{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HTGF7DRLVKTFKJE5SKIWZGGGDD","short_pith_number":"pith:HTGF7DRL","canonical_record":{"source":{"id":"2506.12796","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-15T10:04:42Z","cross_cats_sorted":[],"title_canon_sha256":"21165b8e7d0b363b8c6190bf0678d149754a78502c1994b5c52ecdfff54885de","abstract_canon_sha256":"e41d15b7d20232df7cd4467cd752608992c651f7f75c56a1d4db1e0a4bb12125"},"schema_version":"1.0"},"canonical_sha256":"3ccc5f8e2baaa655249d92916c98c618db65df5888a4ee28ba3aa4392bdd5dab","source":{"kind":"arxiv","id":"2506.12796","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12796","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12796v2","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12796","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_12","alias_value":"HTGF7DRLVKTF","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_16","alias_value":"HTGF7DRLVKTFKJE5","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_8","alias_value":"HTGF7DRL","created_at":"2026-07-05T11:22:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HTGF7DRLVKTFKJE5SKIWZGGGDD","target":"record","payload":{"canonical_record":{"source":{"id":"2506.12796","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-15T10:04:42Z","cross_cats_sorted":[],"title_canon_sha256":"21165b8e7d0b363b8c6190bf0678d149754a78502c1994b5c52ecdfff54885de","abstract_canon_sha256":"e41d15b7d20232df7cd4467cd752608992c651f7f75c56a1d4db1e0a4bb12125"},"schema_version":"1.0"},"canonical_sha256":"3ccc5f8e2baaa655249d92916c98c618db65df5888a4ee28ba3aa4392bdd5dab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:42.265281Z","signature_b64":"wTbSrp1aof+bqkV9EeXWfLC8mCoQ5v3foCdAW8K2JIano3MDfmJ3UBFZguF6nPvBfLQcHVCqDjzmVq6Q+VIhCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ccc5f8e2baaa655249d92916c98c618db65df5888a4ee28ba3aa4392bdd5dab","last_reissued_at":"2026-07-05T11:22:42.264721Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:42.264721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.12796","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":"2026-07-05T11:22:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"srxNVuxvzKWM8ISH+KYjhWDDVWPEMc416gHTmuwIhRyvZSr82R9zLc/ljWyzepQiSLeHyg4V9xUJJgFFuWRVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:09:59.849666Z"},"content_sha256":"7ed9428d4c00d1f3d79dc64ea9b63fed13c10c41a579c7c41c800bd8f28c666c","schema_version":"1.0","event_id":"sha256:7ed9428d4c00d1f3d79dc64ea9b63fed13c10c41a579c7c41c800bd8f28c666c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HTGF7DRLVKTFKJE5SKIWZGGGDD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Surprise Calibration for Better In-Context Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jingrui Hou, Peng Zhu, Ping Wang, Qibiao Hu, Zhihang Tan","submitted_at":"2025-06-15T10:04:42Z","abstract_excerpt":"In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible to biases that arise from prior knowledge and contextual demonstrations, which can degrade the performance of LLMs. Existing bias calibration methods typically apply fixed class priors across all inputs, limiting their efficacy in dynamic ICL settings where the context for each query differs. To address these limitations, we adopt implicit sequential Bayesian inference as a fra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12796","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/2506.12796/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":"2026-07-05T11:22:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lxSsipKaB+QANB2kyTvIwSOyLBHBYJJUeEgsyoo+yaSnH/YfRolM9/I5fPjEiDwTls2GpmQKldTP+EtOZpzZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:09:59.850225Z"},"content_sha256":"d087ca826fe19aa5b8e5c352a2154506af464529eb842d83fd217399fb5026e6","schema_version":"1.0","event_id":"sha256:d087ca826fe19aa5b8e5c352a2154506af464529eb842d83fd217399fb5026e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/bundle.json","state_url":"https://pith.science/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/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":"2026-08-09T01:09:59Z","links":{"resolver":"https://pith.science/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD","bundle":"https://pith.science/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/bundle.json","state":"https://pith.science/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HTGF7DRLVKTFKJE5SKIWZGGGDD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HTGF7DRLVKTFKJE5SKIWZGGGDD","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":"e41d15b7d20232df7cd4467cd752608992c651f7f75c56a1d4db1e0a4bb12125","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-15T10:04:42Z","title_canon_sha256":"21165b8e7d0b363b8c6190bf0678d149754a78502c1994b5c52ecdfff54885de"},"schema_version":"1.0","source":{"id":"2506.12796","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12796","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12796v2","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12796","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_12","alias_value":"HTGF7DRLVKTF","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_16","alias_value":"HTGF7DRLVKTFKJE5","created_at":"2026-07-05T11:22:42Z"},{"alias_kind":"pith_short_8","alias_value":"HTGF7DRL","created_at":"2026-07-05T11:22:42Z"}],"graph_snapshots":[{"event_id":"sha256:d087ca826fe19aa5b8e5c352a2154506af464529eb842d83fd217399fb5026e6","target":"graph","created_at":"2026-07-05T11:22:42Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.12796/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible to biases that arise from prior knowledge and contextual demonstrations, which can degrade the performance of LLMs. Existing bias calibration methods typically apply fixed class priors across all inputs, limiting their efficacy in dynamic ICL settings where the context for each query differs. To address these limitations, we adopt implicit sequential Bayesian inference as a fra","authors_text":"Jingrui Hou, Peng Zhu, Ping Wang, Qibiao Hu, Zhihang Tan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-15T10:04:42Z","title":"Surprise Calibration for Better In-Context Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12796","kind":"arxiv","version":2},"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":"sha256:7ed9428d4c00d1f3d79dc64ea9b63fed13c10c41a579c7c41c800bd8f28c666c","target":"record","created_at":"2026-07-05T11:22:42Z","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":"e41d15b7d20232df7cd4467cd752608992c651f7f75c56a1d4db1e0a4bb12125","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-15T10:04:42Z","title_canon_sha256":"21165b8e7d0b363b8c6190bf0678d149754a78502c1994b5c52ecdfff54885de"},"schema_version":"1.0","source":{"id":"2506.12796","kind":"arxiv","version":2}},"canonical_sha256":"3ccc5f8e2baaa655249d92916c98c618db65df5888a4ee28ba3aa4392bdd5dab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ccc5f8e2baaa655249d92916c98c618db65df5888a4ee28ba3aa4392bdd5dab","first_computed_at":"2026-07-05T11:22:42.264721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:42.264721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wTbSrp1aof+bqkV9EeXWfLC8mCoQ5v3foCdAW8K2JIano3MDfmJ3UBFZguF6nPvBfLQcHVCqDjzmVq6Q+VIhCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:42.265281Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.12796","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ed9428d4c00d1f3d79dc64ea9b63fed13c10c41a579c7c41c800bd8f28c666c","sha256:d087ca826fe19aa5b8e5c352a2154506af464529eb842d83fd217399fb5026e6"],"state_sha256":"1ec6130cdef7d5a71c24e352f38d2961c5d0636f60a7eda5748cc51da7e8f9c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W4VWewKbP4oX1zpzZ/Fb7gsH7LuSQP0PzX1y/UHk40ItB93rgUpEWmjC0gK0dcOY3o49vaMWB2IAHrzl8IJqBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:09:59.853997Z","bundle_sha256":"875821633ceef1c8240622898dfdeabc5170381448a5d0208adf24826025a945"}}