{"_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_d2825b17d2260711533e7064f4ef89234882c287f2d88a8880f495d29d61fbd8","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_d2825b17d2260711533e7064f4ef89234882c287f2d88a8880f495d29d61fbd8","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"fef8e8b30eacc87b758c269df202e39326523682cb938f7d450c6e0a9bb1624d","published":"Thu, 18 Jun 2026 00:00:00 -0400","receipt_hash":"fef8e8b30eacc87b758c269df202e39326523682cb938f7d450c6e0a9bb1624d","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":"fef8e8b30eacc87b758c269df202e39326523682cb938f7d450c6e0a9bb1624d","observed_at":"2026-06-18T04:43:37.219665Z","parent_run_hash":"de79a40f7b3537d88842f7ac355e799c5df2adcb4fc32a4e28096d4bbdf01739","published":"Thu, 18 Jun 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:2601.21626v2 Announce Type: replace-cross \nAbstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joint framework reshapes the loss landscape by reducing the largest Hessian eigenvalue and reducing its max eigenvalue, thereby significantly enhancing robustness to quantization noise. HeRo-Q requires no architectural modifications, incurs negligible computational overhead, and integrates seamlessly into existing PTQ pipelines. Experiments on Llama and Qwen models show that HeRo Q consistently outp","title":"HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning","url":"https://arxiv.org/abs/2601.21626","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.21626v2 Announce Type: replace-cross \nAbstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joint framework reshapes the loss landscape by reducing the largest Hessian eigenvalue and reducing its max eigenvalue, thereby significantly enhancing robustness to quantization noise. HeRo-Q requires no architectural modifications, incurs negligible computational overhead, and integrates seamlessly into existing PTQ pipelines. Experiments on Llama and Qwen models show that HeRo Q consistently outp","title":"HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-18T04:43:37Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2601.21626"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:6f1573d3566e5631d4fa54548cf4704577eb41e1f42117c4a1b6c49499aff1a629304012fb9ecbad0c865ad41093db9a0de23840b192fa44b31909bd417e4904","signer":"crovia.substrate","subject":{"observed_at":"2026-06-18T04:43:37Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2601.21626"},"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":"6a2d6b25d46ffb76052d9072c646b665db924718dae53b999851b12ae5f480a5","leaf_index":233487,"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":"9e3047290c20f34a13eaa2459eea93bd565944f7c20380a36ebb2c3947f33f9b","side":"left"},{"sibling":"14964fef74b1298cc9c6aaff0c4a10aeff325a10e58436f91080c4d7a9914156","side":"left"},{"sibling":"9a05b0917c1f8f86f190afc61a981543ef8b0214f20e5057039560da24107f21","side":"left"},{"sibling":"8ee80e90a5859a0f3970010c25af4705db79811ae9dd29e704d29144f4182b75","side":"left"},{"sibling":"7649f67f4250c1762457d97675664e11db17378d8f2e1217749de00c26d834bc","side":"right"},{"sibling":"8ea5d1acc478cab779b8d8c6bd66dc15ad77122446a534f70804af03d690458e","side":"right"},{"sibling":"5e436eec5370aaf4cccb6c432bd004f0a554eab0e19ec87f70ba66470ad7303b","side":"right"},{"sibling":"fe9c643bdeb268143f61f15d89d0ff9dd03becb2914656c7a873d95f7266b5ce","side":"right"},{"sibling":"ad9e1cf26277141407aebd8692bfb16135dd1e614913e54b1dd445fed15d105a","side":"right"},{"sibling":"b151db1a7de0ce9a329250fae8b690f5b55ab3cfe5468a1fbb5f5bde0703b420","side":"right"},{"sibling":"7dc9143c057343b46a3b988492fba5262dec443cc1fb6070e3ea81543ca6a522","side":"right"},{"sibling":"ad5850946feb9a22361b5b9df0884f9ef1edcc7efea0374ff5782080ccb1a947","side":"right"},{"sibling":"410c633928fea11c5b4bdddb431956b1d7c320db9cda00d2fe32e0fcf888d7b7","side":"left"},{"sibling":"e616c34dbaf9456d5a6d3e2da82cde8621293c9f6d8a4cf9e441d7fd9cc81579","side":"right"},{"sibling":"94c0c932e61657f5e37fdba43f6ca9eddea8359425a7c1558dabe566911d5304","side":"right"},{"sibling":"a04392fb9f2a3a620840e3b3fecd93d12e6d224e481c162389d8ae64e7194599","side":"left"},{"sibling":"c300cf0154c136afc09b1702a0be98f4ba5b6dc5cf57e8cc714ec1eaf4196eff","side":"left"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","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":234491,"merkle_root":"02576a6980e38bab47864ae2c57b5a5ff21e554e9bdf8f64bdf28155ff1aabec","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260618T143732Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-18T18:33:39Z","sig_algorithm":"ed25519","signature":"b6c708778fc38b7789a2b91156cfe87252a7cd3a1d29121cba11a0c78f8cf104ca3019fc50a962fa5a216bcc4922fc8f3f69c04d1f8332c6dc0d931e1012e502","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_d2825b17d2260711533e7064f4ef89234882c287f2d88a8880f495d29d61fbd8"}}