{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HWL2M7GKIU5YAB2YEDCSFWKEJ6","short_pith_number":"pith:HWL2M7GK","canonical_record":{"source":{"id":"2508.14783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T15:29:00Z","cross_cats_sorted":[],"title_canon_sha256":"22139db8840154972db9ff9a02ca70f968b2b3b766bb2d2e046331bbea0dbdaa","abstract_canon_sha256":"6a3399e5b534fd6dd821d739a2729404e83b124a8995b49f7a53b94db1d0afa2"},"schema_version":"1.0"},"canonical_sha256":"3d97a67cca453b80075820c522d9444fb2bb3c9c6b971443da99ea787d7bd2ab","source":{"kind":"arxiv","id":"2508.14783","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.14783","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"arxiv_version","alias_value":"2508.14783v1","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14783","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_12","alias_value":"HWL2M7GKIU5Y","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_16","alias_value":"HWL2M7GKIU5YAB2Y","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_8","alias_value":"HWL2M7GK","created_at":"2026-07-05T11:56:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HWL2M7GKIU5YAB2YEDCSFWKEJ6","target":"record","payload":{"canonical_record":{"source":{"id":"2508.14783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T15:29:00Z","cross_cats_sorted":[],"title_canon_sha256":"22139db8840154972db9ff9a02ca70f968b2b3b766bb2d2e046331bbea0dbdaa","abstract_canon_sha256":"6a3399e5b534fd6dd821d739a2729404e83b124a8995b49f7a53b94db1d0afa2"},"schema_version":"1.0"},"canonical_sha256":"3d97a67cca453b80075820c522d9444fb2bb3c9c6b971443da99ea787d7bd2ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:38.410925Z","signature_b64":"4JE8IpHYF0YwS0TR9JbWeEbr4dokozvIJvd87xmeKNKjeLWsW1BI71Awp2QfMAjTBFmUdW8OMeobm4LUo/m5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d97a67cca453b80075820c522d9444fb2bb3c9c6b971443da99ea787d7bd2ab","last_reissued_at":"2026-07-05T11:56:38.410541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:38.410541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.14783","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":"2026-07-05T11:56:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tWlwCpgGZX1RGF3X2nbcgTIWgeijtO4ospMw6oKdKfI3x7Ccl//yFMCHVKc+jD3Y7cAOO7uw/ub4WdRQZrexBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:34:39.901492Z"},"content_sha256":"44644175487d3d423a4f953e6f6276fefa84df4ed19fc769245860faee11bb74","schema_version":"1.0","event_id":"sha256:44644175487d3d423a4f953e6f6276fefa84df4ed19fc769245860faee11bb74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HWL2M7GKIU5YAB2YEDCSFWKEJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mark V. Albert, Poli A. Nemkova, Suleyman Olcay Polat","submitted_at":"2025-08-20T15:29:00Z","abstract_excerpt":"Model distillation enables the transfer of knowledge from large-scale models to compact student models, facilitating deployment in resource-constrained environments. However, conventional distillation approaches often suffer from computational overhead and limited generalization. We propose a novel adaptive distillation framework that dynamically augments training data in regions of high student model loss. Using UMAP-based dimensionality reduction and nearest neighbor sampling, our method identifies underperforming regions in the embedding space and generates targeted synthetic examples to gu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14783","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/2508.14783/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":"2026-07-05T11:56:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lFRRLc4zD+gRATTa+FBT9frBt0R8BzsYPkQjhQOQyCujzEXasBOKiITGeycBr5p2gz1ErtBN3KkE3fcxCULoDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T21:34:39.902395Z"},"content_sha256":"9fd637c6d3c04fb30ca27773d7159189786cefb6f1113d6398a66ab6f3aaaa29","schema_version":"1.0","event_id":"sha256:9fd637c6d3c04fb30ca27773d7159189786cefb6f1113d6398a66ab6f3aaaa29"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/bundle.json","state_url":"https://pith.science/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/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":"2026-08-03T21:34:39Z","links":{"resolver":"https://pith.science/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6","bundle":"https://pith.science/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/bundle.json","state":"https://pith.science/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HWL2M7GKIU5YAB2YEDCSFWKEJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HWL2M7GKIU5YAB2YEDCSFWKEJ6","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":"6a3399e5b534fd6dd821d739a2729404e83b124a8995b49f7a53b94db1d0afa2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T15:29:00Z","title_canon_sha256":"22139db8840154972db9ff9a02ca70f968b2b3b766bb2d2e046331bbea0dbdaa"},"schema_version":"1.0","source":{"id":"2508.14783","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.14783","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"arxiv_version","alias_value":"2508.14783v1","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14783","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_12","alias_value":"HWL2M7GKIU5Y","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_16","alias_value":"HWL2M7GKIU5YAB2Y","created_at":"2026-07-05T11:56:38Z"},{"alias_kind":"pith_short_8","alias_value":"HWL2M7GK","created_at":"2026-07-05T11:56:38Z"}],"graph_snapshots":[{"event_id":"sha256:9fd637c6d3c04fb30ca27773d7159189786cefb6f1113d6398a66ab6f3aaaa29","target":"graph","created_at":"2026-07-05T11:56:38Z","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/2508.14783/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model distillation enables the transfer of knowledge from large-scale models to compact student models, facilitating deployment in resource-constrained environments. However, conventional distillation approaches often suffer from computational overhead and limited generalization. We propose a novel adaptive distillation framework that dynamically augments training data in regions of high student model loss. Using UMAP-based dimensionality reduction and nearest neighbor sampling, our method identifies underperforming regions in the embedding space and generates targeted synthetic examples to gu","authors_text":"Mark V. Albert, Poli A. Nemkova, Suleyman Olcay Polat","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T15:29:00Z","title":"Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14783","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":"sha256:44644175487d3d423a4f953e6f6276fefa84df4ed19fc769245860faee11bb74","target":"record","created_at":"2026-07-05T11:56:38Z","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":"6a3399e5b534fd6dd821d739a2729404e83b124a8995b49f7a53b94db1d0afa2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T15:29:00Z","title_canon_sha256":"22139db8840154972db9ff9a02ca70f968b2b3b766bb2d2e046331bbea0dbdaa"},"schema_version":"1.0","source":{"id":"2508.14783","kind":"arxiv","version":1}},"canonical_sha256":"3d97a67cca453b80075820c522d9444fb2bb3c9c6b971443da99ea787d7bd2ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d97a67cca453b80075820c522d9444fb2bb3c9c6b971443da99ea787d7bd2ab","first_computed_at":"2026-07-05T11:56:38.410541Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:56:38.410541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4JE8IpHYF0YwS0TR9JbWeEbr4dokozvIJvd87xmeKNKjeLWsW1BI71Awp2QfMAjTBFmUdW8OMeobm4LUo/m5BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:56:38.410925Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.14783","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44644175487d3d423a4f953e6f6276fefa84df4ed19fc769245860faee11bb74","sha256:9fd637c6d3c04fb30ca27773d7159189786cefb6f1113d6398a66ab6f3aaaa29"],"state_sha256":"f0686a655589d9c2fa803382ccaa458a4bd8610f7fc97ca415facdcb327c26ba"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8YxQU1g7zTm6N5p1/ybfIw/Stx3qwy8854RHGCuaHDgpKj2hJVaMpC4MIb46pjg7KmG7uwhinNHZ3kk7j40zCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T21:34:39.909578Z","bundle_sha256":"43ed1bdf3d10c93f9f62e1671ff75792588679bc784a38e327e48eb6daf96ee2"}}