{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:EHXUG5PRRDKCWTLED2VVDIQZ4Z","short_pith_number":"pith:EHXUG5PR","canonical_record":{"source":{"id":"2605.30327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T17:57:32Z","cross_cats_sorted":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"7f35aba040257a05af9d9da4c70869c3ad2a2eb036e2d0fafaf39ae3b4a6b842","abstract_canon_sha256":"e0a7791a9b09c451c92eb9f4f16cb891a0ce0df0bfde564f08255105154ef89a"},"schema_version":"1.0"},"canonical_sha256":"21ef4375f188d42b4d641eab51a219e66a93ed1a65a27a3d92a751ae0b45b6c5","source":{"kind":"arxiv","id":"2605.30327","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30327","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30327v1","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30327","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"EHXUG5PRRDKC","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_16","alias_value":"EHXUG5PRRDKCWTLE","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_8","alias_value":"EHXUG5PR","created_at":"2026-05-29T02:06:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:EHXUG5PRRDKCWTLED2VVDIQZ4Z","target":"record","payload":{"canonical_record":{"source":{"id":"2605.30327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T17:57:32Z","cross_cats_sorted":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"7f35aba040257a05af9d9da4c70869c3ad2a2eb036e2d0fafaf39ae3b4a6b842","abstract_canon_sha256":"e0a7791a9b09c451c92eb9f4f16cb891a0ce0df0bfde564f08255105154ef89a"},"schema_version":"1.0"},"canonical_sha256":"21ef4375f188d42b4d641eab51a219e66a93ed1a65a27a3d92a751ae0b45b6c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:16.616267Z","signature_b64":"sqOcd19bFdOOVYiMtM4ENYNuz9ch+RruJOpZtI5AKNmUDWiQJ+1rynLFANax2Ffys7XPON8Pe1AK2YOeydCHBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21ef4375f188d42b4d641eab51a219e66a93ed1a65a27a3d92a751ae0b45b6c5","last_reissued_at":"2026-05-29T02:06:16.615875Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:16.615875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.30327","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":"2026-05-29T02:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"maFlFInu9OD6KuDXsI5mCD4MxHEDPsi+lq8UjgUlgGcrSfLlq7gjP0SttpWA8M/yCECi4/NUABfWc5cPwX0mBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T08:36:00.007736Z"},"content_sha256":"564024ee76769970bdf2489503078de13fd3224640fdf8000f202b9d663239af","schema_version":"1.0","event_id":"sha256:564024ee76769970bdf2489503078de13fd3224640fdf8000f202b9d663239af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:EHXUG5PRRDKCWTLED2VVDIQZ4Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reasoning with Sampling: Cutting at Decision Points","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Anay Mehrotra, Felix Zhou, Quanquan C. Liu","submitted_at":"2026-05-28T17:57:32Z","abstract_excerpt":"Frontier reasoning models are produced by posttraining base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened version of the base model's distribution, a so-called power distribution, elicits comparable reasoning without additional training, curated datasets, or verifiers. However, making this method practical requires efficiently sampling from the power distribution. A sampler needs to \"mix\" to the power distribution, which necessitates moving between modes of the target distribution; intuitively, e.g., trying different reas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30327","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/2605.30327/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":"2026-05-29T02:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTMbCmk314o/j1N3y/LTqFmqSA+Ds6YyWp3y57Yg3V1lp6UAfpDp0O+Y5MrCQcg2L9ojeD74ufGHfyGj80EzCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T08:36:00.008122Z"},"content_sha256":"81a54523d96d0008f85765f18aca2874e22e948857b5efbf25d640a13c6283b6","schema_version":"1.0","event_id":"sha256:81a54523d96d0008f85765f18aca2874e22e948857b5efbf25d640a13c6283b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/bundle.json","state_url":"https://pith.science/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/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":"2026-07-01T08:36:00Z","links":{"resolver":"https://pith.science/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z","bundle":"https://pith.science/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/bundle.json","state":"https://pith.science/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EHXUG5PRRDKCWTLED2VVDIQZ4Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:EHXUG5PRRDKCWTLED2VVDIQZ4Z","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":"e0a7791a9b09c451c92eb9f4f16cb891a0ce0df0bfde564f08255105154ef89a","cross_cats_sorted":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T17:57:32Z","title_canon_sha256":"7f35aba040257a05af9d9da4c70869c3ad2a2eb036e2d0fafaf39ae3b4a6b842"},"schema_version":"1.0","source":{"id":"2605.30327","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30327","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30327v1","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30327","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"EHXUG5PRRDKC","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_16","alias_value":"EHXUG5PRRDKCWTLE","created_at":"2026-05-29T02:06:16Z"},{"alias_kind":"pith_short_8","alias_value":"EHXUG5PR","created_at":"2026-05-29T02:06:16Z"}],"graph_snapshots":[{"event_id":"sha256:81a54523d96d0008f85765f18aca2874e22e948857b5efbf25d640a13c6283b6","target":"graph","created_at":"2026-05-29T02:06:16Z","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/2605.30327/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Frontier reasoning models are produced by posttraining base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened version of the base model's distribution, a so-called power distribution, elicits comparable reasoning without additional training, curated datasets, or verifiers. However, making this method practical requires efficiently sampling from the power distribution. A sampler needs to \"mix\" to the power distribution, which necessitates moving between modes of the target distribution; intuitively, e.g., trying different reas","authors_text":"Anay Mehrotra, Felix Zhou, Quanquan C. Liu","cross_cats":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T17:57:32Z","title":"Reasoning with Sampling: Cutting at Decision Points"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30327","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":"sha256:564024ee76769970bdf2489503078de13fd3224640fdf8000f202b9d663239af","target":"record","created_at":"2026-05-29T02:06:16Z","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":"e0a7791a9b09c451c92eb9f4f16cb891a0ce0df0bfde564f08255105154ef89a","cross_cats_sorted":["cs.AI","cs.CL","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T17:57:32Z","title_canon_sha256":"7f35aba040257a05af9d9da4c70869c3ad2a2eb036e2d0fafaf39ae3b4a6b842"},"schema_version":"1.0","source":{"id":"2605.30327","kind":"arxiv","version":1}},"canonical_sha256":"21ef4375f188d42b4d641eab51a219e66a93ed1a65a27a3d92a751ae0b45b6c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21ef4375f188d42b4d641eab51a219e66a93ed1a65a27a3d92a751ae0b45b6c5","first_computed_at":"2026-05-29T02:06:16.615875Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T02:06:16.615875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sqOcd19bFdOOVYiMtM4ENYNuz9ch+RruJOpZtI5AKNmUDWiQJ+1rynLFANax2Ffys7XPON8Pe1AK2YOeydCHBQ==","signature_status":"signed_v1","signed_at":"2026-05-29T02:06:16.616267Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30327","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:564024ee76769970bdf2489503078de13fd3224640fdf8000f202b9d663239af","sha256:81a54523d96d0008f85765f18aca2874e22e948857b5efbf25d640a13c6283b6"],"state_sha256":"34a6a82531568d02b8e179eecff40cdc8f370b3ef7d7b5b9d66db2b42fb1f8a9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aLqxE6Wuzs7Pt0pB70Z6p6X+biZgV2cOxjrScQ9aDPUlFMukoaVxflBuyaZ5/0vwo99ieQv9DVC0pouHKQUbDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T08:36:00.010203Z","bundle_sha256":"56056aac56ccc82e76c8d7eb3c2e5cb07c51a3f5ab9a1b01b5da0eca2cb24e44"}}