{"_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_a7b508fc87232a63508f5cc457af6b4934ef50c27a56e6da04ca1c916e51856f","bitcoin_anchor":{"bitcoin_attestations":["bitcoin_block_949451"],"calendar_attestations":["https://finney.calendar.eternitywall.com","https://btc.calendar.catallaxy.com","https://alice.btc.calendar.opentimestamps.org","https://bob.btc.calendar.opentimestamps.org"],"ots_url":"/registry/data/substrate/anchors/77fc9c28fae777b81da5b495b3115474df6592dfac590333213d3bdf8b94a9b3.ots","stamped_at":"2026-05-15T03:00:03Z","status":"bitcoin"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_a7b508fc87232a63508f5cc457af6b4934ef50c27a56e6da04ca1c916e51856f","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"027fa35a43fef9526a5169af9cc38f0d21dbf8d22dfe6aa4fbb42542ddb4b73b","published":"Wed, 06 May 2026 00:00:00 -0400","receipt_hash":"027fa35a43fef9526a5169af9cc38f0d21dbf8d22dfe6aa4fbb42542ddb4b73b","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":"027fa35a43fef9526a5169af9cc38f0d21dbf8d22dfe6aa4fbb42542ddb4b73b","observed_at":"2026-05-06T04:43:18.518819Z","parent_run_hash":"184209fc0f2ebdc4b721e1a74f74ab17637dab3344682b6e6bd1f3e5e8f2cd00","published":"Wed, 06 May 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:2506.13727v2 Announce Type: replace-cross \nAbstract: Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors. Mechanistic interpretability addresses this challenge by identifying circuits -- subsets of model components responsible for specific behaviors. However, discovering such circuits in LLMs remains difficult due to their scale and complexity. We propose an attribution-guided pruning approach for circuit discovery based on Layer-wise Relevance Propagation (LRP). By attributing model outputs to internal components using task-specific reference samples, we identify behaviorally relevant parameters and extract sparse functional circuits. Building on this, we introduce contrastive relevance to isolate circuits associated with undesired behaviors while preserving general capabilities, enabling targeted model correction. O","title":"Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs","url":"https://arxiv.org/abs/2506.13727","vendor":"arxiv_cs_ai"},"summary":"arXiv:2506.13727v2 Announce Type: replace-cross \nAbstract: Large Language Models (LLMs) are widely deployed in real-world applications, yet their internal mechanisms remain difficult to interpret and control, limiting our ability to diagnose and correct undesirable behaviors. Mechanistic interpretability addresses this challenge by identifying circuits -- subsets of model components responsible for specific behaviors. However, discovering such circuits in LLMs remains difficult due to their scale and complexity. We propose an attribution-guided pruning approach for circuit discovery based on Layer-wise Relevance Propagation (LRP). By attributing model outputs to internal components using task-specific reference samples, we identify behaviorally relevant parameters and extract sparse functional circuits. Building on this, we introduce contrastive relevance to isolate circuits associated with undesired behaviors while preserving general capabilities, enabling targeted model correction. O","title":"Attribution-Guided Pruning for Insight and Control: Circuit Discovery and Targeted Correction in Small-scale LLMs","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-06T04:43:18Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2506.13727"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:b20648b8e80dbf64c587dc802fa0696086ee6889d3fa145e4c3ceb7cb2d804f954029c6aa6e7d922bcd75728a834fab19b3b75e8ec350ff79116a50c88723301","signer":"crovia.substrate","subject":{"observed_at":"2026-05-06T04:43:18Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2506.13727"},"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":"92f1a2421f3f56ea78f8deb73de1888ff6d3378e25887d2b17230661b96e33be","leaf_index":116483,"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":"31beba65cb493c91c9ef18ef2827dbf61edf995373396458aea86cc3250067cf","side":"left"},{"sibling":"f062f04b6b27479d9b304543d6d921b11f987f86c69bf0a5303707b02e33d980","side":"left"},{"sibling":"bdb94ffcd01750573e856384d8e30c58791984bb3650cb8d4e1d0f3eff6a1790","side":"right"},{"sibling":"6a5ffe1bde8332a10df02cfaa819275686f14598f656e3a4499edb2455ce3a02","side":"right"},{"sibling":"0bdfebf74d2a2f5c8572930b143ca1b4ea76db11f3b695466cdf94932d256bbb","side":"right"},{"sibling":"584a66d6b1a2c8ae7721fb5ec8540a71ac0ce02cce145234385fd1663f5ed429","side":"right"},{"sibling":"7c2a64bc0c0c90ad8130bbf6b1fe4f1205ab0d27cbd8331b9256ed6da29c4781","side":"right"},{"sibling":"6ee45885d9cd9ed12596a458214030b8197200a928104b38bdab7a9d649f44f8","side":"right"},{"sibling":"5574ba66329e909ad4ff093e3491b05d5aa4f4ee91405ec5ecdb639bea7d87ab","side":"left"},{"sibling":"c750384c973eaf46aa237cdabe7a75729862dd4101dca10cb8e0626e0ec2d34a","side":"left"},{"sibling":"ddc060ac400459417799f688c75d5b271636afa40d89ec7fc98652473a8061f6","side":"left"},{"sibling":"282afa51266e47629e34d808a360bbb276348bcc5a24bdcc93e83a76e580293e","side":"right"},{"sibling":"05f89b32c00462e60adf95c1fe4579cdc2791b36e8b17573d8f3b5fd5da95a0b","side":"right"},{"sibling":"8ccd9937a2c0d5c04044d07d1557791b7d07bb31eac41a39a675608d44b38f23","side":"right"},{"sibling":"3a5e69cf0803f4c91f3895ed7c9a95748fef240bec4422e167c05300f79f06c0","side":"left"},{"sibling":"f2817ab288b5324fe49770372c7a10f33f7cd11005f8d4c0a730316f5229dc98","side":"left"},{"sibling":"725fac972e772ca0dc598810ea1abc70df472f72d2d6ab8a0baee2b80e5d2f4c","side":"left"},{"sibling":"98fc57dfef8873b512edc8340f7181df57302bb96777625e072235c62d7c5895","side":"right"}]},"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":134292,"merkle_root":"77fc9c28fae777b81da5b495b3115474df6592dfac590333213d3bdf8b94a9b3","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260515T023701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-15T02:37:25Z","sig_algorithm":"ed25519","signature":"68107a834b00b24f5d4501e5ec727445311f132a486567ecc4c72a4e6dff24c8c21f2de3105293353ba5fdbe370d032819af6aa70f694e2e39b6af6737507009","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_a7b508fc87232a63508f5cc457af6b4934ef50c27a56e6da04ca1c916e51856f"}}