{"_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_c2d2fdd5da7be5925f889b6ea9bb4cacc544f910ddb4c11a346902db08a6fc7e","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_c2d2fdd5da7be5925f889b6ea9bb4cacc544f910ddb4c11a346902db08a6fc7e","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"5ec0702d87c3009887d5d0400bfa975302d556102f111c7abfee1e283b8c697a","published":"Tue, 28 Jul 2026 00:00:00 -0400","receipt_hash":"5ec0702d87c3009887d5d0400bfa975302d556102f111c7abfee1e283b8c697a","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":"5ec0702d87c3009887d5d0400bfa975302d556102f111c7abfee1e283b8c697a","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:2607.23089v1 Announce Type: new \nAbstract: Recent advances in large language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics. These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs. We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source. Based on this insight, we present our system, a compiler-grounded and hierarchical optimization framework for Triton kernels. the system escalates from lightweight pattern triage and profiling diagnosis to IR attribution and compiler-grounded analysis only when deeper evidence is needed, then proposes evidence-backed source-level rewrites.\n  We implement the system on Triton for Ascen","title":"Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization","url":"https://arxiv.org/abs/2607.23089","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.23089v1 Announce Type: new \nAbstract: Recent advances in large language models (LLMs) have enabled automated kernel generation and optimization, but most existing approaches rely on surface signals such as compilation feedback and profiling metrics. These signals reveal that a kernel is slow, but not why the backend compiler fails to realize a profitable optimization, especially on emerging accelerators such as NPUs. We therefore formulate kernel optimization as a progressive cross-layer diagnosis problem that links runtime symptoms to IR structure and compiler behavior before rewriting source. Based on this insight, we present our system, a compiler-grounded and hierarchical optimization framework for Triton kernels. the system escalates from lightweight pattern triage and profiling diagnosis to IR attribution and compiler-grounded analysis only when deeper evidence is needed, then proposes evidence-backed source-level rewrites.\n  We implement the system on Triton for Ascen","title":"Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization","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/2607.23089"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:1a111cfd83d66c96cd8c3e9d1bfd6749233a5fe8cfa537f3a7b3334d836ef69a672d9a9ed1f057b6d0bc6fef75bebfd33066f5781232c19da048494e24d30e08","signer":"crovia.substrate","subject":{"observed_at":"2026-07-28T04:43:08Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.23089"},"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":"9200d86f427e7d11b5dfdb237cf4b35c5ee465e7b05397a67d44ec3452b3a4d3","leaf_index":360410,"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":"23e029112f621772d32ac936e53874d1a392327b7cb4db2a418f898ba8cbf577","side":"right"},{"sibling":"2f45eb38c4195383050295e05e33c1715bb1333410806c560c1698d85ea94892","side":"left"},{"sibling":"578c6739a1f2e743bf269bd4f0ec85ec1077301a4a0d02e28ff69c9a7ec827b9","side":"right"},{"sibling":"0fb6caf7880d08241fcc468ef43cb4179b48092adc8fb08e5a923baec1b1f8c5","side":"left"},{"sibling":"a8b68441d625277d6f8e8b82ac4aaecb3f2018f90ee1bbedd1a1a5acd6474ba9","side":"left"},{"sibling":"b564425876fb4e37669812b1c9704fcbb44bf84c524737e0c292d8698acadb2a","side":"right"},{"sibling":"d686aadab346134cea3b1a9ca407741e39c2faaec0ce19ee820e7526a4ebd400","side":"left"},{"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_c2d2fdd5da7be5925f889b6ea9bb4cacc544f910ddb4c11a346902db08a6fc7e"}}