{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2MWGHTDLMZHHD4ESRNSS7LUCCC","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":"3b7f973ffc1d512f2aff2f72ec72872acd268cb327d9e253b4f10fc1949cfcf4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-09T17:22:42Z","title_canon_sha256":"0e3ab6f5f6b8210e920daa2ffd728caa1707353c92719c6f45163be709038b40"},"schema_version":"1.0","source":{"id":"2604.08479","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.08479","created_at":"2026-06-09T02:08:42Z"},{"alias_kind":"arxiv_version","alias_value":"2604.08479v2","created_at":"2026-06-09T02:08:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.08479","created_at":"2026-06-09T02:08:42Z"},{"alias_kind":"pith_short_12","alias_value":"2MWGHTDLMZHH","created_at":"2026-06-09T02:08:42Z"},{"alias_kind":"pith_short_16","alias_value":"2MWGHTDLMZHHD4ES","created_at":"2026-06-09T02:08:42Z"},{"alias_kind":"pith_short_8","alias_value":"2MWGHTDL","created_at":"2026-06-09T02:08:42Z"}],"graph_snapshots":[{"event_id":"sha256:2b6992f1d90ca5e64f5d4639b986fe799e3fe171c7f713978322df64f7541259","target":"graph","created_at":"2026-06-09T02:08:42Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We discovered a template -- a structured sequence of tactics -- that matches between 83--90% of LLM responses (and 60--83% in a held out sample), and when those are matched, covers 81--92% of the response."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The taxonomy of 10 empathic tactics is a valid, unbiased, and sufficiently complete framework for characterizing both AI and human responses without missing important functional differences or introducing classification bias."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"LLMs generate empathic responses using a predictable template of 10 tactics that matches 83-90% of outputs and covers most of each response, while human responses are more diverse."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Large language models rely on one recurring sequence of empathic tactics in most of their responses."}],"snapshot_sha256":"9142f39187f647b177d04dcb6e2268fd44cb9b6c3898015059118cf019f71ce0"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.08479/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than human-written responses. We suggest a reason for this success: LLMs have learned and consistently deploy a well-liked template for expressing empathy. We develop a taxonomy of 10 empathic language \"tactics\" that include validating someone's feelings and paraphrasing, and apply this taxonomy to characterize the language that people and LLMs produce when writing empathic responses. Across a set of 2 studies comparing a to","authors_text":"Desmond C. Ong, Emma S. Gueorguieva, Hongli Zhan, Javier Hernandez, Jina Suh, Junyi Jessy Li, Tatiana Lau","cross_cats":[],"headline":"Large language models rely on one recurring sequence of empathic tactics in most of their responses.","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-09T17:22:42Z","title":"AI generates well-liked but templatic empathic responses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.08479","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T17:54:01.263620Z","id":"3d1561bb-a2a0-4239-a298-c19586f40a1f","model_set":{"reader":"grok-4.3"},"one_line_summary":"LLMs generate empathic responses using a predictable template of 10 tactics that matches 83-90% of outputs and covers most of each response, while human responses are more diverse.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Large language models rely on one recurring sequence of empathic tactics in most of their responses.","strongest_claim":"We discovered a template -- a structured sequence of tactics -- that matches between 83--90% of LLM responses (and 60--83% in a held out sample), and when those are matched, covers 81--92% of the response.","weakest_assumption":"The taxonomy of 10 empathic tactics is a valid, unbiased, and sufficiently complete framework for characterizing both AI and human responses without missing important functional differences or introducing classification bias."}},"verdict_id":"3d1561bb-a2a0-4239-a298-c19586f40a1f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dc1dcc4a532e116fcd6f693820f05fd88a31c147463c6e49ab7830e2264da93a","target":"record","created_at":"2026-06-09T02:08:42Z","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":"3b7f973ffc1d512f2aff2f72ec72872acd268cb327d9e253b4f10fc1949cfcf4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-04-09T17:22:42Z","title_canon_sha256":"0e3ab6f5f6b8210e920daa2ffd728caa1707353c92719c6f45163be709038b40"},"schema_version":"1.0","source":{"id":"2604.08479","kind":"arxiv","version":2}},"canonical_sha256":"d32c63cc6b664e71f0928b652fae82109824c8d023f828c0f8dc226b3c7c8585","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d32c63cc6b664e71f0928b652fae82109824c8d023f828c0f8dc226b3c7c8585","first_computed_at":"2026-06-09T02:08:42.015100Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:08:42.015100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EVoe44hKSWrCNfqbV07q8U5RR9d7VCRd5k+9ljQOsIMNYNyKCRpVzkKYDeVhbTflSJrfIquxhJZnv/KTN6fCBA==","signature_status":"signed_v1","signed_at":"2026-06-09T02:08:42.016012Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.08479","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc1dcc4a532e116fcd6f693820f05fd88a31c147463c6e49ab7830e2264da93a","sha256:2b6992f1d90ca5e64f5d4639b986fe799e3fe171c7f713978322df64f7541259"],"state_sha256":"672c6b7aa4f46fdd877030df3a50178d4585a4937a16d989de980ed0f2b6a13e"}