{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WJLR6S43UQI6POXUYSDIRZECTW","short_pith_number":"pith:WJLR6S43","canonical_record":{"source":{"id":"2607.11976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-13T03:53:56Z","cross_cats_sorted":[],"title_canon_sha256":"f33ea90912e47c96d69104aa611208986fff713fbe5b9a8b9f3ca43d3ed1a2e1","abstract_canon_sha256":"b59e90bb7e7a1e336d69f26cd7d5986a4610613aba802d22db7af18875d8eceb"},"schema_version":"1.0"},"canonical_sha256":"b2571f4b9ba411e7baf4c48688e4829db1b8c9bdc8af4b5462d217ab006e9112","source":{"kind":"arxiv","id":"2607.11976","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11976","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11976v1","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11976","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"WJLR6S43UQI6","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"WJLR6S43UQI6POXU","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"WJLR6S43","created_at":"2026-07-15T00:21:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WJLR6S43UQI6POXUYSDIRZECTW","target":"record","payload":{"canonical_record":{"source":{"id":"2607.11976","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-13T03:53:56Z","cross_cats_sorted":[],"title_canon_sha256":"f33ea90912e47c96d69104aa611208986fff713fbe5b9a8b9f3ca43d3ed1a2e1","abstract_canon_sha256":"b59e90bb7e7a1e336d69f26cd7d5986a4610613aba802d22db7af18875d8eceb"},"schema_version":"1.0"},"canonical_sha256":"b2571f4b9ba411e7baf4c48688e4829db1b8c9bdc8af4b5462d217ab006e9112","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T00:21:25.683044Z","signature_b64":"oE8I1PaOUAipggrzqqs8QTHqphki5PvFnnpRAbQ0iCj6xsSmHtucChYv131I/3NDXyj+3xxoSPHgx4shPYFQAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2571f4b9ba411e7baf4c48688e4829db1b8c9bdc8af4b5462d217ab006e9112","last_reissued_at":"2026-07-15T00:21:25.682257Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T00:21:25.682257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.11976","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":"2026-07-15T00:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6I5CY7MAllNDFKL8RdqVIZdwKWBu5pyrMiUR9Q0sNpLgdj3GxJgmKxDFKvBiWFwSe+h/bDDeoCrn5xUsCM/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:34:00.558052Z"},"content_sha256":"2254dcf5c2ac00360777579099579dc4814ac80fdc4a98062b520dbb8af672b4","schema_version":"1.0","event_id":"sha256:2254dcf5c2ac00360777579099579dc4814ac80fdc4a98062b520dbb8af672b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WJLR6S43UQI6POXUYSDIRZECTW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gao Cong, Jiangneng Li, Jianyang Gao, Peiqi Yin, Ziqi Yin","submitted_at":"2026-07-13T03:53:56Z","abstract_excerpt":"Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention kernels in large language models and vector retrieval in recommendation systems and vector databases. However, existing GPU-based Indexer-TopK kernels like DeepSeek Sparse Attention (DSA) remain inefficient due to excessive global memory traffic, costly synchronization, and prohibitive memory overhead. In this work, we exploit the curse of dimensionality in high-dimensional spaces, where distances between high-dimensional vectors tend to concentrate within a narrow range, to de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11976","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/2607.11976/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":"2026-07-15T00:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r3dCtNgCFUF+7uY66ZIERSvONiKcdaiTMeXaU/KLVKJODmWfhF/YadrpQ4IV5Q6GWY0qmz4k5TnE7E5BG8AcAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:34:00.559117Z"},"content_sha256":"a3b3cd16bfebcb2b7f0d20f0349a859d2d215800800ace608950a22599050040","schema_version":"1.0","event_id":"sha256:a3b3cd16bfebcb2b7f0d20f0349a859d2d215800800ace608950a22599050040"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WJLR6S43UQI6POXUYSDIRZECTW/bundle.json","state_url":"https://pith.science/pith/WJLR6S43UQI6POXUYSDIRZECTW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WJLR6S43UQI6POXUYSDIRZECTW/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":"2026-08-07T18:34:00Z","links":{"resolver":"https://pith.science/pith/WJLR6S43UQI6POXUYSDIRZECTW","bundle":"https://pith.science/pith/WJLR6S43UQI6POXUYSDIRZECTW/bundle.json","state":"https://pith.science/pith/WJLR6S43UQI6POXUYSDIRZECTW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WJLR6S43UQI6POXUYSDIRZECTW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WJLR6S43UQI6POXUYSDIRZECTW","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":"b59e90bb7e7a1e336d69f26cd7d5986a4610613aba802d22db7af18875d8eceb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-13T03:53:56Z","title_canon_sha256":"f33ea90912e47c96d69104aa611208986fff713fbe5b9a8b9f3ca43d3ed1a2e1"},"schema_version":"1.0","source":{"id":"2607.11976","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11976","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11976v1","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11976","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"WJLR6S43UQI6","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"WJLR6S43UQI6POXU","created_at":"2026-07-15T00:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"WJLR6S43","created_at":"2026-07-15T00:21:25Z"}],"graph_snapshots":[{"event_id":"sha256:a3b3cd16bfebcb2b7f0d20f0349a859d2d215800800ace608950a22599050040","target":"graph","created_at":"2026-07-15T00:21:25Z","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/2607.11976/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention kernels in large language models and vector retrieval in recommendation systems and vector databases. However, existing GPU-based Indexer-TopK kernels like DeepSeek Sparse Attention (DSA) remain inefficient due to excessive global memory traffic, costly synchronization, and prohibitive memory overhead. In this work, we exploit the curse of dimensionality in high-dimensional spaces, where distances between high-dimensional vectors tend to concentrate within a narrow range, to de","authors_text":"Gao Cong, Jiangneng Li, Jianyang Gao, Peiqi Yin, Ziqi Yin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-13T03:53:56Z","title":"LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11976","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":"sha256:2254dcf5c2ac00360777579099579dc4814ac80fdc4a98062b520dbb8af672b4","target":"record","created_at":"2026-07-15T00:21:25Z","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":"b59e90bb7e7a1e336d69f26cd7d5986a4610613aba802d22db7af18875d8eceb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-13T03:53:56Z","title_canon_sha256":"f33ea90912e47c96d69104aa611208986fff713fbe5b9a8b9f3ca43d3ed1a2e1"},"schema_version":"1.0","source":{"id":"2607.11976","kind":"arxiv","version":1}},"canonical_sha256":"b2571f4b9ba411e7baf4c48688e4829db1b8c9bdc8af4b5462d217ab006e9112","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b2571f4b9ba411e7baf4c48688e4829db1b8c9bdc8af4b5462d217ab006e9112","first_computed_at":"2026-07-15T00:21:25.682257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-15T00:21:25.682257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oE8I1PaOUAipggrzqqs8QTHqphki5PvFnnpRAbQ0iCj6xsSmHtucChYv131I/3NDXyj+3xxoSPHgx4shPYFQAQ==","signature_status":"signed_v1","signed_at":"2026-07-15T00:21:25.683044Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.11976","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2254dcf5c2ac00360777579099579dc4814ac80fdc4a98062b520dbb8af672b4","sha256:a3b3cd16bfebcb2b7f0d20f0349a859d2d215800800ace608950a22599050040"],"state_sha256":"a10a37180246b2b7833d2d83ec06746eefa68503a1cb3fbcd046a90f70cea127"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jnta9jv7L2KNhzKoN815KxbHO8yuF7sXgS0a5mYK61t6zteYBkECVpT+Yk3dI5mwcinWkn9oNvJxZ41x1jyoAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:34:00.565293Z","bundle_sha256":"5cf736d864418ec5fddc3fd56520cd50b281f73244e8e380a994180933a0ced1"}}