{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LRVRRG6VKJXQYXE5SL2VD4LPJU","short_pith_number":"pith:LRVRRG6V","schema_version":"1.0","canonical_sha256":"5c6b189bd5526f0c5c9d92f551f16f4d0f759f6fad2caffdfe916a6eae19da72","source":{"kind":"arxiv","id":"2502.10177","version":2},"attestation_state":"computed","paper":{"title":"STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ge Wang, Jinke Ren, Mingcong Lei, Shuguang Cui, Yatong Han, Yiming Zhao, Zhixin Mai","submitted_at":"2025-02-14T14:12:09Z","abstract_excerpt":"A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achieve this goal, we propose the Spatio-Temporal Memory Agent (STMA), a novel framework designed to enhance task planning and execution by integrating spatio-temporal memory. STMA is built upon three critical components: (1) a spatio-temporal memory module that captures historical and environmental changes in real time, (2) a dynamic knowledge graph that facilitates adaptive spatial reasoning, and (3) a planner-critic m"},"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":"2502.10177","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-02-14T14:12:09Z","cross_cats_sorted":[],"title_canon_sha256":"4f84c65237871b8275ca7b21878af8b362a1e3ea806bbf82103a099ce3349903","abstract_canon_sha256":"3159b074206fc3e99a7b209d5b5da104add5b6e1a85c8c9cb3ea6450f4efd501"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:13.367501Z","signature_b64":"wsaGff5Dp+b8ifWZOIdtZqzhrLyYh6p5dNqoprdxb7C4b5vZlatEoLL9YsnW/Y11/fEqlMmDRv1XA6w+a6slDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c6b189bd5526f0c5c9d92f551f16f4d0f759f6fad2caffdfe916a6eae19da72","last_reissued_at":"2026-07-05T10:22:13.366935Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:13.366935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Ge Wang, Jinke Ren, Mingcong Lei, Shuguang Cui, Yatong Han, Yiming Zhao, Zhixin Mai","submitted_at":"2025-02-14T14:12:09Z","abstract_excerpt":"A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability. To achieve this goal, we propose the Spatio-Temporal Memory Agent (STMA), a novel framework designed to enhance task planning and execution by integrating spatio-temporal memory. STMA is built upon three critical components: (1) a spatio-temporal memory module that captures historical and environmental changes in real time, (2) a dynamic knowledge graph that facilitates adaptive spatial reasoning, and (3) a planner-critic m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.10177","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/2502.10177/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":"2502.10177","created_at":"2026-07-05T10:22:13.366996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.10177v2","created_at":"2026-07-05T10:22:13.366996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.10177","created_at":"2026-07-05T10:22:13.366996+00:00"},{"alias_kind":"pith_short_12","alias_value":"LRVRRG6VKJXQ","created_at":"2026-07-05T10:22:13.366996+00:00"},{"alias_kind":"pith_short_16","alias_value":"LRVRRG6VKJXQYXE5","created_at":"2026-07-05T10:22:13.366996+00:00"},{"alias_kind":"pith_short_8","alias_value":"LRVRRG6V","created_at":"2026-07-05T10:22:13.366996+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16331","citing_title":"BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU","json":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU.json","graph_json":"https://pith.science/api/pith-number/LRVRRG6VKJXQYXE5SL2VD4LPJU/graph.json","events_json":"https://pith.science/api/pith-number/LRVRRG6VKJXQYXE5SL2VD4LPJU/events.json","paper":"https://pith.science/paper/LRVRRG6V"},"agent_actions":{"view_html":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU","download_json":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU.json","view_paper":"https://pith.science/paper/LRVRRG6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.10177&json=true","fetch_graph":"https://pith.science/api/pith-number/LRVRRG6VKJXQYXE5SL2VD4LPJU/graph.json","fetch_events":"https://pith.science/api/pith-number/LRVRRG6VKJXQYXE5SL2VD4LPJU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU/action/storage_attestation","attest_author":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU/action/author_attestation","sign_citation":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU/action/citation_signature","submit_replication":"https://pith.science/pith/LRVRRG6VKJXQYXE5SL2VD4LPJU/action/replication_record"}},"created_at":"2026-07-05T10:22:13.366996+00:00","updated_at":"2026-07-05T10:22:13.366996+00:00"}