{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R6LJZMN2ACCLOATELCAZ7TNQP5","short_pith_number":"pith:R6LJZMN2","schema_version":"1.0","canonical_sha256":"8f969cb1ba0084b7026458819fcdb07f4c7889b02770ce409036d4d960d5c147","source":{"kind":"arxiv","id":"2605.29705","version":1},"attestation_state":"computed","paper":{"title":"BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bomin Kang, Daehee Park, Hyunjin Lim, Mincheol Kang","submitted_at":"2026-05-28T10:04:02Z","abstract_excerpt":"Trajectory prediction is a fundamental task for autonomous systems, requiring complex reasoning about multi-agent interactions and intents. Large language models (LLMs) have recently been adopted for this task, as they provide strong contextual reasoning and interpretable, language-based trajectory representations. However, these LLM-based predictors are extremely memory- and compute-intensive, making them difficult to deploy on resource-constrained edge devices such as on-board computers in autonomous robots. To bridge this gap, we propose BitTP, which converts an LLM-based trajectory predict"},"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":"2605.29705","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-28T10:04:02Z","cross_cats_sorted":[],"title_canon_sha256":"2a8e62bb0c987372c83976ff3d58192fa12e7404fa1c724d543b781429c6ccb1","abstract_canon_sha256":"9f7f17a90ccd19761a70cd9da7d4dd1e4ae824540ede3ec803ca0ce29de4a80f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:56.436550Z","signature_b64":"NSarKwf7AzFPBKTNWs2uUfjvr5o7VQuRwCBvH9LgST6T6kMqGQdged1/ipDEXYh/YpctvLowNsH7svGkqEwIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f969cb1ba0084b7026458819fcdb07f4c7889b02770ce409036d4d960d5c147","last_reissued_at":"2026-05-29T01:05:56.435379Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:56.435379Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bomin Kang, Daehee Park, Hyunjin Lim, Mincheol Kang","submitted_at":"2026-05-28T10:04:02Z","abstract_excerpt":"Trajectory prediction is a fundamental task for autonomous systems, requiring complex reasoning about multi-agent interactions and intents. Large language models (LLMs) have recently been adopted for this task, as they provide strong contextual reasoning and interpretable, language-based trajectory representations. However, these LLM-based predictors are extremely memory- and compute-intensive, making them difficult to deploy on resource-constrained edge devices such as on-board computers in autonomous robots. To bridge this gap, we propose BitTP, which converts an LLM-based trajectory predict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29705","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/2605.29705/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":"2605.29705","created_at":"2026-05-29T01:05:56.436099+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29705v1","created_at":"2026-05-29T01:05:56.436099+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29705","created_at":"2026-05-29T01:05:56.436099+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6LJZMN2ACCL","created_at":"2026-05-29T01:05:56.436099+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6LJZMN2ACCLOATE","created_at":"2026-05-29T01:05:56.436099+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6LJZMN2","created_at":"2026-05-29T01:05:56.436099+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/R6LJZMN2ACCLOATELCAZ7TNQP5","json":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5.json","graph_json":"https://pith.science/api/pith-number/R6LJZMN2ACCLOATELCAZ7TNQP5/graph.json","events_json":"https://pith.science/api/pith-number/R6LJZMN2ACCLOATELCAZ7TNQP5/events.json","paper":"https://pith.science/paper/R6LJZMN2"},"agent_actions":{"view_html":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5","download_json":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5.json","view_paper":"https://pith.science/paper/R6LJZMN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29705&json=true","fetch_graph":"https://pith.science/api/pith-number/R6LJZMN2ACCLOATELCAZ7TNQP5/graph.json","fetch_events":"https://pith.science/api/pith-number/R6LJZMN2ACCLOATELCAZ7TNQP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5/action/storage_attestation","attest_author":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5/action/author_attestation","sign_citation":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5/action/citation_signature","submit_replication":"https://pith.science/pith/R6LJZMN2ACCLOATELCAZ7TNQP5/action/replication_record"}},"created_at":"2026-05-29T01:05:56.436099+00:00","updated_at":"2026-05-29T01:05:56.436099+00:00"}