{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TSQ2G3VIRZRHSZ6LRXY4HJUOHZ","short_pith_number":"pith:TSQ2G3VI","canonical_record":{"source":{"id":"2606.07024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T08:14:28Z","cross_cats_sorted":[],"title_canon_sha256":"f72444e0a56122adbfbb22a807c59b068da370e3c8ee23dfea7c489f1679a1e0","abstract_canon_sha256":"5ee19ca37ccaf141d0d62685600db1e71d57724c3996934d3f4ce1d0852196dc"},"schema_version":"1.0"},"canonical_sha256":"9ca1a36ea88e627967cb8df1c3a68e3e7f69fbfb985427aa86795608ba852344","source":{"kind":"arxiv","id":"2606.07024","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07024","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07024v1","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07024","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_12","alias_value":"TSQ2G3VIRZRH","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_16","alias_value":"TSQ2G3VIRZRHSZ6L","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_8","alias_value":"TSQ2G3VI","created_at":"2026-06-08T01:04:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TSQ2G3VIRZRHSZ6LRXY4HJUOHZ","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07024","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T08:14:28Z","cross_cats_sorted":[],"title_canon_sha256":"f72444e0a56122adbfbb22a807c59b068da370e3c8ee23dfea7c489f1679a1e0","abstract_canon_sha256":"5ee19ca37ccaf141d0d62685600db1e71d57724c3996934d3f4ce1d0852196dc"},"schema_version":"1.0"},"canonical_sha256":"9ca1a36ea88e627967cb8df1c3a68e3e7f69fbfb985427aa86795608ba852344","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:41.887445Z","signature_b64":"wM6zyUgYKnl0jHlcCJLyY6CMsmRD7tq0Cb3GNWpCkrmMlRP44uwlhmn23n5EmCfaLBBtHh42D0VjlUmqo4JbBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ca1a36ea88e627967cb8df1c3a68e3e7f69fbfb985427aa86795608ba852344","last_reissued_at":"2026-06-08T01:04:41.886616Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:41.886616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07024","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":"2026-06-08T01:04:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Sa9fHuiZFpBUapcZhIVNGqXcjgIkLK8es47C5BGXEujakjq4DFf+c8+Ya1iDU/ZiBAsC+YY9bw7S6hsEYoQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T05:11:26.825259Z"},"content_sha256":"8661e9277740f3405f45efabbf9b724c50c65902ed9a5c7f2f7a952e238c477b","schema_version":"1.0","event_id":"sha256:8661e9277740f3405f45efabbf9b724c50c65902ed9a5c7f2f7a952e238c477b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TSQ2G3VIRZRHSZ6LRXY4HJUOHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jibum Kim, Jinyeong Park, Minseong Kim, Sungho Park","submitted_at":"2026-06-05T08:14:28Z","abstract_excerpt":"Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we propose GuideCAD, which leverages semantically rich visual-textual representations having only a small number of trainable parameters to generate 3D CAD models. Specifically, GuideCAD uses a mapping network that converts image embeddings into prefix embeddings, enabling a pretrained large language model to integrate visual and textual information. As a result, a transformer-based decoder predicts the construction sequence using the visual-textual "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07024","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/2606.07024/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":"2026-06-08T01:04:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"POHdjaf59cOm4orreABg9qY7UHSwrlLLrjMCESRAfBdsXwt5gRlrMQUADynYmPURU3/S0QxBX3EmKDkqH+m1Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T05:11:26.825655Z"},"content_sha256":"35795e8b1f2841d65f179e8e045c4f3c0a35ee2d230b3005897f4071d72f49fb","schema_version":"1.0","event_id":"sha256:35795e8b1f2841d65f179e8e045c4f3c0a35ee2d230b3005897f4071d72f49fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/bundle.json","state_url":"https://pith.science/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/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":"2026-06-20T05:11:26Z","links":{"resolver":"https://pith.science/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ","bundle":"https://pith.science/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/bundle.json","state":"https://pith.science/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TSQ2G3VIRZRHSZ6LRXY4HJUOHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TSQ2G3VIRZRHSZ6LRXY4HJUOHZ","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":"5ee19ca37ccaf141d0d62685600db1e71d57724c3996934d3f4ce1d0852196dc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T08:14:28Z","title_canon_sha256":"f72444e0a56122adbfbb22a807c59b068da370e3c8ee23dfea7c489f1679a1e0"},"schema_version":"1.0","source":{"id":"2606.07024","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07024","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07024v1","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07024","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_12","alias_value":"TSQ2G3VIRZRH","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_16","alias_value":"TSQ2G3VIRZRHSZ6L","created_at":"2026-06-08T01:04:41Z"},{"alias_kind":"pith_short_8","alias_value":"TSQ2G3VI","created_at":"2026-06-08T01:04:41Z"}],"graph_snapshots":[{"event_id":"sha256:35795e8b1f2841d65f179e8e045c4f3c0a35ee2d230b3005897f4071d72f49fb","target":"graph","created_at":"2026-06-08T01:04:41Z","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/2606.07024/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we propose GuideCAD, which leverages semantically rich visual-textual representations having only a small number of trainable parameters to generate 3D CAD models. Specifically, GuideCAD uses a mapping network that converts image embeddings into prefix embeddings, enabling a pretrained large language model to integrate visual and textual information. As a result, a transformer-based decoder predicts the construction sequence using the visual-textual ","authors_text":"Jibum Kim, Jinyeong Park, Minseong Kim, Sungho Park","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T08:14:28Z","title":"GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07024","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":"sha256:8661e9277740f3405f45efabbf9b724c50c65902ed9a5c7f2f7a952e238c477b","target":"record","created_at":"2026-06-08T01:04:41Z","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":"5ee19ca37ccaf141d0d62685600db1e71d57724c3996934d3f4ce1d0852196dc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-05T08:14:28Z","title_canon_sha256":"f72444e0a56122adbfbb22a807c59b068da370e3c8ee23dfea7c489f1679a1e0"},"schema_version":"1.0","source":{"id":"2606.07024","kind":"arxiv","version":1}},"canonical_sha256":"9ca1a36ea88e627967cb8df1c3a68e3e7f69fbfb985427aa86795608ba852344","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ca1a36ea88e627967cb8df1c3a68e3e7f69fbfb985427aa86795608ba852344","first_computed_at":"2026-06-08T01:04:41.886616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:04:41.886616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wM6zyUgYKnl0jHlcCJLyY6CMsmRD7tq0Cb3GNWpCkrmMlRP44uwlhmn23n5EmCfaLBBtHh42D0VjlUmqo4JbBg==","signature_status":"signed_v1","signed_at":"2026-06-08T01:04:41.887445Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07024","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8661e9277740f3405f45efabbf9b724c50c65902ed9a5c7f2f7a952e238c477b","sha256:35795e8b1f2841d65f179e8e045c4f3c0a35ee2d230b3005897f4071d72f49fb"],"state_sha256":"abc61dd0355f5d6872d0d81bf621815e6a47db1e0066a1f5baea97bf1c181a99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cN+u7dxTFyPUyNLNnxm473ZGUFPbWGnLv2QY/MCF6y4rLIwrWNyNhQ0gsyQLOThyix3SSdZ42BoFBwbdzXtSBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T05:11:26.827651Z","bundle_sha256":"2cf97e43cf957c02bb459e4a594a7f04ee0bd973a5a9cf3cd91c701b1453c797"}}