{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:X3AIZBQKXXQZSCZ6RCGABAFFA6","short_pith_number":"pith:X3AIZBQK","canonical_record":{"source":{"id":"2505.08981","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-05-13T21:46:56Z","cross_cats_sorted":[],"title_canon_sha256":"a9201314ba49df491938e68fc7259ae9917b57068aebada29a03545dd804a36b","abstract_canon_sha256":"fde5f95422c218657705fa241aac40203af4f279ff24e993347ed5c3658bbb49"},"schema_version":"1.0"},"canonical_sha256":"bec08c860abde1990b3e888c0080a507b9938ff5e64783cb99af9ca2fdb625ae","source":{"kind":"arxiv","id":"2505.08981","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08981","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08981v1","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08981","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"X3AIZBQKXXQZ","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_16","alias_value":"X3AIZBQKXXQZSCZ6","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_8","alias_value":"X3AIZBQK","created_at":"2026-07-05T11:02:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:X3AIZBQKXXQZSCZ6RCGABAFFA6","target":"record","payload":{"canonical_record":{"source":{"id":"2505.08981","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-05-13T21:46:56Z","cross_cats_sorted":[],"title_canon_sha256":"a9201314ba49df491938e68fc7259ae9917b57068aebada29a03545dd804a36b","abstract_canon_sha256":"fde5f95422c218657705fa241aac40203af4f279ff24e993347ed5c3658bbb49"},"schema_version":"1.0"},"canonical_sha256":"bec08c860abde1990b3e888c0080a507b9938ff5e64783cb99af9ca2fdb625ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:48.231484Z","signature_b64":"XgbCxZUg5gV6E8b6o9XFwnbMpm9cvUEkjPOmk9tgTdUSKSoyQiB7Zz/b3dQ4BsWSjfZoF3B6eVAdjhj7o1XpDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bec08c860abde1990b3e888c0080a507b9938ff5e64783cb99af9ca2fdb625ae","last_reissued_at":"2026-07-05T11:02:48.231007Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:48.231007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.08981","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8D0ND9zNZvHTFq8kYreRRIs0gXmDdLb5G/ESL/OI2iuESbPKoDY36NKO62k/NyeZy/oBb9oJcDI5MHnICLSpCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:24:37.250032Z"},"content_sha256":"89e147f967f0dac97ff2ba67ef4986d7aa8f2e59ea928a83fee8593da14395a9","schema_version":"1.0","event_id":"sha256:89e147f967f0dac97ff2ba67ef4986d7aa8f2e59ea928a83fee8593da14395a9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:X3AIZBQKXXQZSCZ6RCGABAFFA6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AR","authors_text":"Christos-Savvas Bouganis, Keran Zheng, Yinting Huang, Zhewen Yu","submitted_at":"2025-05-13T21:46:56Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have demonstrated impressive capabilities as their scale expands to billions of parameters. Deploying these large-scale models on resource-constrained platforms presents significant challenges, with post-training fixed-point quantization often used as a model compression technique. However, quantization-only methods typically lead to significant accuracy degradation in LLMs when precision falls below 8 bits. This paper addresses this challenge through a software-hardware co-design framework, ITERA-LLM, which integrates sub-8-bit quantization "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08981","kind":"arxiv","version":1},"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/2505.08981/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":"2026-07-05T11:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6TmF7dLdgt1maZvCYWAaqSQy3CamWMU39MVIw7mxqs5zM7gLP+NU29zpNsANoYC5DJ4KsvxGErbTbRzsBFN+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:24:37.250529Z"},"content_sha256":"1ba7c5d3f68c93bcda8847b550dfc7744e3a802adaefdc5d17c0efefbe1e37b6","schema_version":"1.0","event_id":"sha256:1ba7c5d3f68c93bcda8847b550dfc7744e3a802adaefdc5d17c0efefbe1e37b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/bundle.json","state_url":"https://pith.science/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/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":"2026-08-16T16:24:37Z","links":{"resolver":"https://pith.science/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6","bundle":"https://pith.science/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/bundle.json","state":"https://pith.science/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X3AIZBQKXXQZSCZ6RCGABAFFA6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:X3AIZBQKXXQZSCZ6RCGABAFFA6","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":"fde5f95422c218657705fa241aac40203af4f279ff24e993347ed5c3658bbb49","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-05-13T21:46:56Z","title_canon_sha256":"a9201314ba49df491938e68fc7259ae9917b57068aebada29a03545dd804a36b"},"schema_version":"1.0","source":{"id":"2505.08981","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08981","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08981v1","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08981","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"X3AIZBQKXXQZ","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_16","alias_value":"X3AIZBQKXXQZSCZ6","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_8","alias_value":"X3AIZBQK","created_at":"2026-07-05T11:02:48Z"}],"graph_snapshots":[{"event_id":"sha256:1ba7c5d3f68c93bcda8847b550dfc7744e3a802adaefdc5d17c0efefbe1e37b6","target":"graph","created_at":"2026-07-05T11:02:48Z","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/2505.08981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have demonstrated impressive capabilities as their scale expands to billions of parameters. Deploying these large-scale models on resource-constrained platforms presents significant challenges, with post-training fixed-point quantization often used as a model compression technique. However, quantization-only methods typically lead to significant accuracy degradation in LLMs when precision falls below 8 bits. This paper addresses this challenge through a software-hardware co-design framework, ITERA-LLM, which integrates sub-8-bit quantization ","authors_text":"Christos-Savvas Bouganis, Keran Zheng, Yinting Huang, Zhewen Yu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-05-13T21:46:56Z","title":"ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08981","kind":"arxiv","version":1},"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":"sha256:89e147f967f0dac97ff2ba67ef4986d7aa8f2e59ea928a83fee8593da14395a9","target":"record","created_at":"2026-07-05T11:02:48Z","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":"fde5f95422c218657705fa241aac40203af4f279ff24e993347ed5c3658bbb49","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AR","submitted_at":"2025-05-13T21:46:56Z","title_canon_sha256":"a9201314ba49df491938e68fc7259ae9917b57068aebada29a03545dd804a36b"},"schema_version":"1.0","source":{"id":"2505.08981","kind":"arxiv","version":1}},"canonical_sha256":"bec08c860abde1990b3e888c0080a507b9938ff5e64783cb99af9ca2fdb625ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bec08c860abde1990b3e888c0080a507b9938ff5e64783cb99af9ca2fdb625ae","first_computed_at":"2026-07-05T11:02:48.231007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:48.231007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XgbCxZUg5gV6E8b6o9XFwnbMpm9cvUEkjPOmk9tgTdUSKSoyQiB7Zz/b3dQ4BsWSjfZoF3B6eVAdjhj7o1XpDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:48.231484Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.08981","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89e147f967f0dac97ff2ba67ef4986d7aa8f2e59ea928a83fee8593da14395a9","sha256:1ba7c5d3f68c93bcda8847b550dfc7744e3a802adaefdc5d17c0efefbe1e37b6"],"state_sha256":"18975d77258cc858aeb069a332da20ccdb170c92f2210d40c7b7f673af30093c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lx/IsczYgAWaSeI8ZTW+utIY6KxmyQ8Rso7sdnoXaQMlYlDLHhgqdvo5gs28IeqatqkPhyWViZFq3XrbMhijAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T16:24:37.255338Z","bundle_sha256":"8839f9158b1814187b717aea666821db4b8c8081333086ac75de37b55a445fec"}}