{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IKEZCCXN6STQPYSEHL3DMP5Q74","short_pith_number":"pith:IKEZCCXN","canonical_record":{"source":{"id":"2512.06553","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-12-06T19:49:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aee1091196b24e6d83c34d402a610731a45244ad15ec5ae4aff1c4ac43d3efa5","abstract_canon_sha256":"de543813f607448197bc57e9f0613702ac1b9c067f629b10a7d776e3b5f25ee3"},"schema_version":"1.0"},"canonical_sha256":"4289910aedf4a707e2443af6363fb0ff05a2c2d49ef2f82e1e19962ae95b529a","source":{"kind":"arxiv","id":"2512.06553","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.06553","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"arxiv_version","alias_value":"2512.06553v2","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.06553","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_12","alias_value":"IKEZCCXN6STQ","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_16","alias_value":"IKEZCCXN6STQPYSE","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_8","alias_value":"IKEZCCXN","created_at":"2026-06-04T00:06:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IKEZCCXN6STQPYSEHL3DMP5Q74","target":"record","payload":{"canonical_record":{"source":{"id":"2512.06553","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-12-06T19:49:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"aee1091196b24e6d83c34d402a610731a45244ad15ec5ae4aff1c4ac43d3efa5","abstract_canon_sha256":"de543813f607448197bc57e9f0613702ac1b9c067f629b10a7d776e3b5f25ee3"},"schema_version":"1.0"},"canonical_sha256":"4289910aedf4a707e2443af6363fb0ff05a2c2d49ef2f82e1e19962ae95b529a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T00:06:50.571991Z","signature_b64":"/zuA0IegMY3F1rNKYay1nyktN93KF7ZeoZS0OharqDaut6mYRcUHBytRP3RYd/WsZcVc8WhhSF4vpK5RbZiaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4289910aedf4a707e2443af6363fb0ff05a2c2d49ef2f82e1e19962ae95b529a","last_reissued_at":"2026-06-04T00:06:50.571529Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T00:06:50.571529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.06553","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":"2026-06-04T00:06:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j6vXv2wtujGO6mfFCkXHau0msZr+S8vIF5Sf7IIkAcpcilzayRtOTgBTtMIwigWA2junAGEXl+zJka05HvZnAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T20:58:15.562217Z"},"content_sha256":"975cfe1dd2b97c6666438e13dbbc5618fcde9043973d44f0110f5ee8dc026a67","schema_version":"1.0","event_id":"sha256:975cfe1dd2b97c6666438e13dbbc5618fcde9043973d44f0110f5ee8dc026a67"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IKEZCCXN6STQPYSEHL3DMP5Q74","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Latent Variable Framework for Scaling Laws in Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.AP","authors_text":"Chengyu Cui, Felipe Maia Polo, Gongjun Xu, Kean Ming Tan, Leshem Choshen, Mikhail Yurochkin, Peiyao Cai, Seamus Somerstep, Yuekai Sun","submitted_at":"2025-12-06T19:49:31Z","abstract_excerpt":"We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new LLM families with distinct architectures and training strategies, evaluated on an increasing number of benchmarks. This heterogeneity makes a single global scaling curve inadequate for capturing how performance varies across families and benchmarks. To address this, we propose a latent variable modeling framework in which each LLM family is associated with a latent variable that captures the common underlying feature"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.06553","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/2512.06553/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":"2026-06-04T00:06:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iJuXnvorz7ujCr/XtP6+r7RyIIm/bWP630Sg+JrYPQEmULugZElW/qwzihUMaoJ2owySwo0pagWuGnB71SN1CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T20:58:15.562854Z"},"content_sha256":"736e098ea720a8ee63b2f90eedc22f7a2aa914b01594ab2be973c5282e221487","schema_version":"1.0","event_id":"sha256:736e098ea720a8ee63b2f90eedc22f7a2aa914b01594ab2be973c5282e221487"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/bundle.json","state_url":"https://pith.science/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/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":"2026-06-06T20:58:15Z","links":{"resolver":"https://pith.science/pith/IKEZCCXN6STQPYSEHL3DMP5Q74","bundle":"https://pith.science/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/bundle.json","state":"https://pith.science/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IKEZCCXN6STQPYSEHL3DMP5Q74/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IKEZCCXN6STQPYSEHL3DMP5Q74","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":"de543813f607448197bc57e9f0613702ac1b9c067f629b10a7d776e3b5f25ee3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-12-06T19:49:31Z","title_canon_sha256":"aee1091196b24e6d83c34d402a610731a45244ad15ec5ae4aff1c4ac43d3efa5"},"schema_version":"1.0","source":{"id":"2512.06553","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.06553","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"arxiv_version","alias_value":"2512.06553v2","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.06553","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_12","alias_value":"IKEZCCXN6STQ","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_16","alias_value":"IKEZCCXN6STQPYSE","created_at":"2026-06-04T00:06:50Z"},{"alias_kind":"pith_short_8","alias_value":"IKEZCCXN","created_at":"2026-06-04T00:06:50Z"}],"graph_snapshots":[{"event_id":"sha256:736e098ea720a8ee63b2f90eedc22f7a2aa914b01594ab2be973c5282e221487","target":"graph","created_at":"2026-06-04T00:06:50Z","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/2512.06553/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated by the rapid emergence of numerous new LLM families with distinct architectures and training strategies, evaluated on an increasing number of benchmarks. This heterogeneity makes a single global scaling curve inadequate for capturing how performance varies across families and benchmarks. To address this, we propose a latent variable modeling framework in which each LLM family is associated with a latent variable that captures the common underlying feature","authors_text":"Chengyu Cui, Felipe Maia Polo, Gongjun Xu, Kean Ming Tan, Leshem Choshen, Mikhail Yurochkin, Peiyao Cai, Seamus Somerstep, Yuekai Sun","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-12-06T19:49:31Z","title":"A Latent Variable Framework for Scaling Laws in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.06553","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":"sha256:975cfe1dd2b97c6666438e13dbbc5618fcde9043973d44f0110f5ee8dc026a67","target":"record","created_at":"2026-06-04T00:06:50Z","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":"de543813f607448197bc57e9f0613702ac1b9c067f629b10a7d776e3b5f25ee3","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2025-12-06T19:49:31Z","title_canon_sha256":"aee1091196b24e6d83c34d402a610731a45244ad15ec5ae4aff1c4ac43d3efa5"},"schema_version":"1.0","source":{"id":"2512.06553","kind":"arxiv","version":2}},"canonical_sha256":"4289910aedf4a707e2443af6363fb0ff05a2c2d49ef2f82e1e19962ae95b529a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4289910aedf4a707e2443af6363fb0ff05a2c2d49ef2f82e1e19962ae95b529a","first_computed_at":"2026-06-04T00:06:50.571529Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T00:06:50.571529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/zuA0IegMY3F1rNKYay1nyktN93KF7ZeoZS0OharqDaut6mYRcUHBytRP3RYd/WsZcVc8WhhSF4vpK5RbZiaDA==","signature_status":"signed_v1","signed_at":"2026-06-04T00:06:50.571991Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.06553","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:975cfe1dd2b97c6666438e13dbbc5618fcde9043973d44f0110f5ee8dc026a67","sha256:736e098ea720a8ee63b2f90eedc22f7a2aa914b01594ab2be973c5282e221487"],"state_sha256":"991f779712b75c25b23741badfa2155a252cfacaa30b2e523ab668bd3745fe7f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KKUS0O6OyoyzO5E7m3GXRIdyA2Jr3ZqdeWPZMyWehKxoLlZGDE3kGUm/GhDKGi3NTrXZX6L0zXDEYPVdPKIjBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T20:58:15.566543Z","bundle_sha256":"f6aa2f1e1a76b25e5c53e627f16c30fff019292ef5ed5a8ed7a8127b6be33cd4"}}