structures (physics-based equations or neural architectures) and then their parameters are calibrated against smart thermostat data. This repository contains the source code for ThermalForge and the necessary configurations needed to replicate the experiments in the accompanying paper: > **Exploring LLM-Powered Agents for Modeling Thermal Dynamics of Buildings** > in *Proceedings of ACM BuildSys 2026* --- How It Works ThermalForge runs a two-phase modeling pipeline for each building: 1. **Physics-Based Modeling** — The LLM generates grey-box thermal models (RC networks) as PyTorch code. 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