{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_03e350f786d795c8375ecc225d498dacbb2a6f21d202dd96e9df1963241e56fe","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_03e350f786d795c8375ecc225d498dacbb2a6f21d202dd96e9df1963241e56fe","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"9e1e7ff44173f768173ca2ce15f496638a0f9fe33aba83d3d8871c845bf828b8","published":"Tue, 28 Jul 2026 00:00:00 -0400","receipt_hash":"9e1e7ff44173f768173ca2ce15f496638a0f9fe33aba83d3d8871c845bf828b8","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"9e1e7ff44173f768173ca2ce15f496638a0f9fe33aba83d3d8871c845bf828b8","observed_at":"2026-07-28T04:43:08.282317Z","parent_run_hash":"23a1ef85134515049ced29518443d084afc46fd7c967741e6c6acdbdbbf29939","published":"Tue, 28 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.22652v1 Announce Type: new \nAbstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA). Existing approaches primarily combine LLMs with KGs through retrieval-augmented generation (RAG)-based, agent-based, and SPARQL-based methods. Although these methods have achieved notable success, they still suffer from several limitations, including structural information loss, unfaithful reasoning, and limited flexibility and generalization. To address these challenges, this paper proposes KG2Code, a novel approach that transforms knowledge graphs into a code-based representation, preserving structural semantics while naturally aligning with the code-aware pretraining of modern LLMs. Based on KG2Code, KG2Code-QA is further introduced as a KGQA framework that formulates KGQA as a code generation task. This for","title":"KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering","url":"https://arxiv.org/abs/2607.22652","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.22652v1 Announce Type: new \nAbstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA). Existing approaches primarily combine LLMs with KGs through retrieval-augmented generation (RAG)-based, agent-based, and SPARQL-based methods. Although these methods have achieved notable success, they still suffer from several limitations, including structural information loss, unfaithful reasoning, and limited flexibility and generalization. To address these challenges, this paper proposes KG2Code, a novel approach that transforms knowledge graphs into a code-based representation, preserving structural semantics while naturally aligning with the code-aware pretraining of modern LLMs. Based on KG2Code, KG2Code-QA is further introduced as a KGQA framework that formulates KGQA as a code generation task. This for","title":"KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-28T04:43:08Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.22652"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:3efbf9d88d9483ebc63dffd45d8e5d2a32a7802af4efd3838cd6cded9d0dcf8e4a5095d53266cb7b771f2a7fac572b706d7228a6bd355956ddb51398c3585408","signer":"crovia.substrate","subject":{"observed_at":"2026-07-28T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.22652"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"554b29c7338bab73aa7abcdfcd945d65b9c0ca7f6757f1c9bb40c9c9c2642e41","leaf_index":360366,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"6a6c0338f4af2b9cfb51aa1d1033464c7ed9aa514685d64897050db6f73631ab","side":"right"},{"sibling":"f9a3c6c74cd201e325f84929802eaa1e73bddf588d718b02d23f85ab35958ace","side":"left"},{"sibling":"a6e3a597a1b905b54359354862a999d499a882b06310996df314a1733b24a6e8","side":"left"},{"sibling":"1498e9c396911fac4bbc49e29b20ad4ff5dee5104e2281946b3130f016c14ace","side":"left"},{"sibling":"8e61571c2e7965fe958bf3041495b21b1c16f123f46afbf1db022c614f67130c","side":"right"},{"sibling":"2c87e5a910c5dcf9ab7bb899eaf82222f3503a24a3dea73c210014c194f6dd0a","side":"left"},{"sibling":"48dace931609a55569e4aa242be3eefac99c722b35aa9345c111116089b4e976","side":"right"},{"sibling":"ce393210d8cb9e0705a24ff80bd76d7becf22bfce1258f4a57fd2dbdca17218b","side":"left"},{"sibling":"8fe957ea378915d410087f8b2c41171bdfdac25ba34c42d93c5aaa835f32246c","side":"left"},{"sibling":"f57ebea8749a327b4323e9ad9c906a9400f5a5d8c83084dfc7d80be83bd44007","side":"left"},{"sibling":"4424ff61f20c9cef2251672611ec86369d87b1608ab089738930fd3d52e0dd54","side":"left"},{"sibling":"8db22b6d8b4004df8ccd40f648d9fd8a62f564821c80fbfd9c743849f4102e1b","side":"left"},{"sibling":"cff500acb83b14a8a7195a72d90dd4b7a6b8a28fc1069f8300b1191728718f90","side":"left"},{"sibling":"51ee2566d84aba54b9d07233fb460f9fab044b4edfa3fbe376fa1df0727acfa5","side":"left"},{"sibling":"f3e45bceed774d2402fa45d41ff5190f295823bd2f216eb90157884150034693","side":"left"},{"sibling":"2096cd69b54e283ddc45b26b63564a5e4c02f303ffd855b3b3533bcf26bc284d","side":"right"},{"sibling":"77025bcb374a7ad74f520643e20a8ae1205a7ee78507b0beb93117760f1c29d3","side":"left"},{"sibling":"3b50864499c874394ea0928567747666eaf59b01380e46cd52164ec5acec0f71","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":361008,"merkle_root":"3065e8369ea437c06beba806dc4e4bb159979adeb21fe632242c1906a7204647","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260728T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-28T05:38:48Z","sig_algorithm":"ed25519","signature":"9141644407577a82611c1579110f667de2d46dc6b93c6322edf26f4c3056ea99f0e56502853908e30d87c38bcf95eb6e0ab5130525aa51505bd6f61938120609","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_03e350f786d795c8375ecc225d498dacbb2a6f21d202dd96e9df1963241e56fe"}}