{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MMSF6765YWMFAGWZ6VYJLWKBRR","short_pith_number":"pith:MMSF6765","schema_version":"1.0","canonical_sha256":"63245f7fddc598501ad9f57095d9418c60f36432be5d85a8128d17738610c282","source":{"kind":"arxiv","id":"2406.05003","version":2},"attestation_state":"computed","paper":{"title":"Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.RO","authors_text":"Kimberlee Chang, Matthew Gombolay, Michael Munje, Reed Jensen, Rohan Paleja","submitted_at":"2024-06-07T15:17:06Z","abstract_excerpt":"Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain, Overcooked-AI, that state-of-the-art techniques for human-machine teaming (HMT), which rely on imitation or reinforcement learning, are brittle and result in a machine agent that aims to decouple the machine and human's actions to act independently rather than in a synergistic fashion. To remedy this deficiency, we develop HMT approaches that enable iterative, mixed-i"},"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":"2406.05003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-06-07T15:17:06Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"1148fc5976e7384b3927fbc4403f3ba9ac77f3987f0fbbe4cc3d41103fa09849","abstract_canon_sha256":"c856a81f6fedf94bbe5328cb434c208e4b55c29ce938db5f3dd87fbf53cfb457"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:12.208736Z","signature_b64":"9pa6npzBPJvxpygvenwaE7GqOrZ+ftzuLbdZ5kRIO5d+8nL0jVcvWphrQt/z+Px78TPJOrRKhuvOqIQf64DuDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63245f7fddc598501ad9f57095d9418c60f36432be5d85a8128d17738610c282","last_reissued_at":"2026-07-05T09:36:12.208253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:12.208253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.RO","authors_text":"Kimberlee Chang, Matthew Gombolay, Michael Munje, Reed Jensen, Rohan Paleja","submitted_at":"2024-06-07T15:17:06Z","abstract_excerpt":"Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain, Overcooked-AI, that state-of-the-art techniques for human-machine teaming (HMT), which rely on imitation or reinforcement learning, are brittle and result in a machine agent that aims to decouple the machine and human's actions to act independently rather than in a synergistic fashion. To remedy this deficiency, we develop HMT approaches that enable iterative, mixed-i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05003","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/2406.05003/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":"2406.05003","created_at":"2026-07-05T09:36:12.208321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05003v2","created_at":"2026-07-05T09:36:12.208321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05003","created_at":"2026-07-05T09:36:12.208321+00:00"},{"alias_kind":"pith_short_12","alias_value":"MMSF6765YWMF","created_at":"2026-07-05T09:36:12.208321+00:00"},{"alias_kind":"pith_short_16","alias_value":"MMSF6765YWMFAGWZ","created_at":"2026-07-05T09:36:12.208321+00:00"},{"alias_kind":"pith_short_8","alias_value":"MMSF6765","created_at":"2026-07-05T09:36:12.208321+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.06325","citing_title":"Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR","json":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR.json","graph_json":"https://pith.science/api/pith-number/MMSF6765YWMFAGWZ6VYJLWKBRR/graph.json","events_json":"https://pith.science/api/pith-number/MMSF6765YWMFAGWZ6VYJLWKBRR/events.json","paper":"https://pith.science/paper/MMSF6765"},"agent_actions":{"view_html":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR","download_json":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR.json","view_paper":"https://pith.science/paper/MMSF6765","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05003&json=true","fetch_graph":"https://pith.science/api/pith-number/MMSF6765YWMFAGWZ6VYJLWKBRR/graph.json","fetch_events":"https://pith.science/api/pith-number/MMSF6765YWMFAGWZ6VYJLWKBRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR/action/storage_attestation","attest_author":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR/action/author_attestation","sign_citation":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR/action/citation_signature","submit_replication":"https://pith.science/pith/MMSF6765YWMFAGWZ6VYJLWKBRR/action/replication_record"}},"created_at":"2026-07-05T09:36:12.208321+00:00","updated_at":"2026-07-05T09:36:12.208321+00:00"}