{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZZDLTSLYOQEU7TFHY52OJ6CD2R","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":"abf3f7a301dc7b4fb2900d36dfd84aaf3aea58e3c64a36ef48320d616daace2a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-17T16:57:19Z","title_canon_sha256":"b55350882f9b402f272b220fe82daa9796b7671e520295fb2d795219fe234ab5"},"schema_version":"1.0","source":{"id":"2401.09334","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09334","created_at":"2026-07-05T07:34:42Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09334v1","created_at":"2026-07-05T07:34:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09334","created_at":"2026-07-05T07:34:42Z"},{"alias_kind":"pith_short_12","alias_value":"ZZDLTSLYOQEU","created_at":"2026-07-05T07:34:42Z"},{"alias_kind":"pith_short_16","alias_value":"ZZDLTSLYOQEU7TFH","created_at":"2026-07-05T07:34:42Z"},{"alias_kind":"pith_short_8","alias_value":"ZZDLTSLY","created_at":"2026-07-05T07:34:42Z"}],"graph_snapshots":[{"event_id":"sha256:a16af1ab602a3f5882de47d66c1c8d6fb46e1652f457302dbf34050072c33664","target":"graph","created_at":"2026-07-05T07:34:42Z","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/2401.09334/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initial","authors_text":"Jun Wang, Ling Chen, Meng Fang, Mykola Pechenizkiy, Shilong Deng, Yudi Zhang, Zijing Shi","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-17T16:57:19Z","title":"Large Language Models Are Neurosymbolic Reasoners"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09334","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":"sha256:3a55526d1ce8cd0796a79902ac6485f7e90f43151c77294480522ceeed790d33","target":"record","created_at":"2026-07-05T07:34:42Z","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":"abf3f7a301dc7b4fb2900d36dfd84aaf3aea58e3c64a36ef48320d616daace2a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-17T16:57:19Z","title_canon_sha256":"b55350882f9b402f272b220fe82daa9796b7671e520295fb2d795219fe234ab5"},"schema_version":"1.0","source":{"id":"2401.09334","kind":"arxiv","version":1}},"canonical_sha256":"ce46b9c97874094fcca7c774e4f843d45c8c15ae1db8f80db276b8ec22940701","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce46b9c97874094fcca7c774e4f843d45c8c15ae1db8f80db276b8ec22940701","first_computed_at":"2026-07-05T07:34:42.434979Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:34:42.434979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0q1QE6yuySxXNkI5QmuhQ4mf8n3VPPiAjzF5Y7VtMRkkzdJadv81rpgszZCa/dNgb242nsjYgJshu6bZq5FbBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:34:42.435386Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.09334","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a55526d1ce8cd0796a79902ac6485f7e90f43151c77294480522ceeed790d33","sha256:a16af1ab602a3f5882de47d66c1c8d6fb46e1652f457302dbf34050072c33664"],"state_sha256":"8da7f841cff871661fcb256688bfc21f2cf5f498907f5585aad5a489ad0f6e4f"}