{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:7LSA565U2GA4VQ6LIXRKJTSM47","short_pith_number":"pith:7LSA565U","canonical_record":{"source":{"id":"2602.00424","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-31T00:22:52Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"8ea11efb5864335bdc0b9561069e5cd1de8931a94f142ca0f668dc89df3c1d28","abstract_canon_sha256":"8f5ba1b06e3172df6d89c89b7d2e2ba4ef87514fdee469b5de5863783d700ac1"},"schema_version":"1.0"},"canonical_sha256":"fae40efbb4d181cac3cb45e2a4ce4ce7de98429c0cced402bcea0491bd2faa09","source":{"kind":"arxiv","id":"2602.00424","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.00424","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"arxiv_version","alias_value":"2602.00424v2","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.00424","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_12","alias_value":"7LSA565U2GA4","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_16","alias_value":"7LSA565U2GA4VQ6L","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_8","alias_value":"7LSA565U","created_at":"2026-06-11T01:09:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:7LSA565U2GA4VQ6LIXRKJTSM47","target":"record","payload":{"canonical_record":{"source":{"id":"2602.00424","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-31T00:22:52Z","cross_cats_sorted":["cond-mat.mtrl-sci"],"title_canon_sha256":"8ea11efb5864335bdc0b9561069e5cd1de8931a94f142ca0f668dc89df3c1d28","abstract_canon_sha256":"8f5ba1b06e3172df6d89c89b7d2e2ba4ef87514fdee469b5de5863783d700ac1"},"schema_version":"1.0"},"canonical_sha256":"fae40efbb4d181cac3cb45e2a4ce4ce7de98429c0cced402bcea0491bd2faa09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:28.098634Z","signature_b64":"Qz9qzwTlPw21wbNaNl4MNByKchTtItQftSbyo/wzcWrh3Lmqxn98oFLmijqA/ckAM8du5BRmDigK9z96C091AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fae40efbb4d181cac3cb45e2a4ce4ce7de98429c0cced402bcea0491bd2faa09","last_reissued_at":"2026-06-11T01:09:28.097751Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:28.097751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.00424","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-11T01:09:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oMUf4Zn06TG6j1amCw2PD9apD6PQcrzP7AFQ5LYYA+KIFY946Yr5ADBH1UifsxkTG97mZjLJTBAS/6vyzSjHDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T00:42:14.284582Z"},"content_sha256":"5586d81bdb388713923b2aac13910b08d36f20f970e97bee0e1e0be633f7cde3","schema_version":"1.0","event_id":"sha256:5586d81bdb388713923b2aac13910b08d36f20f970e97bee0e1e0be633f7cde3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:7LSA565U2GA4VQ6LIXRKJTSM47","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Open Materials Generation with Inference-Time Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.mtrl-sci"],"primary_cat":"cs.LG","authors_text":"Philipp Hoellmer, Stefano Martiniani","submitted_at":"2026-01-31T00:22:52Z","abstract_excerpt":"Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.00424","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.00424/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-11T01:09:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S3cE0UswMLOkft+jSVqQNRQhlWGZd2PtQ/72/FsZGxnqWUZ15yT0NpsNW4ISonoolYNJCLU5YpfNITs95SQ1Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T00:42:14.284958Z"},"content_sha256":"138a8bfae65bd74c1f63561f466a490fd0c51f4af66292df0ad9899fdad6d333","schema_version":"1.0","event_id":"sha256:138a8bfae65bd74c1f63561f466a490fd0c51f4af66292df0ad9899fdad6d333"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7LSA565U2GA4VQ6LIXRKJTSM47/bundle.json","state_url":"https://pith.science/pith/7LSA565U2GA4VQ6LIXRKJTSM47/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7LSA565U2GA4VQ6LIXRKJTSM47/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-25T00:42:14Z","links":{"resolver":"https://pith.science/pith/7LSA565U2GA4VQ6LIXRKJTSM47","bundle":"https://pith.science/pith/7LSA565U2GA4VQ6LIXRKJTSM47/bundle.json","state":"https://pith.science/pith/7LSA565U2GA4VQ6LIXRKJTSM47/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7LSA565U2GA4VQ6LIXRKJTSM47/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:7LSA565U2GA4VQ6LIXRKJTSM47","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8f5ba1b06e3172df6d89c89b7d2e2ba4ef87514fdee469b5de5863783d700ac1","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-31T00:22:52Z","title_canon_sha256":"8ea11efb5864335bdc0b9561069e5cd1de8931a94f142ca0f668dc89df3c1d28"},"schema_version":"1.0","source":{"id":"2602.00424","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.00424","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"arxiv_version","alias_value":"2602.00424v2","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.00424","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_12","alias_value":"7LSA565U2GA4","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_16","alias_value":"7LSA565U2GA4VQ6L","created_at":"2026-06-11T01:09:28Z"},{"alias_kind":"pith_short_8","alias_value":"7LSA565U","created_at":"2026-06-11T01:09:28Z"}],"graph_snapshots":[{"event_id":"sha256:138a8bfae65bd74c1f63561f466a490fd0c51f4af66292df0ad9899fdad6d333","target":"graph","created_at":"2026-06-11T01:09:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2602.00424/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a","authors_text":"Philipp Hoellmer, Stefano Martiniani","cross_cats":["cond-mat.mtrl-sci"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-31T00:22:52Z","title":"Open Materials Generation with Inference-Time Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.00424","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5586d81bdb388713923b2aac13910b08d36f20f970e97bee0e1e0be633f7cde3","target":"record","created_at":"2026-06-11T01:09:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8f5ba1b06e3172df6d89c89b7d2e2ba4ef87514fdee469b5de5863783d700ac1","cross_cats_sorted":["cond-mat.mtrl-sci"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-31T00:22:52Z","title_canon_sha256":"8ea11efb5864335bdc0b9561069e5cd1de8931a94f142ca0f668dc89df3c1d28"},"schema_version":"1.0","source":{"id":"2602.00424","kind":"arxiv","version":2}},"canonical_sha256":"fae40efbb4d181cac3cb45e2a4ce4ce7de98429c0cced402bcea0491bd2faa09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fae40efbb4d181cac3cb45e2a4ce4ce7de98429c0cced402bcea0491bd2faa09","first_computed_at":"2026-06-11T01:09:28.097751Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:09:28.097751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qz9qzwTlPw21wbNaNl4MNByKchTtItQftSbyo/wzcWrh3Lmqxn98oFLmijqA/ckAM8du5BRmDigK9z96C091AQ==","signature_status":"signed_v1","signed_at":"2026-06-11T01:09:28.098634Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.00424","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5586d81bdb388713923b2aac13910b08d36f20f970e97bee0e1e0be633f7cde3","sha256:138a8bfae65bd74c1f63561f466a490fd0c51f4af66292df0ad9899fdad6d333"],"state_sha256":"a848d92e8f4b54a2e1061e320c2d61e63fe077ae021946ce949a15be88ddb70b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KBte0Nl13tXeZC3yaGZxwDvAWM/34eWC/ynRkK98jLDsdcFLJTqZkn01MwHFbR7gQGfLC6xA1HGS28IgcxhQCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T00:42:14.287036Z","bundle_sha256":"8b3b83fd5e3c562219039b2e7f84f9fca09a12ac4e472ab6f357480fc56dc4bd"}}