{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2NDDB6LKBGED7EX3IDDNWTVVEU","short_pith_number":"pith:2NDDB6LK","canonical_record":{"source":{"id":"2607.14455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T01:04:32Z","cross_cats_sorted":[],"title_canon_sha256":"cc1a183f3f62de6663da8b6a77b35ab628e7c4c378f546e38704a38402e2a809","abstract_canon_sha256":"103a0e6063592536e9a43d5f8e902322a98163624ab209aadb78c6b9ba28bca6"},"schema_version":"1.0"},"canonical_sha256":"d34630f96a09883f92fb40c6db4eb5253232d4c416f7f3214c2523f11f2c61ac","source":{"kind":"arxiv","id":"2607.14455","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14455","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14455v1","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14455","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_12","alias_value":"2NDDB6LKBGED","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_16","alias_value":"2NDDB6LKBGED7EX3","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_8","alias_value":"2NDDB6LK","created_at":"2026-07-17T00:21:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2NDDB6LKBGED7EX3IDDNWTVVEU","target":"record","payload":{"canonical_record":{"source":{"id":"2607.14455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T01:04:32Z","cross_cats_sorted":[],"title_canon_sha256":"cc1a183f3f62de6663da8b6a77b35ab628e7c4c378f546e38704a38402e2a809","abstract_canon_sha256":"103a0e6063592536e9a43d5f8e902322a98163624ab209aadb78c6b9ba28bca6"},"schema_version":"1.0"},"canonical_sha256":"d34630f96a09883f92fb40c6db4eb5253232d4c416f7f3214c2523f11f2c61ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T00:21:13.233739Z","signature_b64":"dr5hzxRCep4v1Zi0LYMUQGZJ9JMtVkyMk8X4YG7QMsE8xSfmhhdtrShompC3I8SHAuBikmOFqkrqJw92FD0bCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d34630f96a09883f92fb40c6db4eb5253232d4c416f7f3214c2523f11f2c61ac","last_reissued_at":"2026-07-17T00:21:13.232864Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T00:21:13.232864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.14455","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-17T00:21:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZeliVfdMPXkHUfYyx19RLLqH5x+RPlzPXhwg+BgJo/LXPjVcklnANGON30wpkNkBIhaYRfNOXyKyFPQtg8ODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T05:51:40.175947Z"},"content_sha256":"70d090ab2b9249601da0115ff4a35d37f23cbf17e3f846d1bd64b7007f0f06ac","schema_version":"1.0","event_id":"sha256:70d090ab2b9249601da0115ff4a35d37f23cbf17e3f846d1bd64b7007f0f06ac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2NDDB6LKBGED7EX3IDDNWTVVEU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Bowei Li, Changliu Liu, Jianlin Dou, Yuner Zhang, Zhilin He","submitted_at":"2026-07-16T01:04:32Z","abstract_excerpt":"Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14455","kind":"arxiv","version":1},"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/2607.14455/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":"2026-07-17T00:21:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DUTM6B1CaZx122t1XmE5VQ4Xps/+j9Vv0od6O0FdY8aiU87+U5e9VUXS8fCIbzz0ufDG9bMTbd6acvCZiMceDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T05:51:40.176460Z"},"content_sha256":"50ab8897c879e0d449c598a890a6a9020aa568462863e162395b15c270972133","schema_version":"1.0","event_id":"sha256:50ab8897c879e0d449c598a890a6a9020aa568462863e162395b15c270972133"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/bundle.json","state_url":"https://pith.science/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/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":"2026-08-03T05:51:40Z","links":{"resolver":"https://pith.science/pith/2NDDB6LKBGED7EX3IDDNWTVVEU","bundle":"https://pith.science/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/bundle.json","state":"https://pith.science/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2NDDB6LKBGED7EX3IDDNWTVVEU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2NDDB6LKBGED7EX3IDDNWTVVEU","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":"103a0e6063592536e9a43d5f8e902322a98163624ab209aadb78c6b9ba28bca6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T01:04:32Z","title_canon_sha256":"cc1a183f3f62de6663da8b6a77b35ab628e7c4c378f546e38704a38402e2a809"},"schema_version":"1.0","source":{"id":"2607.14455","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.14455","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"arxiv_version","alias_value":"2607.14455v1","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.14455","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_12","alias_value":"2NDDB6LKBGED","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_16","alias_value":"2NDDB6LKBGED7EX3","created_at":"2026-07-17T00:21:13Z"},{"alias_kind":"pith_short_8","alias_value":"2NDDB6LK","created_at":"2026-07-17T00:21:13Z"}],"graph_snapshots":[{"event_id":"sha256:50ab8897c879e0d449c598a890a6a9020aa568462863e162395b15c270972133","target":"graph","created_at":"2026-07-17T00:21:13Z","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/2607.14455/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Single-Robot Motion Planning (SRMP) in highly non-convex constrained environments, where robots must satisfy collision-free guarantees, dynamic feasibility, and task-related constraints, is challenging under complex constraints and computational limits. Recent Model-Based Diffusion (MBD) approaches recast the SRMP as trajectory optimization that samples from a posterior over trajectories, using known dynamics, and analytically estimates the score function from rollout samples to guide diffusion denoising toward a low-cost, clean trajectory without demonstration learning. While existing works f","authors_text":"Bowei Li, Changliu Liu, Jianlin Dou, Yuner Zhang, Zhilin He","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T01:04:32Z","title":"Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.14455","kind":"arxiv","version":1},"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":"sha256:70d090ab2b9249601da0115ff4a35d37f23cbf17e3f846d1bd64b7007f0f06ac","target":"record","created_at":"2026-07-17T00:21:13Z","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":"103a0e6063592536e9a43d5f8e902322a98163624ab209aadb78c6b9ba28bca6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T01:04:32Z","title_canon_sha256":"cc1a183f3f62de6663da8b6a77b35ab628e7c4c378f546e38704a38402e2a809"},"schema_version":"1.0","source":{"id":"2607.14455","kind":"arxiv","version":1}},"canonical_sha256":"d34630f96a09883f92fb40c6db4eb5253232d4c416f7f3214c2523f11f2c61ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d34630f96a09883f92fb40c6db4eb5253232d4c416f7f3214c2523f11f2c61ac","first_computed_at":"2026-07-17T00:21:13.232864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-17T00:21:13.232864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dr5hzxRCep4v1Zi0LYMUQGZJ9JMtVkyMk8X4YG7QMsE8xSfmhhdtrShompC3I8SHAuBikmOFqkrqJw92FD0bCQ==","signature_status":"signed_v1","signed_at":"2026-07-17T00:21:13.233739Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.14455","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70d090ab2b9249601da0115ff4a35d37f23cbf17e3f846d1bd64b7007f0f06ac","sha256:50ab8897c879e0d449c598a890a6a9020aa568462863e162395b15c270972133"],"state_sha256":"a95c6eacc15999814962b1a8b5ab464109650a9fe66378711cb6ba5ad9ab43b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HhmBPu7lcz8lb7KI6G5QpQpylJUhoqx6ZSnUknl8sqlAvt5QtO0Sfg30jICkBtchzz/D9rhw4FqbRDMKfbQFDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T05:51:40.179808Z","bundle_sha256":"8b71b4752efdf51ceb15d3ddcc6d82043e1d1d84b4fd8279585a42e3bd477d53"}}