{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:O5LD6WDXIQJXS7F5ZHFTOR2B7F","short_pith_number":"pith:O5LD6WDX","canonical_record":{"source":{"id":"2604.10895","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.HC","submitted_at":"2026-04-13T01:56:00Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"c82852f6836d50e8f529841c65f2dbb06258d757fb51cf82b5c4601725ff05f8","abstract_canon_sha256":"b47e37e1ea35a4828ccbd433b6d720d3440366b75f18038aca36f0ded60fbca6"},"schema_version":"1.0"},"canonical_sha256":"77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792","source":{"kind":"arxiv","id":"2604.10895","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10895","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10895v3","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10895","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"O5LD6WDXIQJX","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"O5LD6WDXIQJXS7F5","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"O5LD6WDX","created_at":"2026-05-20T00:05:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:O5LD6WDXIQJXS7F5ZHFTOR2B7F","target":"record","payload":{"canonical_record":{"source":{"id":"2604.10895","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.HC","submitted_at":"2026-04-13T01:56:00Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"c82852f6836d50e8f529841c65f2dbb06258d757fb51cf82b5c4601725ff05f8","abstract_canon_sha256":"b47e37e1ea35a4828ccbd433b6d720d3440366b75f18038aca36f0ded60fbca6"},"schema_version":"1.0"},"canonical_sha256":"77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:44.666593Z","signature_b64":"6Z9bNWWcfMVBQW3H05BfT7ghKKdp9NuERHbf5OWjm0Ps34WeCSpS6OoHlfV4n5//F390sSWPWtl/99H4L6yzDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792","last_reissued_at":"2026-05-20T00:05:44.666074Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:44.666074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.10895","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"krOOTnibZ4HuZ5kBKJl+E//5Q4M00yxYq1qT4ePTyCRTSgxrs0QtPckxdRsfLXgXL5UlhgveBj+mYIyK79eWBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:59:36.710144Z"},"content_sha256":"dfd6ac604dae33a3b331837021f6e54605aa9369ac7b6a38d35f8cc25dbb0cf5","schema_version":"1.0","event_id":"sha256:dfd6ac604dae33a3b331837021f6e54605aa9369ac7b6a38d35f8cc25dbb0cf5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:O5LD6WDXIQJXS7F5ZHFTOR2B7F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.","cross_cats":["cs.RO"],"primary_cat":"cs.HC","authors_text":"Mathieu Chollet, Tanaya Guha, Tongfei Bian","submitted_at":"2026-04-13T01:56:00Z","abstract_excerpt":"For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future behaviour, and if required, respond appropriately. In this work, we investigate how robots can be endowed with such social intelligence by modelling the dynamic relationship between user's internal states (latent) and actions (observable state). Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically. Drawing inspiration from theories in Cognitive Science, we propose a novel mult"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"SocialLDG achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"8e6f3bf3c91c2d23890b3a08d2944154b8ba1743680bf77b5bba336b97145cbc"},"source":{"id":"2604.10895","kind":"arxiv","version":3},"verdict":{"id":"a24df988-e2fa-4dc4-8f7e-d231b8926d92","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:31:37.512050Z","strongest_claim":"SocialLDG achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making.","one_line_summary":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically.","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10895/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"},"verdict_id":"a24df988-e2fa-4dc4-8f7e-d231b8926d92"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JMeNWMd6SItWVfErJ87KAdPtsNyzowEOZETR9Mm4bpbgqGnGeqGHAkcgQpynMUrvaXKvpClKdDMqn6ERDGc4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:59:36.710920Z"},"content_sha256":"b893dac465d2c4930e95ebbbecf02f5fbed3ca3ccdc0eb7bb33b410ddf10a7f0","schema_version":"1.0","event_id":"sha256:b893dac465d2c4930e95ebbbecf02f5fbed3ca3ccdc0eb7bb33b410ddf10a7f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/bundle.json","state_url":"https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T17:59:36Z","links":{"resolver":"https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F","bundle":"https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/bundle.json","state":"https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:O5LD6WDXIQJXS7F5ZHFTOR2B7F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b47e37e1ea35a4828ccbd433b6d720d3440366b75f18038aca36f0ded60fbca6","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.HC","submitted_at":"2026-04-13T01:56:00Z","title_canon_sha256":"c82852f6836d50e8f529841c65f2dbb06258d757fb51cf82b5c4601725ff05f8"},"schema_version":"1.0","source":{"id":"2604.10895","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10895","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10895v3","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10895","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"O5LD6WDXIQJX","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"O5LD6WDXIQJXS7F5","created_at":"2026-05-20T00:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"O5LD6WDX","created_at":"2026-05-20T00:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:b893dac465d2c4930e95ebbbecf02f5fbed3ca3ccdc0eb7bb33b410ddf10a7f0","target":"graph","created_at":"2026-05-20T00:05:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":3,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"SocialLDG achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting."}],"snapshot_sha256":"8e6f3bf3c91c2d23890b3a08d2944154b8ba1743680bf77b5bba336b97145cbc"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.10895/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future behaviour, and if required, respond appropriately. In this work, we investigate how robots can be endowed with such social intelligence by modelling the dynamic relationship between user's internal states (latent) and actions (observable state). Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically. Drawing inspiration from theories in Cognitive Science, we propose a novel mult","authors_text":"Mathieu Chollet, Tanaya Guha, Tongfei Bian","cross_cats":["cs.RO"],"headline":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.HC","submitted_at":"2026-04-13T01:56:00Z","title":"Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.10895","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-10T16:31:37.512050Z","id":"a24df988-e2fa-4dc4-8f7e-d231b8926d92","model_set":{"reader":"grok-4.3"},"one_line_summary":"SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"","strongest_claim":"SocialLDG achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making.","weakest_assumption":"Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically."}},"verdict_id":"a24df988-e2fa-4dc4-8f7e-d231b8926d92"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dfd6ac604dae33a3b331837021f6e54605aa9369ac7b6a38d35f8cc25dbb0cf5","target":"record","created_at":"2026-05-20T00:05:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b47e37e1ea35a4828ccbd433b6d720d3440366b75f18038aca36f0ded60fbca6","cross_cats_sorted":["cs.RO"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.HC","submitted_at":"2026-04-13T01:56:00Z","title_canon_sha256":"c82852f6836d50e8f529841c65f2dbb06258d757fb51cf82b5c4601725ff05f8"},"schema_version":"1.0","source":{"id":"2604.10895","kind":"arxiv","version":3}},"canonical_sha256":"77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792","first_computed_at":"2026-05-20T00:05:44.666074Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:05:44.666074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6Z9bNWWcfMVBQW3H05BfT7ghKKdp9NuERHbf5OWjm0Ps34WeCSpS6OoHlfV4n5//F390sSWPWtl/99H4L6yzDw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:05:44.666593Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.10895","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfd6ac604dae33a3b331837021f6e54605aa9369ac7b6a38d35f8cc25dbb0cf5","sha256:b893dac465d2c4930e95ebbbecf02f5fbed3ca3ccdc0eb7bb33b410ddf10a7f0"],"state_sha256":"894fa1f2b98e14df20b3f2281ac0971cf243040313148ec434a7ba38d9a39778"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3QhmRHJVlDBqpSMbei2N73YYP/OJJAc6j8NlCLib1Bido7UhFGXXDUmWfMj2YEj9HlfGmtP7jUubrsT5mY5dAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:59:36.715049Z","bundle_sha256":"6fd5cc85d6a5a5d7bb58a083ad922de27cc7545808f792a58f566ede89c3d901"}}