{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SYPRT7MHZKDXNZKC7BZFYD6SBC","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":"dca5337b7825e2762e448cf6f14d486b47ae090bcaf630dd113405a73ed99d93","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T08:33:30Z","title_canon_sha256":"ba57d673620176c47a3bf1c45cee033bcf99f75e6b6a03a08f574283f5aae1c3"},"schema_version":"1.0","source":{"id":"2605.26693","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26693","created_at":"2026-05-27T01:06:06Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26693v1","created_at":"2026-05-27T01:06:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26693","created_at":"2026-05-27T01:06:06Z"},{"alias_kind":"pith_short_12","alias_value":"SYPRT7MHZKDX","created_at":"2026-05-27T01:06:06Z"},{"alias_kind":"pith_short_16","alias_value":"SYPRT7MHZKDXNZKC","created_at":"2026-05-27T01:06:06Z"},{"alias_kind":"pith_short_8","alias_value":"SYPRT7MH","created_at":"2026-05-27T01:06:06Z"}],"graph_snapshots":[{"event_id":"sha256:49e214b37c740c8e82e2d8f3eb1b3e54c1d4e6ef433bcb9583680db975d11494","target":"graph","created_at":"2026-05-27T01:06:06Z","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/2605.26693/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the geometry of the loss landscape or rely on intractable full-space Hessian approximations. We propose EpiMer, a framework that casts model merging as solving the Fr\\'echet mean on a Riemannian manifold and restricts the computation to a low-rank subspace spanned by the task vectors. With the expected Hessian as the metric, we reveal a connection between local curvature and epistemic uncertainty of the parameters. Our theoretical analysis decompose","authors_text":"Anand Bhaskar, Brian Axelrod, Ekaterina Tolstaya, Juanwu Lu, Tristan Emrich","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T08:33:30Z","title":"Model Merging on Loss Landscape: A Geometry Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26693","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":"sha256:94497f6a850e11356b9395dc37eaa49df76ac8213071cf3ee32887d8e5f96128","target":"record","created_at":"2026-05-27T01:06:06Z","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":"dca5337b7825e2762e448cf6f14d486b47ae090bcaf630dd113405a73ed99d93","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-26T08:33:30Z","title_canon_sha256":"ba57d673620176c47a3bf1c45cee033bcf99f75e6b6a03a08f574283f5aae1c3"},"schema_version":"1.0","source":{"id":"2605.26693","kind":"arxiv","version":1}},"canonical_sha256":"961f19fd87ca8776e542f8725c0fd208a0d38070dabd41d4aec36c4e6613445b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"961f19fd87ca8776e542f8725c0fd208a0d38070dabd41d4aec36c4e6613445b","first_computed_at":"2026-05-27T01:06:06.419692Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:06:06.419692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bTPD5cXHb8kbdvmtQO4XoYgHQaP3yZmPcJXeUhWQ7njo4GyH/sa5XaVNQcp5cxkTdk5uvbHXt/fweQPwKfCYCQ==","signature_status":"signed_v1","signed_at":"2026-05-27T01:06:06.420488Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.26693","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94497f6a850e11356b9395dc37eaa49df76ac8213071cf3ee32887d8e5f96128","sha256:49e214b37c740c8e82e2d8f3eb1b3e54c1d4e6ef433bcb9583680db975d11494"],"state_sha256":"ada865d16e2e4fdc6ee65b2cc09bc87fe92c17260d0c40d2f277ada1af0793f2"}