{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GJFIT6CCJDKLJZDOLIOXFASTNG","short_pith_number":"pith:GJFIT6CC","canonical_record":{"source":{"id":"2405.17893","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T07:13:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3247a64b50eb9b89a961d025a79a462cef20d547dd05ef163fc9ca4499290249","abstract_canon_sha256":"f55a71945356ca253aca1cd7b862899170bfb54c23058c4a04cc7475eac2315d"},"schema_version":"1.0"},"canonical_sha256":"324a89f84248d4b4e46e5a1d72825369b2b14c18a0efffd96f446ab8db6e3c4f","source":{"kind":"arxiv","id":"2405.17893","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17893","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17893v1","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17893","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_12","alias_value":"GJFIT6CCJDKL","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_16","alias_value":"GJFIT6CCJDKLJZDO","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_8","alias_value":"GJFIT6CC","created_at":"2026-07-05T08:24:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GJFIT6CCJDKLJZDOLIOXFASTNG","target":"record","payload":{"canonical_record":{"source":{"id":"2405.17893","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T07:13:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3247a64b50eb9b89a961d025a79a462cef20d547dd05ef163fc9ca4499290249","abstract_canon_sha256":"f55a71945356ca253aca1cd7b862899170bfb54c23058c4a04cc7475eac2315d"},"schema_version":"1.0"},"canonical_sha256":"324a89f84248d4b4e46e5a1d72825369b2b14c18a0efffd96f446ab8db6e3c4f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:12.557884Z","signature_b64":"xUd474CxyoN4AvC6YDoIo6QkCMDKfAdZ+8mL8tMV7kH7xPQSr5B3JTIFHdHkDdb5CXsqnpY4TsgrKxMm+vSeDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"324a89f84248d4b4e46e5a1d72825369b2b14c18a0efffd96f446ab8db6e3c4f","last_reissued_at":"2026-07-05T08:24:12.557419Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:12.557419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.17893","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:24:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h50CLMA5ixCf7SCtceyq1DUYtXuFrQvPkIB6pKphITFpbm6ZDFjxz1+ouX1LMbU7TZwt0JO+onMYDBlJgWkyDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T11:20:18.588113Z"},"content_sha256":"71de476509a2a8302f82bfc9bb07fc2798a384e1009de93f59ac50c609c56a16","schema_version":"1.0","event_id":"sha256:71de476509a2a8302f82bfc9bb07fc2798a384e1009de93f59ac50c609c56a16"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GJFIT6CCJDKLJZDOLIOXFASTNG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Arithmetic Reasoning with LLM: Prolog Generation & Permutation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bingsen Chen, Xiaocheng Yang, Yik-Cheung Tam","submitted_at":"2024-05-28T07:13:25Z","abstract_excerpt":"Instructing large language models (LLMs) to solve elementary school math problems has shown great success using Chain of Thought (CoT). However, the CoT approach relies on an LLM to generate a sequence of arithmetic calculations which can be prone to cascaded calculation errors. We hypothesize that an LLM should focus on extracting predicates and generating symbolic formulas from the math problem description so that the underlying calculation can be done via an external code interpreter. We investigate using LLM to generate Prolog programs to solve mathematical questions. Experimental results "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17893","kind":"arxiv","version":1},"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/2405.17893/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":"2026-07-05T08:24:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rWN487IcZdlTgJaR2Z9oKZkjieZvO7lmWRA1Hm/mp3eQNEANjB7ToY7CTgwa5oGeyg6GDfqY6XMUyU2FtRJsBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T11:20:18.588519Z"},"content_sha256":"1c001392b77326ad7b0ca9639c45c159d0b61e9523c9177d0f34ed3951327413","schema_version":"1.0","event_id":"sha256:1c001392b77326ad7b0ca9639c45c159d0b61e9523c9177d0f34ed3951327413"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/bundle.json","state_url":"https://pith.science/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/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":"2026-07-11T11:20:18Z","links":{"resolver":"https://pith.science/pith/GJFIT6CCJDKLJZDOLIOXFASTNG","bundle":"https://pith.science/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/bundle.json","state":"https://pith.science/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GJFIT6CCJDKLJZDOLIOXFASTNG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GJFIT6CCJDKLJZDOLIOXFASTNG","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":"f55a71945356ca253aca1cd7b862899170bfb54c23058c4a04cc7475eac2315d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T07:13:25Z","title_canon_sha256":"3247a64b50eb9b89a961d025a79a462cef20d547dd05ef163fc9ca4499290249"},"schema_version":"1.0","source":{"id":"2405.17893","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17893","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17893v1","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17893","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_12","alias_value":"GJFIT6CCJDKL","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_16","alias_value":"GJFIT6CCJDKLJZDO","created_at":"2026-07-05T08:24:12Z"},{"alias_kind":"pith_short_8","alias_value":"GJFIT6CC","created_at":"2026-07-05T08:24:12Z"}],"graph_snapshots":[{"event_id":"sha256:1c001392b77326ad7b0ca9639c45c159d0b61e9523c9177d0f34ed3951327413","target":"graph","created_at":"2026-07-05T08:24:12Z","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/2405.17893/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Instructing large language models (LLMs) to solve elementary school math problems has shown great success using Chain of Thought (CoT). However, the CoT approach relies on an LLM to generate a sequence of arithmetic calculations which can be prone to cascaded calculation errors. We hypothesize that an LLM should focus on extracting predicates and generating symbolic formulas from the math problem description so that the underlying calculation can be done via an external code interpreter. We investigate using LLM to generate Prolog programs to solve mathematical questions. Experimental results ","authors_text":"Bingsen Chen, Xiaocheng Yang, Yik-Cheung Tam","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T07:13:25Z","title":"Arithmetic Reasoning with LLM: Prolog Generation & Permutation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17893","kind":"arxiv","version":1},"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":"sha256:71de476509a2a8302f82bfc9bb07fc2798a384e1009de93f59ac50c609c56a16","target":"record","created_at":"2026-07-05T08:24:12Z","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":"f55a71945356ca253aca1cd7b862899170bfb54c23058c4a04cc7475eac2315d","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-28T07:13:25Z","title_canon_sha256":"3247a64b50eb9b89a961d025a79a462cef20d547dd05ef163fc9ca4499290249"},"schema_version":"1.0","source":{"id":"2405.17893","kind":"arxiv","version":1}},"canonical_sha256":"324a89f84248d4b4e46e5a1d72825369b2b14c18a0efffd96f446ab8db6e3c4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"324a89f84248d4b4e46e5a1d72825369b2b14c18a0efffd96f446ab8db6e3c4f","first_computed_at":"2026-07-05T08:24:12.557419Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:24:12.557419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xUd474CxyoN4AvC6YDoIo6QkCMDKfAdZ+8mL8tMV7kH7xPQSr5B3JTIFHdHkDdb5CXsqnpY4TsgrKxMm+vSeDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:24:12.557884Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17893","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71de476509a2a8302f82bfc9bb07fc2798a384e1009de93f59ac50c609c56a16","sha256:1c001392b77326ad7b0ca9639c45c159d0b61e9523c9177d0f34ed3951327413"],"state_sha256":"aa6c4e85e9d0193094c65c3385c7b85720af5039b89b84d4fa652e28118cfc87"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FXcsi4kepk3AXZ/NZyiWqAVqoQHpIR0gC29RrorbtO9hB8U/OU56YC9MlPjAFkwIeasWN/7L9VQzRaYpORgTBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-11T11:20:18.590704Z","bundle_sha256":"409459b880d0ea726a5659112a9efbd014c860d251e91487d04b50edd9406f95"}}