{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IHEVABDZRTW3UAOFBIXIP3L4BP","short_pith_number":"pith:IHEVABDZ","schema_version":"1.0","canonical_sha256":"41c95004798cedba01c50a2e87ed7c0bfee226f3e9b4ef425b87cddb6e472580","source":{"kind":"arxiv","id":"2303.08268","version":3},"attestation_state":"computed","paper":{"title":"Chat with the Environment: Interactive Multimodal Perception Using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.RO","authors_text":"Cornelius Weber, Mengdi Li, Muhammad Burhan Hafez, Stefan Wermter, Xufeng Zhao","submitted_at":"2023-03-14T23:01:27Z","abstract_excerpt":"Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on "},"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":"2303.08268","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-03-14T23:01:27Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG","cs.SD","eess.AS"],"title_canon_sha256":"7981d51e94c9ec2c6037d620d280c1116b126380f3e70fe980ff7a3a81c316da","abstract_canon_sha256":"86d582edda54324e06fbb4ff9c8292926e84e6d16767ceeee6ec2b83d76bcf03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:30.416397Z","signature_b64":"1cAxVZUdwZu+Zx6xtpi2Eqz3rkal8nhYMHmwPZU+ppgaZmjmyb2usI3PzB6JeB7BuLKN+6kol3RFWfg6dS1bDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41c95004798cedba01c50a2e87ed7c0bfee226f3e9b4ef425b87cddb6e472580","last_reissued_at":"2026-07-05T06:59:30.415961Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:30.415961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chat with the Environment: Interactive Multimodal Perception Using Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","cs.SD","eess.AS"],"primary_cat":"cs.RO","authors_text":"Cornelius Weber, Mengdi Li, Muhammad Burhan Hafez, Stefan Wermter, Xufeng Zhao","submitted_at":"2023-03-14T23:01:27Z","abstract_excerpt":"Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.08268","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/2303.08268/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":"2303.08268","created_at":"2026-07-05T06:59:30.416018+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.08268v3","created_at":"2026-07-05T06:59:30.416018+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.08268","created_at":"2026-07-05T06:59:30.416018+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHEVABDZRTW3","created_at":"2026-07-05T06:59:30.416018+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHEVABDZRTW3UAOF","created_at":"2026-07-05T06:59:30.416018+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHEVABDZ","created_at":"2026-07-05T06:59:30.416018+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2309.07864","citing_title":"The Rise and Potential of Large Language Model Based Agents: A Survey","ref_index":192,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP","json":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP.json","graph_json":"https://pith.science/api/pith-number/IHEVABDZRTW3UAOFBIXIP3L4BP/graph.json","events_json":"https://pith.science/api/pith-number/IHEVABDZRTW3UAOFBIXIP3L4BP/events.json","paper":"https://pith.science/paper/IHEVABDZ"},"agent_actions":{"view_html":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP","download_json":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP.json","view_paper":"https://pith.science/paper/IHEVABDZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.08268&json=true","fetch_graph":"https://pith.science/api/pith-number/IHEVABDZRTW3UAOFBIXIP3L4BP/graph.json","fetch_events":"https://pith.science/api/pith-number/IHEVABDZRTW3UAOFBIXIP3L4BP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP/action/storage_attestation","attest_author":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP/action/author_attestation","sign_citation":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP/action/citation_signature","submit_replication":"https://pith.science/pith/IHEVABDZRTW3UAOFBIXIP3L4BP/action/replication_record"}},"created_at":"2026-07-05T06:59:30.416018+00:00","updated_at":"2026-07-05T06:59:30.416018+00:00"}