{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MFCSPPSDL2IWCNPIUB4K3QRHSZ","short_pith_number":"pith:MFCSPPSD","canonical_record":{"source":{"id":"2604.25098","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-28T01:04:09Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"e91c863716a629e04247d0bb4438c0092697100616644fbee312afa49d92d4fd","abstract_canon_sha256":"6f70b08c28aba9830f635b821f12b065ea57ae29d83e77ff0b1b0c109b98463b"},"schema_version":"1.0"},"canonical_sha256":"614527be435e916135e8a078adc2279672935fe36c6aac9748396704e7268cd9","source":{"kind":"arxiv","id":"2604.25098","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.25098","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"arxiv_version","alias_value":"2604.25098v2","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.25098","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_12","alias_value":"MFCSPPSDL2IW","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_16","alias_value":"MFCSPPSDL2IWCNPI","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_8","alias_value":"MFCSPPSD","created_at":"2026-05-29T01:05:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MFCSPPSDL2IWCNPIUB4K3QRHSZ","target":"record","payload":{"canonical_record":{"source":{"id":"2604.25098","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-28T01:04:09Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"e91c863716a629e04247d0bb4438c0092697100616644fbee312afa49d92d4fd","abstract_canon_sha256":"6f70b08c28aba9830f635b821f12b065ea57ae29d83e77ff0b1b0c109b98463b"},"schema_version":"1.0"},"canonical_sha256":"614527be435e916135e8a078adc2279672935fe36c6aac9748396704e7268cd9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:10.586018Z","signature_b64":"j5aUivAgyDupuV4yL5EUz8qU76nTgh0e3NZ02WTNlQIhKY1BQtG6JNP4voAr/h+2LKnDUElFNqZBABojlC6hCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"614527be435e916135e8a078adc2279672935fe36c6aac9748396704e7268cd9","last_reissued_at":"2026-05-29T01:05:10.585343Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:10.585343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.25098","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":"2026-05-29T01:05:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QAU33ku+DLXj56pELEEGy4J8T+K934yvd1jvvx1oyjwjNFNTWluFOiOemgX26zo284T2hODPGqaGp1KIXqnVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:28:51.778743Z"},"content_sha256":"7cd89db1cf91f71c07f17883a0256f22a61512af1e56020cfe9f48fb1571d8ee","schema_version":"1.0","event_id":"sha256:7cd89db1cf91f71c07f17883a0256f22a61512af1e56020cfe9f48fb1571d8ee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MFCSPPSDL2IWCNPIUB4K3QRHSZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models.","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Anshuman Chhabra, Ocean Monjur, Shahriar Kabir Nahin","submitted_at":"2026-04-28T01:04:09Z","abstract_excerpt":"Large Language Models (LLMs) now exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), with impressive performance across math and coding benchmarks. In parallel, research in model compression has developed pruning methods that seek to remove redundant/detrimental parameters without sacrificing task performance. The intersection of these two research advancements lays the foundation for our work. Specific to reasoning LLMs, prior work has shown that structured pruning (methods which remove entire set of layer blocks), significantly degrades TTS reasoning performanc"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the specific unstructured pruning implementations and layer-wise sparsity allocation strategies chosen do not introduce hidden biases or overfit to the four benchmarks, and that results will generalize beyond the tested models and tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Unstructured pruning augments test-time scaling reasoning performance in LLMs and can outperform the unpruned model on benchmarks, contrary to expectations from structured pruning studies.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b52d18379fa4748203049682c057d1cc6879384db60e62adbf5c18f8ce23b7ed"},"source":{"id":"2604.25098","kind":"arxiv","version":2},"verdict":{"id":"ede0f32e-63da-4b35-8bb7-e7a8e981ecc5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T16:45:47.003146Z","strongest_claim":"our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs.","one_line_summary":"Unstructured pruning augments test-time scaling reasoning performance in LLMs and can outperform the unpruned model on benchmarks, contrary to expectations from structured pruning studies.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the specific unstructured pruning implementations and layer-wise sparsity allocation strategies chosen do not introduce hidden biases or overfit to the four benchmarks, and that results will generalize beyond the tested models and tasks.","pith_extraction_headline":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.25098/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T05:39:27.267886Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:25:45.354408Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"0ae678d26e70eeaedad0242d907778add8ed55fd2c79d4cb0c68418b76c19a51"},"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":"ede0f32e-63da-4b35-8bb7-e7a8e981ecc5"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-29T01:05:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cq8h4//vaChd63KwpPdm/+3i8E7ZHdFQOA9qSpFBGmgmJFEpLTPSq8CanzS6E3j9gZGf5TvLWfjJU0ggv/QeCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T18:28:51.779645Z"},"content_sha256":"3965427868818517707a24661b3ffb3b80359c7e68cfea34da2ea8278139c6de","schema_version":"1.0","event_id":"sha256:3965427868818517707a24661b3ffb3b80359c7e68cfea34da2ea8278139c6de"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/bundle.json","state_url":"https://pith.science/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/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":"2026-06-06T18:28:51Z","links":{"resolver":"https://pith.science/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ","bundle":"https://pith.science/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/bundle.json","state":"https://pith.science/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MFCSPPSDL2IWCNPIUB4K3QRHSZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MFCSPPSDL2IWCNPIUB4K3QRHSZ","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":"6f70b08c28aba9830f635b821f12b065ea57ae29d83e77ff0b1b0c109b98463b","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-28T01:04:09Z","title_canon_sha256":"e91c863716a629e04247d0bb4438c0092697100616644fbee312afa49d92d4fd"},"schema_version":"1.0","source":{"id":"2604.25098","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.25098","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"arxiv_version","alias_value":"2604.25098v2","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.25098","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_12","alias_value":"MFCSPPSDL2IW","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_16","alias_value":"MFCSPPSDL2IWCNPI","created_at":"2026-05-29T01:05:10Z"},{"alias_kind":"pith_short_8","alias_value":"MFCSPPSD","created_at":"2026-05-29T01:05:10Z"}],"graph_snapshots":[{"event_id":"sha256:3965427868818517707a24661b3ffb3b80359c7e68cfea34da2ea8278139c6de","target":"graph","created_at":"2026-05-29T01:05:10Z","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":"our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the specific unstructured pruning implementations and layer-wise sparsity allocation strategies chosen do not introduce hidden biases or overfit to the four benchmarks, and that results will generalize beyond the tested models and tasks."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Unstructured pruning augments test-time scaling reasoning performance in LLMs and can outperform the unpruned model on benchmarks, contrary to expectations from structured pruning studies."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models."}],"snapshot_sha256":"b52d18379fa4748203049682c057d1cc6879384db60e62adbf5c18f8ce23b7ed"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T05:39:27.267886Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:25:45.354408Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.25098/integrity.json","findings":[],"snapshot_sha256":"0ae678d26e70eeaedad0242d907778add8ed55fd2c79d4cb0c68418b76c19a51","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) now exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), with impressive performance across math and coding benchmarks. In parallel, research in model compression has developed pruning methods that seek to remove redundant/detrimental parameters without sacrificing task performance. The intersection of these two research advancements lays the foundation for our work. Specific to reasoning LLMs, prior work has shown that structured pruning (methods which remove entire set of layer blocks), significantly degrades TTS reasoning performanc","authors_text":"Anshuman Chhabra, Ocean Monjur, Shahriar Kabir Nahin","cross_cats":["cs.CL","cs.LG"],"headline":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-28T01:04:09Z","title":"Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.25098","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-07T16:45:47.003146Z","id":"ede0f32e-63da-4b35-8bb7-e7a8e981ecc5","model_set":{"reader":"grok-4.3"},"one_line_summary":"Unstructured pruning augments test-time scaling reasoning performance in LLMs and can outperform the unpruned model on benchmarks, contrary to expectations from structured pruning studies.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models.","strongest_claim":"our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs.","weakest_assumption":"That the specific unstructured pruning implementations and layer-wise sparsity allocation strategies chosen do not introduce hidden biases or overfit to the four benchmarks, and that results will generalize beyond the tested models and tasks."}},"verdict_id":"ede0f32e-63da-4b35-8bb7-e7a8e981ecc5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7cd89db1cf91f71c07f17883a0256f22a61512af1e56020cfe9f48fb1571d8ee","target":"record","created_at":"2026-05-29T01:05:10Z","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":"6f70b08c28aba9830f635b821f12b065ea57ae29d83e77ff0b1b0c109b98463b","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-04-28T01:04:09Z","title_canon_sha256":"e91c863716a629e04247d0bb4438c0092697100616644fbee312afa49d92d4fd"},"schema_version":"1.0","source":{"id":"2604.25098","kind":"arxiv","version":2}},"canonical_sha256":"614527be435e916135e8a078adc2279672935fe36c6aac9748396704e7268cd9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"614527be435e916135e8a078adc2279672935fe36c6aac9748396704e7268cd9","first_computed_at":"2026-05-29T01:05:10.585343Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:05:10.585343Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j5aUivAgyDupuV4yL5EUz8qU76nTgh0e3NZ02WTNlQIhKY1BQtG6JNP4voAr/h+2LKnDUElFNqZBABojlC6hCQ==","signature_status":"signed_v1","signed_at":"2026-05-29T01:05:10.586018Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.25098","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7cd89db1cf91f71c07f17883a0256f22a61512af1e56020cfe9f48fb1571d8ee","sha256:3965427868818517707a24661b3ffb3b80359c7e68cfea34da2ea8278139c6de"],"state_sha256":"6e4432a8fe66d3b4b589e3315707c574d9a129b4e4ff8a21cabae82b3481f07e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bMgJnjycEWF2CcR+dQIGqPwpjcDPZMp+76lazqTZpIlyrLY/4y0hiLkSwNJB+tafK7oS+LyThfi/hTtwKaIpBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T18:28:51.784313Z","bundle_sha256":"740df5731ce43192bafc25f631740ff254196b2b5f35c3f6ac759c92642212b3"}}