{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PFWGUHJPUNQFSD57MMMUEVRFND","short_pith_number":"pith:PFWGUHJP","canonical_record":{"source":{"id":"2602.11715","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-12T08:45:13Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"64734884736047034060221e23df066a9113f5da645f349b791e609b1ceefae8","abstract_canon_sha256":"f73a09dd313fbc74165a0e8c49cc6e4c8b223665e395f9323f27e67b4c41cb46"},"schema_version":"1.0"},"canonical_sha256":"796c6a1d2fa360590fbf631942562568d667a33d3b1c86407f661816fdbb8af4","source":{"kind":"arxiv","id":"2602.11715","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.11715","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"arxiv_version","alias_value":"2602.11715v2","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.11715","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_12","alias_value":"PFWGUHJPUNQF","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFWGUHJPUNQFSD57","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFWGUHJP","created_at":"2026-06-19T16:09:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PFWGUHJPUNQFSD57MMMUEVRFND","target":"record","payload":{"canonical_record":{"source":{"id":"2602.11715","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-12T08:45:13Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"64734884736047034060221e23df066a9113f5da645f349b791e609b1ceefae8","abstract_canon_sha256":"f73a09dd313fbc74165a0e8c49cc6e4c8b223665e395f9323f27e67b4c41cb46"},"schema_version":"1.0"},"canonical_sha256":"796c6a1d2fa360590fbf631942562568d667a33d3b1c86407f661816fdbb8af4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:09:54.956235Z","signature_b64":"QieZ4a/n0+zfuOIHx9ks3bXDIjMNJRAeZmSTKLMp6+4Wff+hmHOqPDaxm8FImU4JQm72Dt/7a2UMnuJl0B+cCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"796c6a1d2fa360590fbf631942562568d667a33d3b1c86407f661816fdbb8af4","last_reissued_at":"2026-06-19T16:09:54.955796Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:09:54.955796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.11715","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":"2026-06-19T16:09:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66wB5T0vKEx4QL2Ao7+4Bmz9uNge9yKdNW7KrBbtVPL1AqWkYmYdXlQXRkdgCljLcFPuOiZ7hBn6hAUJ7yjyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T20:10:15.868214Z"},"content_sha256":"9b8e79583cbf29de35f9ec20e1a25c93155fc908d109800275906a482f4e83fd","schema_version":"1.0","event_id":"sha256:9b8e79583cbf29de35f9ec20e1a25c93155fc908d109800275906a482f4e83fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PFWGUHJPUNQFSD57MMMUEVRFND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Haolei Bai, Huan Wang, Jianmian Wang, Lingcheng Kong, Xueyi Chen, Zhiqiang Tao","submitted_at":"2026-02-12T08:45:13Z","abstract_excerpt":"Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation. This paradigm is particularly well-suited for code generation, where holistic structural planning and non-sequential refinement are critical. Despite this potential, tailoring dLLMs for CUDA kernel generation remains challenging, obstructed not only by the high specialization but also by the severe lack of high-quality training data. To address these challenges, we construct CuKe, an augmented supervised fine-tuning dataset optimiz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.11715","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/2602.11715/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":"2026-06-19T16:09:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vgvRJ0+GirA48R+O1+hiYwvBPPyODuBr6PncHHNeo3JHGXXR8xK7EvlDVb1iBPquuatFoRhG8+6cGBBzy5+FDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T20:10:15.868607Z"},"content_sha256":"a8f3e7dba16079ea0e7f11b2ee5fb1d6779fbacb714dee96c551709d161331d3","schema_version":"1.0","event_id":"sha256:a8f3e7dba16079ea0e7f11b2ee5fb1d6779fbacb714dee96c551709d161331d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PFWGUHJPUNQFSD57MMMUEVRFND/bundle.json","state_url":"https://pith.science/pith/PFWGUHJPUNQFSD57MMMUEVRFND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PFWGUHJPUNQFSD57MMMUEVRFND/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":"2026-06-22T20:10:15Z","links":{"resolver":"https://pith.science/pith/PFWGUHJPUNQFSD57MMMUEVRFND","bundle":"https://pith.science/pith/PFWGUHJPUNQFSD57MMMUEVRFND/bundle.json","state":"https://pith.science/pith/PFWGUHJPUNQFSD57MMMUEVRFND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PFWGUHJPUNQFSD57MMMUEVRFND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PFWGUHJPUNQFSD57MMMUEVRFND","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":"f73a09dd313fbc74165a0e8c49cc6e4c8b223665e395f9323f27e67b4c41cb46","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-12T08:45:13Z","title_canon_sha256":"64734884736047034060221e23df066a9113f5da645f349b791e609b1ceefae8"},"schema_version":"1.0","source":{"id":"2602.11715","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.11715","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"arxiv_version","alias_value":"2602.11715v2","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.11715","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_12","alias_value":"PFWGUHJPUNQF","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFWGUHJPUNQFSD57","created_at":"2026-06-19T16:09:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFWGUHJP","created_at":"2026-06-19T16:09:54Z"}],"graph_snapshots":[{"event_id":"sha256:a8f3e7dba16079ea0e7f11b2ee5fb1d6779fbacb714dee96c551709d161331d3","target":"graph","created_at":"2026-06-19T16:09:54Z","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/2602.11715/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation. This paradigm is particularly well-suited for code generation, where holistic structural planning and non-sequential refinement are critical. Despite this potential, tailoring dLLMs for CUDA kernel generation remains challenging, obstructed not only by the high specialization but also by the severe lack of high-quality training data. To address these challenges, we construct CuKe, an augmented supervised fine-tuning dataset optimiz","authors_text":"Haolei Bai, Huan Wang, Jianmian Wang, Lingcheng Kong, Xueyi Chen, Zhiqiang Tao","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-12T08:45:13Z","title":"DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.11715","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":"sha256:9b8e79583cbf29de35f9ec20e1a25c93155fc908d109800275906a482f4e83fd","target":"record","created_at":"2026-06-19T16:09:54Z","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":"f73a09dd313fbc74165a0e8c49cc6e4c8b223665e395f9323f27e67b4c41cb46","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-12T08:45:13Z","title_canon_sha256":"64734884736047034060221e23df066a9113f5da645f349b791e609b1ceefae8"},"schema_version":"1.0","source":{"id":"2602.11715","kind":"arxiv","version":2}},"canonical_sha256":"796c6a1d2fa360590fbf631942562568d667a33d3b1c86407f661816fdbb8af4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"796c6a1d2fa360590fbf631942562568d667a33d3b1c86407f661816fdbb8af4","first_computed_at":"2026-06-19T16:09:54.955796Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:09:54.955796Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QieZ4a/n0+zfuOIHx9ks3bXDIjMNJRAeZmSTKLMp6+4Wff+hmHOqPDaxm8FImU4JQm72Dt/7a2UMnuJl0B+cCA==","signature_status":"signed_v1","signed_at":"2026-06-19T16:09:54.956235Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.11715","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b8e79583cbf29de35f9ec20e1a25c93155fc908d109800275906a482f4e83fd","sha256:a8f3e7dba16079ea0e7f11b2ee5fb1d6779fbacb714dee96c551709d161331d3"],"state_sha256":"f35521713bb62da5f18726ae30e540bc7f9c2ee79de25585318f44d15cf3f65d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ECnQQ03TZ3xsE1kgL+ZMv+h0YwxdeUr/ouEnS5YdyPOGr6cqOBPLbrJ7vYavzYtQB42YQWJTH5qTekOVhNCDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T20:10:15.870542Z","bundle_sha256":"d63d50a01a36baea96b090e53a93380701b5dad5aa9c4b5edfcd1ed26b6625c5"}}