{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:W6MJRLPB7VYH76IUDJNXHTWB4Y","short_pith_number":"pith:W6MJRLPB","canonical_record":{"source":{"id":"2404.11216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-17T10:00:56Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"fd695ac50640804781cbbdd7bb99bc4197f9555030425c57040f279f963eb996","abstract_canon_sha256":"f90a903d1ce8b5c1236dd661476de62690314d96eb63d3256efa8218d9b46288"},"schema_version":"1.0"},"canonical_sha256":"b79898ade1fd707ff9141a5b73cec1e6363de5c22793ba52b1dcfe2395e661bf","source":{"kind":"arxiv","id":"2404.11216","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.11216","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"arxiv_version","alias_value":"2404.11216v2","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11216","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_12","alias_value":"W6MJRLPB7VYH","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_16","alias_value":"W6MJRLPB7VYH76IU","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_8","alias_value":"W6MJRLPB","created_at":"2026-07-05T09:23:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:W6MJRLPB7VYH76IUDJNXHTWB4Y","target":"record","payload":{"canonical_record":{"source":{"id":"2404.11216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-17T10:00:56Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"fd695ac50640804781cbbdd7bb99bc4197f9555030425c57040f279f963eb996","abstract_canon_sha256":"f90a903d1ce8b5c1236dd661476de62690314d96eb63d3256efa8218d9b46288"},"schema_version":"1.0"},"canonical_sha256":"b79898ade1fd707ff9141a5b73cec1e6363de5c22793ba52b1dcfe2395e661bf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:48.991059Z","signature_b64":"Eoi2rX47qOndF0a4qf4TEjXXlfTzcQMr+pdDfhKEmMDo4WOBVUCbh4kp/GcTP1fwSEmujFx4vs78EkQ2GE16AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b79898ade1fd707ff9141a5b73cec1e6363de5c22793ba52b1dcfe2395e661bf","last_reissued_at":"2026-07-05T09:23:48.990521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:48.990521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.11216","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":"2026-07-05T09:23:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3IzTcS1wsWQZjJTID5y03h4V36JdbDb4mm4LKCP5yL3ya0nD4SjSNcZCJbvRnr3zcmPUyVCmYM2dh352xhToDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:58:19.025354Z"},"content_sha256":"4c7f08079c9abc42e54cf7ed609c160bc07f61d7b7def04131ff5ea4fdab2d06","schema_version":"1.0","event_id":"sha256:4c7f08079c9abc42e54cf7ed609c160bc07f61d7b7def04131ff5ea4fdab2d06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:W6MJRLPB7VYH76IUDJNXHTWB4Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Position Engineering: Boosting Large Language Models through Positional Information Manipulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Huiqiang Jiang, Lili Qiu, Luna Qiu, Yuqing Yang, Zhiyuan He, Zilong Wang","submitted_at":"2024-04-17T10:00:56Z","abstract_excerpt":"The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the prompt text to enhance task performance. In this paper, we introduce a novel technique termed position engineering, which offers a more efficient way to guide large language models. Unlike prompt engineering, which requires substantial effort to modify the text provided to LLMs, position engineering merely involves altering the positional information in the prompt without modifyi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11216","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/2404.11216/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":"2026-07-05T09:23:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UcaESq7IJcKvHpwN/sm2x3/qPcSexjP7YPfNb1RLOcXdowMGFTfsroxKhFlM2qsmIgUHPQh0zO1xCi91wK33DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T23:58:19.026244Z"},"content_sha256":"1f3c90483c803b6dff554b3e73ebece0806372238e218ff310ed9041a0425b6a","schema_version":"1.0","event_id":"sha256:1f3c90483c803b6dff554b3e73ebece0806372238e218ff310ed9041a0425b6a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/bundle.json","state_url":"https://pith.science/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/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":"2026-08-05T23:58:19Z","links":{"resolver":"https://pith.science/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y","bundle":"https://pith.science/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/bundle.json","state":"https://pith.science/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W6MJRLPB7VYH76IUDJNXHTWB4Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:W6MJRLPB7VYH76IUDJNXHTWB4Y","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":"f90a903d1ce8b5c1236dd661476de62690314d96eb63d3256efa8218d9b46288","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-17T10:00:56Z","title_canon_sha256":"fd695ac50640804781cbbdd7bb99bc4197f9555030425c57040f279f963eb996"},"schema_version":"1.0","source":{"id":"2404.11216","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.11216","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"arxiv_version","alias_value":"2404.11216v2","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11216","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_12","alias_value":"W6MJRLPB7VYH","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_16","alias_value":"W6MJRLPB7VYH76IU","created_at":"2026-07-05T09:23:48Z"},{"alias_kind":"pith_short_8","alias_value":"W6MJRLPB","created_at":"2026-07-05T09:23:48Z"}],"graph_snapshots":[{"event_id":"sha256:1f3c90483c803b6dff554b3e73ebece0806372238e218ff310ed9041a0425b6a","target":"graph","created_at":"2026-07-05T09:23:48Z","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/2404.11216/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the prompt text to enhance task performance. In this paper, we introduce a novel technique termed position engineering, which offers a more efficient way to guide large language models. Unlike prompt engineering, which requires substantial effort to modify the text provided to LLMs, position engineering merely involves altering the positional information in the prompt without modifyi","authors_text":"Huiqiang Jiang, Lili Qiu, Luna Qiu, Yuqing Yang, Zhiyuan He, Zilong Wang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-17T10:00:56Z","title":"Position Engineering: Boosting Large Language Models through Positional Information Manipulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11216","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":"sha256:4c7f08079c9abc42e54cf7ed609c160bc07f61d7b7def04131ff5ea4fdab2d06","target":"record","created_at":"2026-07-05T09:23:48Z","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":"f90a903d1ce8b5c1236dd661476de62690314d96eb63d3256efa8218d9b46288","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-17T10:00:56Z","title_canon_sha256":"fd695ac50640804781cbbdd7bb99bc4197f9555030425c57040f279f963eb996"},"schema_version":"1.0","source":{"id":"2404.11216","kind":"arxiv","version":2}},"canonical_sha256":"b79898ade1fd707ff9141a5b73cec1e6363de5c22793ba52b1dcfe2395e661bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b79898ade1fd707ff9141a5b73cec1e6363de5c22793ba52b1dcfe2395e661bf","first_computed_at":"2026-07-05T09:23:48.990521Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:48.990521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Eoi2rX47qOndF0a4qf4TEjXXlfTzcQMr+pdDfhKEmMDo4WOBVUCbh4kp/GcTP1fwSEmujFx4vs78EkQ2GE16AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:48.991059Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.11216","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c7f08079c9abc42e54cf7ed609c160bc07f61d7b7def04131ff5ea4fdab2d06","sha256:1f3c90483c803b6dff554b3e73ebece0806372238e218ff310ed9041a0425b6a"],"state_sha256":"c430bf398c61a039506c48b4386c823f3940843c2ad83376eda608534f8f3f3b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NDwm1MrbNI4EgmrhEd1zlss3soR4bkTBrSuwWD5e44N+swRZhMYQcVw8ODTkZItfo7WLz+97s3M+DH3BbV+BDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T23:58:19.033177Z","bundle_sha256":"b7371169c6a33babd41eca8ab8c7f69dcec8a4a226b8c695af997bcc9b68188e"}}