{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EIJ4E3EHEQ7HG635AM6BR43734","short_pith_number":"pith:EIJ4E3EH","canonical_record":{"source":{"id":"2401.03385","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-07T04:32:29Z","cross_cats_sorted":[],"title_canon_sha256":"5e66259645111ec8212a200d5af714dd65b8c3488f08c075945b3e1207ac47d7","abstract_canon_sha256":"a569206ae10a6863de8352ff80566eb3b5ec638543f6973b985fee0c701ade2b"},"schema_version":"1.0"},"canonical_sha256":"2213c26c87243e737b7d033c18f37fdf22f2ac1728f7d93ee91002afc488d34d","source":{"kind":"arxiv","id":"2401.03385","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.03385","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"arxiv_version","alias_value":"2401.03385v2","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03385","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_12","alias_value":"EIJ4E3EHEQ7H","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"EIJ4E3EHEQ7HG635","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"EIJ4E3EH","created_at":"2026-07-05T07:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EIJ4E3EHEQ7HG635AM6BR43734","target":"record","payload":{"canonical_record":{"source":{"id":"2401.03385","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-07T04:32:29Z","cross_cats_sorted":[],"title_canon_sha256":"5e66259645111ec8212a200d5af714dd65b8c3488f08c075945b3e1207ac47d7","abstract_canon_sha256":"a569206ae10a6863de8352ff80566eb3b5ec638543f6973b985fee0c701ade2b"},"schema_version":"1.0"},"canonical_sha256":"2213c26c87243e737b7d033c18f37fdf22f2ac1728f7d93ee91002afc488d34d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:31:56.852505Z","signature_b64":"MCwW4iDTg2XEyz2wEg57lkHNTMjSQV8S/V+6Cr379Mh2M79ise24rNkY3GXKJFoQcKQDcx/R4lF4Rp9gYg/TBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2213c26c87243e737b7d033c18f37fdf22f2ac1728f7d93ee91002afc488d34d","last_reissued_at":"2026-07-05T07:31:56.852125Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:31:56.852125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.03385","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":"2026-07-05T07:31:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zly7SY65oDYpA3gI6zn3OGHcHEtL/ZsTRE6320NLxhPgodKmhwwm8RNyqfOcYD4HW7VYmIlqKMz/Lw/rrhw9Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:03:30.405616Z"},"content_sha256":"8284bcad9f2328696620eafb0409b8d7b93f28ed086cf561521c7682a4e8f914","schema_version":"1.0","event_id":"sha256:8284bcad9f2328696620eafb0409b8d7b93f28ed086cf561521c7682a4e8f914"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EIJ4E3EHEQ7HG635AM6BR43734","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Grimoire is All You Need for Enhancing Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bo Tang, Ding Chen, Feiyu Xiong, Qingchen Yu, Shichao Song, Wenjin Wang, Zhiyu Li","submitted_at":"2024-01-07T04:32:29Z","abstract_excerpt":"In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the ICL capability of different types of models shows significant variation due to factors such as model architecture, volume of learning data, and the size of parameters. Generally, the larger the model's parameter size and the more extensive the learning data, the stronger its ICL capability. In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03385","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/2401.03385/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":"2026-07-05T07:31:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I9n7f2zIq73dK780v2K7DuFICkllP7/9nsrz9gbMavVHPUtozseWMg2bfCCQ12S4JNYNzoLfVwPuQcKdFaRIAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:03:30.406148Z"},"content_sha256":"9f1e63934feee1a9ba0aca168c1773e993b01defe7bfff8d79cf4908c1fbfdcd","schema_version":"1.0","event_id":"sha256:9f1e63934feee1a9ba0aca168c1773e993b01defe7bfff8d79cf4908c1fbfdcd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EIJ4E3EHEQ7HG635AM6BR43734/bundle.json","state_url":"https://pith.science/pith/EIJ4E3EHEQ7HG635AM6BR43734/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EIJ4E3EHEQ7HG635AM6BR43734/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":"2026-08-19T07:03:30Z","links":{"resolver":"https://pith.science/pith/EIJ4E3EHEQ7HG635AM6BR43734","bundle":"https://pith.science/pith/EIJ4E3EHEQ7HG635AM6BR43734/bundle.json","state":"https://pith.science/pith/EIJ4E3EHEQ7HG635AM6BR43734/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EIJ4E3EHEQ7HG635AM6BR43734/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EIJ4E3EHEQ7HG635AM6BR43734","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":"a569206ae10a6863de8352ff80566eb3b5ec638543f6973b985fee0c701ade2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-07T04:32:29Z","title_canon_sha256":"5e66259645111ec8212a200d5af714dd65b8c3488f08c075945b3e1207ac47d7"},"schema_version":"1.0","source":{"id":"2401.03385","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.03385","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"arxiv_version","alias_value":"2401.03385v2","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03385","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_12","alias_value":"EIJ4E3EHEQ7H","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"EIJ4E3EHEQ7HG635","created_at":"2026-07-05T07:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"EIJ4E3EH","created_at":"2026-07-05T07:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:9f1e63934feee1a9ba0aca168c1773e993b01defe7bfff8d79cf4908c1fbfdcd","target":"graph","created_at":"2026-07-05T07:31:56Z","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/2401.03385/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the ICL capability of different types of models shows significant variation due to factors such as model architecture, volume of learning data, and the size of parameters. Generally, the larger the model's parameter size and the more extensive the learning data, the stronger its ICL capability. In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing ","authors_text":"Bo Tang, Ding Chen, Feiyu Xiong, Qingchen Yu, Shichao Song, Wenjin Wang, Zhiyu Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-07T04:32:29Z","title":"Grimoire is All You Need for Enhancing Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03385","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":"sha256:8284bcad9f2328696620eafb0409b8d7b93f28ed086cf561521c7682a4e8f914","target":"record","created_at":"2026-07-05T07:31:56Z","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":"a569206ae10a6863de8352ff80566eb3b5ec638543f6973b985fee0c701ade2b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-07T04:32:29Z","title_canon_sha256":"5e66259645111ec8212a200d5af714dd65b8c3488f08c075945b3e1207ac47d7"},"schema_version":"1.0","source":{"id":"2401.03385","kind":"arxiv","version":2}},"canonical_sha256":"2213c26c87243e737b7d033c18f37fdf22f2ac1728f7d93ee91002afc488d34d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2213c26c87243e737b7d033c18f37fdf22f2ac1728f7d93ee91002afc488d34d","first_computed_at":"2026-07-05T07:31:56.852125Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:31:56.852125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MCwW4iDTg2XEyz2wEg57lkHNTMjSQV8S/V+6Cr379Mh2M79ise24rNkY3GXKJFoQcKQDcx/R4lF4Rp9gYg/TBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:31:56.852505Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.03385","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8284bcad9f2328696620eafb0409b8d7b93f28ed086cf561521c7682a4e8f914","sha256:9f1e63934feee1a9ba0aca168c1773e993b01defe7bfff8d79cf4908c1fbfdcd"],"state_sha256":"5cb6be37f101b254b4e3d84f6c6ce80f8525be5836ccc29af9d137f11d5eac10"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ghtW0CDMmsY4uSiFxzFunbZ6E5ZRboxDkE9pWhEtZiRdckFv4bvMdK6gRTV1LC++OCefXWrGiG4ijyDDejh3CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T07:03:30.411236Z","bundle_sha256":"ff0e85a500d7ea804ad8985abaeeb33462f4fc725b44fa2832eb88949a883502"}}