{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2PWUEHUSHOVQ2VKTOTTBE2WD4N","short_pith_number":"pith:2PWUEHUS","canonical_record":{"source":{"id":"2311.06668","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T21:19:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7689219bdae9fbfcd63b9cfbf83ff0b07a1189457c475345fa0ea29af2759d1b","abstract_canon_sha256":"9984d657a37d19a0e8de008f2c0cb0b680401f422aedadee1df555a3e3687cc4"},"schema_version":"1.0"},"canonical_sha256":"d3ed421e923bab0d555374e6126ac3e3596416386c5054de1472c7f42a8ec176","source":{"kind":"arxiv","id":"2311.06668","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06668","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06668v3","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06668","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_12","alias_value":"2PWUEHUSHOVQ","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_16","alias_value":"2PWUEHUSHOVQ2VKT","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_8","alias_value":"2PWUEHUS","created_at":"2026-07-05T07:44:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2PWUEHUSHOVQ2VKTOTTBE2WD4N","target":"record","payload":{"canonical_record":{"source":{"id":"2311.06668","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T21:19:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"7689219bdae9fbfcd63b9cfbf83ff0b07a1189457c475345fa0ea29af2759d1b","abstract_canon_sha256":"9984d657a37d19a0e8de008f2c0cb0b680401f422aedadee1df555a3e3687cc4"},"schema_version":"1.0"},"canonical_sha256":"d3ed421e923bab0d555374e6126ac3e3596416386c5054de1472c7f42a8ec176","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:44:57.963543Z","signature_b64":"OvY2FwoMGvj+j1tqMoTw1sX0qSZWpdHCHVaUrDm513ECNPsmMEs1gOSCiQCWjjzf2PGIoaFBJ5RtCv1AuMzbBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3ed421e923bab0d555374e6126ac3e3596416386c5054de1472c7f42a8ec176","last_reissued_at":"2026-07-05T07:44:57.963060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:44:57.963060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.06668","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":"2026-07-05T07:44:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E9JBMXllvItVAPqNJlGsLH6VLT0qa4AwYWu7Wp4Rz6qbIoL0LrnpCpupaofI3gN2b6U3o1rGX1ARBXT7tlInDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T18:04:27.901715Z"},"content_sha256":"31de48598d9b31f2b107ef3e2741cf54427cf6dd62edbdd38e1ccbf4c31d98bb","schema_version":"1.0","event_id":"sha256:31de48598d9b31f2b107ef3e2741cf54427cf6dd62edbdd38e1ccbf4c31d98bb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2PWUEHUSHOVQ2VKTOTTBE2WD4N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Haotian Ye, James Zou, Lei Xing, Sheng Liu","submitted_at":"2023-11-11T21:19:44Z","abstract_excerpt":"Large language models (LLMs) demonstrate emergent in-context learning capabilities, where they adapt to new tasks based on example demonstrations. However, in-context learning has seen limited effectiveness in many settings, is difficult to quantitatively control and takes up context window space. To overcome these limitations, we propose an alternative approach that recasts in-context learning as in-context vectors (ICV). Using ICV has two steps. We first use a forward pass on demonstration examples to create the in-context vector from the latent embedding of the LLM. This vector captures ess"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06668","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/2311.06668/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":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:44:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q97ICdaOOlw2lTL3hqn2A5On3KjX83yn8n8qnybdPN/xdE1h6xFoxfPvhTui43YF0tSiQwPvqBXgapAISgfuCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-12T18:04:27.902085Z"},"content_sha256":"44d91b37673f3ac608f032dd1d2d59d0cac88034c4eb8c91b64b980869392261","schema_version":"1.0","event_id":"sha256:44d91b37673f3ac608f032dd1d2d59d0cac88034c4eb8c91b64b980869392261"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/bundle.json","state_url":"https://pith.science/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/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":"2026-07-12T18:04:27Z","links":{"resolver":"https://pith.science/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N","bundle":"https://pith.science/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/bundle.json","state":"https://pith.science/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2PWUEHUSHOVQ2VKTOTTBE2WD4N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2PWUEHUSHOVQ2VKTOTTBE2WD4N","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":"9984d657a37d19a0e8de008f2c0cb0b680401f422aedadee1df555a3e3687cc4","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T21:19:44Z","title_canon_sha256":"7689219bdae9fbfcd63b9cfbf83ff0b07a1189457c475345fa0ea29af2759d1b"},"schema_version":"1.0","source":{"id":"2311.06668","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.06668","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"arxiv_version","alias_value":"2311.06668v3","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06668","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_12","alias_value":"2PWUEHUSHOVQ","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_16","alias_value":"2PWUEHUSHOVQ2VKT","created_at":"2026-07-05T07:44:57Z"},{"alias_kind":"pith_short_8","alias_value":"2PWUEHUS","created_at":"2026-07-05T07:44:57Z"}],"graph_snapshots":[{"event_id":"sha256:44d91b37673f3ac608f032dd1d2d59d0cac88034c4eb8c91b64b980869392261","target":"graph","created_at":"2026-07-05T07:44:57Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.06668/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) demonstrate emergent in-context learning capabilities, where they adapt to new tasks based on example demonstrations. However, in-context learning has seen limited effectiveness in many settings, is difficult to quantitatively control and takes up context window space. To overcome these limitations, we propose an alternative approach that recasts in-context learning as in-context vectors (ICV). Using ICV has two steps. We first use a forward pass on demonstration examples to create the in-context vector from the latent embedding of the LLM. This vector captures ess","authors_text":"Haotian Ye, James Zou, Lei Xing, Sheng Liu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T21:19:44Z","title":"In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06668","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:31de48598d9b31f2b107ef3e2741cf54427cf6dd62edbdd38e1ccbf4c31d98bb","target":"record","created_at":"2026-07-05T07:44:57Z","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":"9984d657a37d19a0e8de008f2c0cb0b680401f422aedadee1df555a3e3687cc4","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-11T21:19:44Z","title_canon_sha256":"7689219bdae9fbfcd63b9cfbf83ff0b07a1189457c475345fa0ea29af2759d1b"},"schema_version":"1.0","source":{"id":"2311.06668","kind":"arxiv","version":3}},"canonical_sha256":"d3ed421e923bab0d555374e6126ac3e3596416386c5054de1472c7f42a8ec176","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3ed421e923bab0d555374e6126ac3e3596416386c5054de1472c7f42a8ec176","first_computed_at":"2026-07-05T07:44:57.963060Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:44:57.963060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OvY2FwoMGvj+j1tqMoTw1sX0qSZWpdHCHVaUrDm513ECNPsmMEs1gOSCiQCWjjzf2PGIoaFBJ5RtCv1AuMzbBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:44:57.963543Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.06668","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:31de48598d9b31f2b107ef3e2741cf54427cf6dd62edbdd38e1ccbf4c31d98bb","sha256:44d91b37673f3ac608f032dd1d2d59d0cac88034c4eb8c91b64b980869392261"],"state_sha256":"7842433518b00f781d4da2def1f68db664ee7e1e2487156daa591e900ff8c46c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d679H1bMTR9bfiKHxygiXLFNtGT/8HmE7WZwghXZbFpHNU/VnLn+9PiXNsSeOUN4FKwdXywMVvxLDaQMivrkBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-12T18:04:27.904436Z","bundle_sha256":"978e7b8ebc528ab836d1c2577b0cffec6b988266a6b8145369244ee188af1d9d"}}