Compositional energy models: learn reusable evaluation factors, compose them for new tasks
du_yilun · x · 2026-09-09
Using the example of placing a mug on a crowded table, the author argues that instead of directly predicting the placement, one can learn reusable local evaluation factors — support, clearance, reachability — each judging a local relationship rather than the whole scene; inference composes these factors into an evaluator and searches for a placement satisfying all constraints together. In their compositional-energy work, factors learned on small SAT problems compose to solve larger ones. The principle extends to planning and reasoning: by combining familiar skills, reasoning operations and expertise, a system can construct both a solution and the reasoning process used to find it.
Related event: Du Yilun Proposes Reasoning-Driven Robot Generalization(3 posts)→
More from Research
- Project CETI's AI uncovers vowel-like patterns in sperm whale vocalizations — begusgasper · 2026-09-10
- Geodesic Launches AI-Native Protein Therapeutics Platform With NovaDDE, NovaAtom-Lite — QuanquanGu · 2026-09-10
- OpenAI spends $20M of compute to frontrun AI-assisted near-solution of century-old math problem — ctjlewis · 2026-09-10
- Autoresearch Loop with Tinker Reproduces Self-Distillation Papers at Predictable Cost — SRSchmidgall · 2026-09-10
- Researcher Lets Codex Run the Experiments, Publishes Recurrent Model Length-Extrapolation Paper — qixing_huang · 2026-09-10
- Author defends paper's narrow scope: precision over breadth, more to come — brwilder · 2026-09-10