{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ELFPNCT27ODWVCV473MG7EPT3Z","short_pith_number":"pith:ELFPNCT2","canonical_record":{"source":{"id":"2601.07475","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-12T12:27:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"15eac9e656bbe099838e79d0c7185c1ff86ca1ebd3e6188e2e12e093757a46ab","abstract_canon_sha256":"da300a283ad60d7384a38b61d8a50d5f20708814a1ee7eaa2c4fbea8e9bc5be8"},"schema_version":"1.0"},"canonical_sha256":"22caf68a7afb876a8abcfed86f91f3de54a23bc463f7e858447938551a8112ab","source":{"kind":"arxiv","id":"2601.07475","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.07475","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"arxiv_version","alias_value":"2601.07475v2","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.07475","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_12","alias_value":"ELFPNCT27ODW","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_16","alias_value":"ELFPNCT27ODWVCV4","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_8","alias_value":"ELFPNCT2","created_at":"2026-07-07T02:17:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ELFPNCT27ODWVCV473MG7EPT3Z","target":"record","payload":{"canonical_record":{"source":{"id":"2601.07475","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-12T12:27:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"15eac9e656bbe099838e79d0c7185c1ff86ca1ebd3e6188e2e12e093757a46ab","abstract_canon_sha256":"da300a283ad60d7384a38b61d8a50d5f20708814a1ee7eaa2c4fbea8e9bc5be8"},"schema_version":"1.0"},"canonical_sha256":"22caf68a7afb876a8abcfed86f91f3de54a23bc463f7e858447938551a8112ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:17:16.248450Z","signature_b64":"F8kWTQqVpkYyIeM+VKvmWcjWdhZofB/WDJMW9u4AKbJ/qObqNeEmcvN/nqPfD6imqxxGH7HIRX0nxboIymgrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22caf68a7afb876a8abcfed86f91f3de54a23bc463f7e858447938551a8112ab","last_reissued_at":"2026-07-07T02:17:16.247527Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:17:16.247527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.07475","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-07T02:17:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LqNui7tBsnj4tc5EwAN8+bOGMfxZ2o7sl+4QgWuOAtCXIwgWWAgVPFWc1bZkUKTgAJ6xV7qYvOX0TpoJHIhZBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:21:38.672336Z"},"content_sha256":"77f164987cd02b288999e2cccb577f3b480ecd5cafbf8024283553899bc30ed0","schema_version":"1.0","event_id":"sha256:77f164987cd02b288999e2cccb577f3b480ecd5cafbf8024283553899bc30ed0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ELFPNCT27ODWVCV473MG7EPT3Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoqian Meng, Peng Zhang, Wenyuan Liu, Xindian Ma, Yafei Zhao, Yilun Luo","submitted_at":"2026-01-12T12:27:22Z","abstract_excerpt":"The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing Post-Training Quantization (PTQ) strategies to these formats: rotation-based methods compromise fine-grained block isolation; smoothing techniques struggle with significant 4-bit quantization errors; and mixed-precision approaches often conflict with hardware constraints on unified-precision computation. To address these challenges, we propose ARCQuant, a framework that boosts NVFP4 performance via Augmented Residua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.07475","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.07475/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-07T02:17:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YjMUUn8M+mO2orXawExPeJfO7mAFJg/pUJXuUXgTMAEQ2V9E5/2VzGzPMOJ0h0TjleRMBbLR0KiEyK2eKY2XCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:21:38.672859Z"},"content_sha256":"c9a10f26a78bcffc9edbadfc3c98f899dd402c80b11e4352aa4d6019b74122c0","schema_version":"1.0","event_id":"sha256:c9a10f26a78bcffc9edbadfc3c98f899dd402c80b11e4352aa4d6019b74122c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ELFPNCT27ODWVCV473MG7EPT3Z/bundle.json","state_url":"https://pith.science/pith/ELFPNCT27ODWVCV473MG7EPT3Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ELFPNCT27ODWVCV473MG7EPT3Z/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T08:21:38Z","links":{"resolver":"https://pith.science/pith/ELFPNCT27ODWVCV473MG7EPT3Z","bundle":"https://pith.science/pith/ELFPNCT27ODWVCV473MG7EPT3Z/bundle.json","state":"https://pith.science/pith/ELFPNCT27ODWVCV473MG7EPT3Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ELFPNCT27ODWVCV473MG7EPT3Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ELFPNCT27ODWVCV473MG7EPT3Z","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":"da300a283ad60d7384a38b61d8a50d5f20708814a1ee7eaa2c4fbea8e9bc5be8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-12T12:27:22Z","title_canon_sha256":"15eac9e656bbe099838e79d0c7185c1ff86ca1ebd3e6188e2e12e093757a46ab"},"schema_version":"1.0","source":{"id":"2601.07475","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.07475","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"arxiv_version","alias_value":"2601.07475v2","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.07475","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_12","alias_value":"ELFPNCT27ODW","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_16","alias_value":"ELFPNCT27ODWVCV4","created_at":"2026-07-07T02:17:16Z"},{"alias_kind":"pith_short_8","alias_value":"ELFPNCT2","created_at":"2026-07-07T02:17:16Z"}],"graph_snapshots":[{"event_id":"sha256:c9a10f26a78bcffc9edbadfc3c98f899dd402c80b11e4352aa4d6019b74122c0","target":"graph","created_at":"2026-07-07T02:17:16Z","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/2601.07475/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing Post-Training Quantization (PTQ) strategies to these formats: rotation-based methods compromise fine-grained block isolation; smoothing techniques struggle with significant 4-bit quantization errors; and mixed-precision approaches often conflict with hardware constraints on unified-precision computation. To address these challenges, we propose ARCQuant, a framework that boosts NVFP4 performance via Augmented Residua","authors_text":"Haoqian Meng, Peng Zhang, Wenyuan Liu, Xindian Ma, Yafei Zhao, Yilun Luo","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-12T12:27:22Z","title":"ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.07475","kind":"arxiv","version":2},"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":"sha256:77f164987cd02b288999e2cccb577f3b480ecd5cafbf8024283553899bc30ed0","target":"record","created_at":"2026-07-07T02:17:16Z","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":"da300a283ad60d7384a38b61d8a50d5f20708814a1ee7eaa2c4fbea8e9bc5be8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-12T12:27:22Z","title_canon_sha256":"15eac9e656bbe099838e79d0c7185c1ff86ca1ebd3e6188e2e12e093757a46ab"},"schema_version":"1.0","source":{"id":"2601.07475","kind":"arxiv","version":2}},"canonical_sha256":"22caf68a7afb876a8abcfed86f91f3de54a23bc463f7e858447938551a8112ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22caf68a7afb876a8abcfed86f91f3de54a23bc463f7e858447938551a8112ab","first_computed_at":"2026-07-07T02:17:16.247527Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T02:17:16.247527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F8kWTQqVpkYyIeM+VKvmWcjWdhZofB/WDJMW9u4AKbJ/qObqNeEmcvN/nqPfD6imqxxGH7HIRX0nxboIymgrDw==","signature_status":"signed_v1","signed_at":"2026-07-07T02:17:16.248450Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.07475","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77f164987cd02b288999e2cccb577f3b480ecd5cafbf8024283553899bc30ed0","sha256:c9a10f26a78bcffc9edbadfc3c98f899dd402c80b11e4352aa4d6019b74122c0"],"state_sha256":"19e8d1c95d249daa93da60dd63870385ba8f60c0088dc71b054f496a05419313"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZclB3eUcgDNomv/PolirylM2+4rf/9d7o6F9Gbwe8pmdSmVBPteovypHvMEAX3vJfMe3oLM3buGYvp/cp+lkDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T08:21:38.677654Z","bundle_sha256":"e7f7abedc0ebfb6fb32a01493ef2a0dff89acd310fe716fcbfacb3ff496a2c10"}}