{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:CTCTZR272KPYZQIDXR4SA7FHXL","short_pith_number":"pith:CTCTZR27","canonical_record":{"source":{"id":"2604.19971","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-21T20:28:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ceb430056b726cd5f07835f79f76e15961b2dda24222df7eb5329256e34181da","abstract_canon_sha256":"ca4c21d3d46c9dae1f13828422266a5811d3fac5ea0706ad45604f6fcc5033f1"},"schema_version":"1.0"},"canonical_sha256":"14c53cc75fd29f8cc103bc79207ca7bae94ac3616064e5622896a1e0df437e16","source":{"kind":"arxiv","id":"2604.19971","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.19971","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"2604.19971v2","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.19971","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"CTCTZR272KPY","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_16","alias_value":"CTCTZR272KPYZQID","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_8","alias_value":"CTCTZR27","created_at":"2026-06-30T01:17:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:CTCTZR272KPYZQIDXR4SA7FHXL","target":"record","payload":{"canonical_record":{"source":{"id":"2604.19971","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-21T20:28:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ceb430056b726cd5f07835f79f76e15961b2dda24222df7eb5329256e34181da","abstract_canon_sha256":"ca4c21d3d46c9dae1f13828422266a5811d3fac5ea0706ad45604f6fcc5033f1"},"schema_version":"1.0"},"canonical_sha256":"14c53cc75fd29f8cc103bc79207ca7bae94ac3616064e5622896a1e0df437e16","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:38.551571Z","signature_b64":"+rksL8ovoi9kGLj9hCyrjaE3xviSzGQUCxzKv+E5VMlzUbCEPNJ92y53tzYovR6jbgboLGeBEvVLhFPp6P5ZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14c53cc75fd29f8cc103bc79207ca7bae94ac3616064e5622896a1e0df437e16","last_reissued_at":"2026-06-30T01:17:38.550983Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:38.550983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.19971","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LbTmTIhChRRKTGOLVZ34hvefp8ZhcdLXU+Mm1VKPNlmn76OJk7Dwax0Q1Ro7UN+vOEfgJ7XdAbZ499e4qd8ODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T13:29:21.861465Z"},"content_sha256":"83cc9accba2096526139a41be1827c27e4628e6744f2d60d640b012cbea2d80c","schema_version":"1.0","event_id":"sha256:83cc9accba2096526139a41be1827c27e4628e6744f2d60d640b012cbea2d80c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:CTCTZR272KPYZQIDXR4SA7FHXL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations.","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Chris North, Eric Krokos, Ibrahim Tahmid, Kirsten Whitley, Xuan Wang, Xuxin Tang","submitted_at":"2026-04-21T20:28:42Z","abstract_excerpt":"Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can automate narrative generation from spatial layouts, current collage-based and re-generation methods struggle to support the incremental spatial refinements inherent to the sensemaking process. We identify three critical gaps in existing spatial-textual generation: interaction-revision misalignment, human-LLM intent misalignment, and lack of granular customization. To address these, we introduce Semantic Prompting, a framework for spatial refinement t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study (N=14) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That LLMs can accurately perceive semantic interactions from spatial layouts and reason about refinement intent without persistent human-LLM misalignment, as assumed in the framework design.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Semantic Prompting enables LLMs to perform precise incremental narrative revisions by perceiving semantic interactions in spatial layouts, addressing misalignment gaps in existing methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5beb05b4297fd0433e914e23cf974c05800cd1d29907ee591d3e64ba5a1b2e9d"},"source":{"id":"2604.19971","kind":"arxiv","version":2},"verdict":{"id":"baca82a1-7d05-4d81-9805-542231646bf6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T01:16:43.451059Z","strongest_claim":"The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study (N=14) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering.","one_line_summary":"Semantic Prompting enables LLMs to perform precise incremental narrative revisions by perceiving semantic interactions in spatial layouts, addressing misalignment gaps in existing methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That LLMs can accurately perceive semantic interactions from spatial layouts and reason about refinement intent without persistent human-LLM misalignment, as assumed in the framework design.","pith_extraction_headline":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.19971/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T15:39:46.206286Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T02:29:52.385944Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"06055c0717c1156dfb2ae7c31de476b6a32b64051fd4d33d614339fe3a8fd05c"},"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":"baca82a1-7d05-4d81-9805-542231646bf6"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:17:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"So3w2Yqzw+vR/1ld7hfE93EdlHaQjCb1BgXhRbX9FQ4YTeD5SbTAd3WwiWiVUdW0ybKa6aDf7HBkydsLUz5PAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T13:29:21.861986Z"},"content_sha256":"2c69a117ff4015e3ddcdf6fded2154804e2c03b40b00a319c6900f9fcebc06b9","schema_version":"1.0","event_id":"sha256:2c69a117ff4015e3ddcdf6fded2154804e2c03b40b00a319c6900f9fcebc06b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CTCTZR272KPYZQIDXR4SA7FHXL/bundle.json","state_url":"https://pith.science/pith/CTCTZR272KPYZQIDXR4SA7FHXL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CTCTZR272KPYZQIDXR4SA7FHXL/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":"2026-07-03T13:29:21Z","links":{"resolver":"https://pith.science/pith/CTCTZR272KPYZQIDXR4SA7FHXL","bundle":"https://pith.science/pith/CTCTZR272KPYZQIDXR4SA7FHXL/bundle.json","state":"https://pith.science/pith/CTCTZR272KPYZQIDXR4SA7FHXL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CTCTZR272KPYZQIDXR4SA7FHXL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CTCTZR272KPYZQIDXR4SA7FHXL","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":"ca4c21d3d46c9dae1f13828422266a5811d3fac5ea0706ad45604f6fcc5033f1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-21T20:28:42Z","title_canon_sha256":"ceb430056b726cd5f07835f79f76e15961b2dda24222df7eb5329256e34181da"},"schema_version":"1.0","source":{"id":"2604.19971","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.19971","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"arxiv_version","alias_value":"2604.19971v2","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.19971","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_12","alias_value":"CTCTZR272KPY","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_16","alias_value":"CTCTZR272KPYZQID","created_at":"2026-06-30T01:17:38Z"},{"alias_kind":"pith_short_8","alias_value":"CTCTZR27","created_at":"2026-06-30T01:17:38Z"}],"graph_snapshots":[{"event_id":"sha256:2c69a117ff4015e3ddcdf6fded2154804e2c03b40b00a319c6900f9fcebc06b9","target":"graph","created_at":"2026-06-30T01:17:38Z","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":"The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study (N=14) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That LLMs can accurately perceive semantic interactions from spatial layouts and reason about refinement intent without persistent human-LLM misalignment, as assumed in the framework design."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Semantic Prompting enables LLMs to perform precise incremental narrative revisions by perceiving semantic interactions in spatial layouts, addressing misalignment gaps in existing methods."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations."}],"snapshot_sha256":"5beb05b4297fd0433e914e23cf974c05800cd1d29907ee591d3e64ba5a1b2e9d"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T15:39:46.206286Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-20T02:29:52.385944Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.19971/integrity.json","findings":[],"snapshot_sha256":"06055c0717c1156dfb2ae7c31de476b6a32b64051fd4d33d614339fe3a8fd05c","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can automate narrative generation from spatial layouts, current collage-based and re-generation methods struggle to support the incremental spatial refinements inherent to the sensemaking process. We identify three critical gaps in existing spatial-textual generation: interaction-revision misalignment, human-LLM intent misalignment, and lack of granular customization. To address these, we introduce Semantic Prompting, a framework for spatial refinement t","authors_text":"Chris North, Eric Krokos, Ibrahim Tahmid, Kirsten Whitley, Xuan Wang, Xuxin Tang","cross_cats":["cs.AI"],"headline":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-21T20:28:42Z","title":"Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.19971","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T01:16:43.451059Z","id":"baca82a1-7d05-4d81-9805-542231646bf6","model_set":{"reader":"grok-4.3"},"one_line_summary":"Semantic Prompting enables LLMs to perform precise incremental narrative revisions by perceiving semantic interactions in spatial layouts, addressing misalignment gaps in existing methods.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Semantic Prompting lets LLMs interpret spatial layout changes to make targeted narrative revisions instead of full regenerations.","strongest_claim":"The empirical evaluation demonstrated that S-PRISM effectively enhanced the precision of interaction-revision refinement. A user study (N=14) highlighted how participants leveraged S-PRISM for incremental formalization through interactive steering.","weakest_assumption":"That LLMs can accurately perceive semantic interactions from spatial layouts and reason about refinement intent without persistent human-LLM misalignment, as assumed in the framework design."}},"verdict_id":"baca82a1-7d05-4d81-9805-542231646bf6"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:83cc9accba2096526139a41be1827c27e4628e6744f2d60d640b012cbea2d80c","target":"record","created_at":"2026-06-30T01:17:38Z","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":"ca4c21d3d46c9dae1f13828422266a5811d3fac5ea0706ad45604f6fcc5033f1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-04-21T20:28:42Z","title_canon_sha256":"ceb430056b726cd5f07835f79f76e15961b2dda24222df7eb5329256e34181da"},"schema_version":"1.0","source":{"id":"2604.19971","kind":"arxiv","version":2}},"canonical_sha256":"14c53cc75fd29f8cc103bc79207ca7bae94ac3616064e5622896a1e0df437e16","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14c53cc75fd29f8cc103bc79207ca7bae94ac3616064e5622896a1e0df437e16","first_computed_at":"2026-06-30T01:17:38.550983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:17:38.550983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+rksL8ovoi9kGLj9hCyrjaE3xviSzGQUCxzKv+E5VMlzUbCEPNJ92y53tzYovR6jbgboLGeBEvVLhFPp6P5ZBA==","signature_status":"signed_v1","signed_at":"2026-06-30T01:17:38.551571Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.19971","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83cc9accba2096526139a41be1827c27e4628e6744f2d60d640b012cbea2d80c","sha256:2c69a117ff4015e3ddcdf6fded2154804e2c03b40b00a319c6900f9fcebc06b9"],"state_sha256":"4dd9910c2b1964d8e7526211268007a7c0b7be62bab650e83d93c4da348fd4d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9O8f9CMBkRnOf7GVNdygahwDtdAvH4xLIWQIMjH46sQ0ooS0t6Wa9uuRqwYGMkLssxzBwU0v7wS1HW45bO3KDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T13:29:21.864616Z","bundle_sha256":"4400564a5bfa9d18b28da311d6d16469a530cd19818e67e4507ed7099f3569cd"}}