{"paper":{"title":"Action-Inspired Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A lightweight learned scalar potential reweights bridge samples during training to penalize uninformative paths and lift generative quality.","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Debnath Pal, Eshwar R. A.","submitted_at":"2026-05-14T09:43:32Z","abstract_excerpt":"We introduce Action-Inspired Generative Models (AGMs), a dual-network generative framework motivated by the observation that existing bridge-matching methods assign uniform regression weight to every stochastic transition in the transport landscape, regardless of whether a given bridge sample lies along a structurally coherent trajectory or a degenerate one. We address this by introducing a lightweight learned scalar potential $V_\\phi$ that scores bridge samples online and modulates the drift objective via importance weights derived through a stop-gradient barrier -- preventing adversarial fee"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"selectively penalising uninformative transport paths through the learned potential yields consistent improvements in generation quality across fidelity and coverage metrics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"that the learned scalar potential V_φ can reliably distinguish structurally coherent trajectories from degenerate ones online during training without introducing instability or adversarial dynamics between the networks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AGMs use a lightweight learned potential V_phi with stop-gradient to selectively weight informative bridge samples in generative model training, yielding better fidelity and coverage.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A lightweight learned scalar potential reweights bridge samples during training to penalize uninformative paths and lift generative quality.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f463664fb5728c39bf20814f73f05d53b46b56ce643fa7d0ec4e59c33e61867c"},"source":{"id":"2605.14631","kind":"arxiv","version":1},"verdict":{"id":"acb33bca-0b5e-4940-9e95-f296b65bf1f7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T04:40:59.250196Z","strongest_claim":"selectively penalising uninformative transport paths through the learned potential yields consistent improvements in generation quality across fidelity and coverage metrics.","one_line_summary":"AGMs use a lightweight learned potential V_phi with stop-gradient to selectively weight informative bridge samples in generative model training, yielding better fidelity and coverage.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"that the learned scalar potential V_φ can reliably distinguish structurally coherent trajectories from degenerate ones online during training without introducing instability or adversarial dynamics between the networks.","pith_extraction_headline":"A lightweight learned scalar potential reweights bridge samples during training to penalize uninformative paths and lift generative quality."},"references":{"count":13,"sample":[{"doi":"","year":1965,"title":"Feynman and Albert R","work_id":"06298579-db66-4529-8249-f8361c0b93ea","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851","work_id":"b707e044-45b2-45ad-a843-aed07db67eff","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole","work_id":"9e587d0c-ff4f-4efe-b98b-2101a602cc3f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Denoising diffusion implicit models","work_id":"884d5b66-10ac-4ea9-88b4-470a6e3404fc","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. InInternational Conference on Learning Representations, 2023","work_id":"60f63f80-86d4-48dd-9f27-ac664f461905","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":13,"snapshot_sha256":"2bb7921727cc173b25aff5db84fb0d4bf085576be87aa1567e51a3744e8da43a","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"aab9721818cbeebe914f4e6b88a1c4cb5d8538a2cc792de479445f31dea07958"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}