{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:X7ILXCBN3AJA5N7SDCZ5AN47WD","short_pith_number":"pith:X7ILXCBN","schema_version":"1.0","canonical_sha256":"bfd0bb882dd8120eb7f218b3d0379fb0eb975a183f6cf3c725e85f14d2eff87a","source":{"kind":"arxiv","id":"2411.15867","version":3},"attestation_state":"computed","paper":{"title":"PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Teng Zhou, Xiaoyu Zhang, Yongchuan Tang","submitted_at":"2024-11-24T15:06:57Z","abstract_excerpt":"Panoramic Image Generation (PIG) aims to create coherent images of arbitrary lengths. Most existing methods fall in the joint diffusion paradigm, but their complex and heuristic crop connection designs often limit their ability to achieve multilevel coherence. By deconstructing this challenge into its core components, we find it naturally aligns with next-token prediction, leading us to adopt an autoregressive (AR) paradigm for PIG modeling. However, existing visual AR (VAR) models are limited to fixed-size generation, lacking the capability to produce panoramic images. In this paper, we propo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.15867","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-24T15:06:57Z","cross_cats_sorted":[],"title_canon_sha256":"cbfdf3ee147d36e75e582c44e489ae4b62b4fefbc50fee7364b47624ba9b9d88","abstract_canon_sha256":"d3d9092e5b2320a4e9a9c7634cd16b764ab17102abc7fe00427189a1a8da60fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:37.447446Z","signature_b64":"8MDZkJ6JPCW1gEhLq+eKKNGA7kUeIGI5leJYz9LaPkbkq4tgLez2Y7G/T/KqFokE8w3ISALmf4zAAaY2SAQRDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfd0bb882dd8120eb7f218b3d0379fb0eb975a183f6cf3c725e85f14d2eff87a","last_reissued_at":"2026-07-05T11:46:37.446939Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:37.446939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Teng Zhou, Xiaoyu Zhang, Yongchuan Tang","submitted_at":"2024-11-24T15:06:57Z","abstract_excerpt":"Panoramic Image Generation (PIG) aims to create coherent images of arbitrary lengths. Most existing methods fall in the joint diffusion paradigm, but their complex and heuristic crop connection designs often limit their ability to achieve multilevel coherence. By deconstructing this challenge into its core components, we find it naturally aligns with next-token prediction, leading us to adopt an autoregressive (AR) paradigm for PIG modeling. However, existing visual AR (VAR) models are limited to fixed-size generation, lacking the capability to produce panoramic images. In this paper, we propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15867","kind":"arxiv","version":3},"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/2411.15867/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.15867","created_at":"2026-07-05T11:46:37.447005+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15867v3","created_at":"2026-07-05T11:46:37.447005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15867","created_at":"2026-07-05T11:46:37.447005+00:00"},{"alias_kind":"pith_short_12","alias_value":"X7ILXCBN3AJA","created_at":"2026-07-05T11:46:37.447005+00:00"},{"alias_kind":"pith_short_16","alias_value":"X7ILXCBN3AJA5N7S","created_at":"2026-07-05T11:46:37.447005+00:00"},{"alias_kind":"pith_short_8","alias_value":"X7ILXCBN","created_at":"2026-07-05T11:46:37.447005+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD","json":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD.json","graph_json":"https://pith.science/api/pith-number/X7ILXCBN3AJA5N7SDCZ5AN47WD/graph.json","events_json":"https://pith.science/api/pith-number/X7ILXCBN3AJA5N7SDCZ5AN47WD/events.json","paper":"https://pith.science/paper/X7ILXCBN"},"agent_actions":{"view_html":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD","download_json":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD.json","view_paper":"https://pith.science/paper/X7ILXCBN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15867&json=true","fetch_graph":"https://pith.science/api/pith-number/X7ILXCBN3AJA5N7SDCZ5AN47WD/graph.json","fetch_events":"https://pith.science/api/pith-number/X7ILXCBN3AJA5N7SDCZ5AN47WD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD/action/storage_attestation","attest_author":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD/action/author_attestation","sign_citation":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD/action/citation_signature","submit_replication":"https://pith.science/pith/X7ILXCBN3AJA5N7SDCZ5AN47WD/action/replication_record"}},"created_at":"2026-07-05T11:46:37.447005+00:00","updated_at":"2026-07-05T11:46:37.447005+00:00"}