{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OXRYN2TOQOYECO5M3HGTWP3DQL","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":"902a117a272fcd9238d3d408303513c06388bdfce6a2c2a3b59da6a271578778","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-25T06:28:58Z","title_canon_sha256":"bb50e61da520dd9eb49619541867382880f6005196f6b1f634af04c91e99cee8"},"schema_version":"1.0","source":{"id":"2603.23985","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.23985","created_at":"2026-05-27T01:05:53Z"},{"alias_kind":"arxiv_version","alias_value":"2603.23985v3","created_at":"2026-05-27T01:05:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.23985","created_at":"2026-05-27T01:05:53Z"},{"alias_kind":"pith_short_12","alias_value":"OXRYN2TOQOYE","created_at":"2026-05-27T01:05:53Z"},{"alias_kind":"pith_short_16","alias_value":"OXRYN2TOQOYECO5M","created_at":"2026-05-27T01:05:53Z"},{"alias_kind":"pith_short_8","alias_value":"OXRYN2TO","created_at":"2026-05-27T01:05:53Z"}],"graph_snapshots":[{"event_id":"sha256:602c82270cec0acd92f92a605f88664b126e2645417b653c14c65f3b022bda77","target":"graph","created_at":"2026-05-27T01:05:53Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"that activation magnitudes computed from only 100 samples per task are sufficient to produce a reliable global importance ranking that generalizes across unseen tasks and inputs."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DIET prunes LLMs dimension-wise by merging task-specific activation importance scores via majority voting on 100 samples per task, yielding up to 10% accuracy gains over prior structured methods at 20% sparsity on Gemma-2 models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DIET prunes LLM dimensions by merging per-task activation scores into one global mask without any training."}],"snapshot_sha256":"80943eec44744cd9029eded8a210fdac1618d561f9e108b13974ec5b9a67f168"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a189ac9aee3c7b82f5eb3e0bdce72037212b4b18be16d87e14f59cb4ef9734e1"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2603.23985/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade-offs: task-agnostic approaches cannot adapt to task-specific requirements, while task-aware methods require costly training to learn task adaptability. We propose DIET (Dimension-wise global pruning of LLMs via merging Task-wise importance scores), a training-free structured pruning method that combines dimension-level","authors_text":"Jaehyung Kim, Jimyung Hong","cross_cats":[],"headline":"DIET prunes LLM dimensions by merging per-task activation scores into one global mask without any training.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-25T06:28:58Z","title":"Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.23985","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-15T00:51:10.787003Z","id":"f9cd5a15-2fcb-495c-9242-b8b6cf769d9e","model_set":{"reader":"grok-4.3"},"one_line_summary":"DIET prunes LLMs dimension-wise by merging task-specific activation importance scores via majority voting on 100 samples per task, yielding up to 10% accuracy gains over prior structured methods at 20% sparsity on Gemma-2 models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DIET prunes LLM dimensions by merging per-task activation scores into one global mask without any training.","strongest_claim":"at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods.","weakest_assumption":"that activation magnitudes computed from only 100 samples per task are sufficient to produce a reliable global importance ranking that generalizes across unseen tasks and inputs."}},"verdict_id":"f9cd5a15-2fcb-495c-9242-b8b6cf769d9e"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:07406a415649a96fa689f68ddfd5e0d4f5939771f39264f9af84222f86d100f3","target":"record","created_at":"2026-05-27T01:05:53Z","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":"902a117a272fcd9238d3d408303513c06388bdfce6a2c2a3b59da6a271578778","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-25T06:28:58Z","title_canon_sha256":"bb50e61da520dd9eb49619541867382880f6005196f6b1f634af04c91e99cee8"},"schema_version":"1.0","source":{"id":"2603.23985","kind":"arxiv","version":3}},"canonical_sha256":"75e386ea6e83b0413bacd9cd3b3f6382e830d6c2bd36482b94a3b5f90829bd03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75e386ea6e83b0413bacd9cd3b3f6382e830d6c2bd36482b94a3b5f90829bd03","first_computed_at":"2026-05-27T01:05:53.464958Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:05:53.464958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XGFmHLEVWnaWtVR0vUTuXsK+KmGHP7HpueFZjUBVYixbLDDHOk2xobB7wDKBhM5f9Z8dvP1QbCt8ima/6rfGBw==","signature_status":"signed_v1","signed_at":"2026-05-27T01:05:53.465905Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.23985","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07406a415649a96fa689f68ddfd5e0d4f5939771f39264f9af84222f86d100f3","sha256:602c82270cec0acd92f92a605f88664b126e2645417b653c14c65f3b022bda77"],"state_sha256":"595fa34395e1e6bcec313cf3d688a5c55d91d0c33a395280fefd8c0b162a73a2"}