{"as_of":"2026-08-09T11:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3359f56220196a19fc6186f4b9641fe8cfe542b1f8116608523a38369926678c","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T04:35:53.249194Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.11586/citation-record","integrity":"/paper/2607.11586/integrity","json":"/paper/2607.11586/citation-record.json","paper":"/paper/2607.11586"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Fedus, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:723923375b6b4ce337fd51e3693079ef990b35a49c6e5dd9d2ee2c072db591e3","observation_id":"0f98dd16-3d4e-4aab-bccb-875d10d0b6e1","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04088","last_updated":"2024-01-08T18:47:34Z","snapshot_observed_at":"2026-08-08T06:16:25.839566Z","submitted_at":"2024-01-08T18:47:34Z","title":"Mixtral of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04088","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2401.04088","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:8feffe7260cb60045aec6a56a20ac8b3a88c6c2b8f56168bc2502599fad31c00","observation_id":"de6dcb08-5831-43e8-8552-108456f6544a","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.06066","last_updated":"2024-01-11T17:31:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-11T17:31:42Z","title":"DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.06066","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2401.06066","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:db5da5ee986d1e4f432709b864c20bae7e45c58a6046c50f223ce39b2ce296ca","observation_id":"b7b70b3a-53a7-45c3-b404-3355e33c66e3","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Yang, et al., Qwen3 technical report (2025).arXiv:2505.09388","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:631dc7885b660fab6a17bad606ba624f0a71c3749dfd65f525bc1ab792692778","observation_id":"4665e30c-2f3d-4330-a641-ea8e72fe0f12","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Hwang, W","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:12760b27654cdecfa49a3e87384de31736cbae21e6c82f41d3b81c143b18c8fc","observation_id":"dbc9663b-a1cf-47e1-9250-198d425b1b9e","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:5fa471f1b85e8f772d302595960be4c1a44bd310d8837c314b29e29c16943337","observation_id":"c08e9a72-3c2e-46a7-9a2d-cff4eaac93ad","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:7d1aaa79bc2f6fd092ca21480a7dc315d1c76362af7894ad805ec8e106181228","observation_id":"45e77b1a-d78b-4cc3-a1c5-0e2af1f3974d","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.04101","last_updated":"2026-06-18T09:05:09Z","snapshot_observed_at":"2026-08-06T18:51:08.635305Z","submitted_at":"2026-06-02T18:07:51Z","title":"UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.04101","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2606.04101","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:d7255c5bf79a7bb9f9b6b3544debc3b2a7268fb2cf355d233eb9f050b0879943","observation_id":"354ecbd9-eb8d-4d1c-88d3-558b63e0b2d6","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:ca3e67b0dfac23d3517363dc993f9f478511b0c84bf3212582350c4a98ee6f80","observation_id":"3ed0dbe9-4946-4b42-809c-5359c2d3625c","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:274e53429c0eb7d447ec449c59ef50faaee918653fee7d33a78a8f08c82c1ef0","observation_id":"87ccfdf8-7f90-4255-a1f3-a1dfd6282798","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Liang, K.-T","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:888050c7917b2bd20c9981b7e1f50265999e4e2ce71430319f5bd7c71ec13ffc","observation_id":"fea194e6-1595-4c23-989d-ce2c70031ad5","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.28768","last_updated":"2026-05-29T18:36:29Z","snapshot_observed_at":"2026-08-03T11:02:17.034733Z","submitted_at":"2026-01-12T19:25:01Z","title":"CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.28768","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2603.28768","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:9c2cf343491bab23cb4d0cb2439ce1466a1bac8cc912a41eabf6d2f3c5786a5e","observation_id":"478938f5-78a4-40f7-8804-d33229afd9df","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.25041","last_updated":"2026-05-06T06:43:02Z","snapshot_observed_at":"2026-08-03T04:43:05.899007Z","submitted_at":"2025-09-29T16:57:33Z","title":"GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.25041","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2509.25041","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:9fd952492c118cdcf2cf1ad37fd1e38747e989bfc1ccbfdcfed8accd29ea94a2","observation_id":"a7a506ce-88a0-4b22-9e76-0e3db1d59b9a","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:5216701fecc91a95d019e81b16853fe89e17095e20ae4240bde64116fb5908c1","observation_id":"c868f33c-fe66-4cbe-b058-3748e269a597","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2285.375990","doi":"10.1145/3712285.3759903","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"487507f9-719e-42b2-a7d1-7ae343639b47","year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:56b34b998d782b3b26bc66e5a5393615a79733ba7cb55ac3068183a2429a87d7","observation_id":"3393ca8f-127f-46c1-b805-66f67ed34d5d","resolution":{"observed_at":"2026-07-14T04:40:13.854036Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:20:08.574831+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:20:08.574831+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:a8954b40ee996293583b44079383e044403d131829007d6407fcac46346c5796","observation_id":"5873342f-e16a-4d1a-8e92-872094a343f4","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:3ce4fede80d2e83acbd5d24ef23cb14b4846ab2e3aec43645f84505a6b845bc9","observation_id":"28ffaf98-9cd3-415c-87e6-b58b4c1c821c","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:3f92b631b33d979deca139a2765c319152f207bbe17e482f901b0772037a87cc","observation_id":"1210f5d4-6620-4053-8b64-4719a10cff77","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:1154b8e7320073c76a06b91916e4e39bbd7c55425697c64c2876a54794f36d3d","observation_id":"8bc87d3b-ad37-41a5-8b7c-ea9d1ff72a73","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Accessed: 2026-07-05 (2026)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:91084125d7effb58435c01bb5656f47034b923a77ee26c22117a0ad9a2d302ef","observation_id":"9ef0284a-f219-4609-a827-866716f7fae3","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.00224","last_updated":"2026-04-30T20:59:49Z","snapshot_observed_at":"2026-07-06T23:13:42.661364Z","submitted_at":"2026-04-30T20:59:49Z","title":"TUR-DPO: Topology- and Uncertainty-Aware Direct Preference Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.00224","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"He, A Time Scaling Theory for Multi-Layer Electronic Systems, ChinaXiv:202605.00224 (May 2026)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2605.00224","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:2ae32277e3663d2bc1fa433b5f9320f550f51282789aabd3de215ce248c3b011","observation_id":"c8296d58-c2da-4aa1-a5f3-e30a18f25a99","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/icpads.2015.72","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"525–534.doi:10.1109/ICPADS.2015.72","venue":null,"work_id":"7bb07009-e6d7-4406-9008-e829184b94bc","year":2015},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:f2e2e3a6336cf296fb30af7d83a9a60dffbe3c454fb4c9f3719bb52096bac570","observation_id":"6cd8c052-bb3e-41d4-83a1-61024e8f6cd7","resolution":{"observed_at":"2026-07-14T04:40:13.851611Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:20:09.127722+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:20:09.127722+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/cpe.4485","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Haidar, H","venue":"Concurrency and Computation Practice and Experience","work_id":"403c0f98-c464-4af9-8039-128b6c4a0d4a","year":2018},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:67014022ae1283b63ddf30c0bf2595d2bf0e5e14dbb00d9fb850814148071f7f","observation_id":"096fff8c-7071-41b3-b39a-e7a5f84a67e8","resolution":{"observed_at":"2026-07-14T04:40:13.847737Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-07-14T07:20:09.358771+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T07:20:09.358771+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Accessed: 2026-06-30 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:5e970315c5b5cb363edd29f253dcbaa4e70eb5217a0e531aa9fb7236e45bd092","observation_id":"eaabad9a-b865-42f5-80d2-a8ba55439388","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Accessed: 2026-06-30 (2026)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:e74af484241d831df71afbf4f88e295578cd883cc185a115f58d52aace6c1202","observation_id":"03468afb-59a7-4b40-99f1-350d657b6451","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:7c8d82ea3719df9879b6cb9815be4de3f524b3c8a77fda33315f915629aa929f","observation_id":"59fcced1-3b10-4c9f-bcdc-6141952d367b","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Horowitz, 1.1 computing’s energy problem (and what we can do aboutit),in:2014IEEEInternationalSolid-StateCircuitsConference DigestofTechnicalPapers(ISSCC),Vol.57,IEEE,2014,pp.10–14","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:05778e163589ccfa132f27bb9cae3a4c39080fbfe0a39b749b4b469e7a319952","observation_id":"43816e75-d3cf-4b6b-b898-c048a6481571","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"Accessed: 2026-06-30 (2023)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:466aee0b9f3ed3ddc33bacefdc02d92083b559558ee2cbe49051ca42a492b072","observation_id":"9bd340e2-fcb7-4d66-a544-a62ff560d74e","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"O’Connor, N","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:977a44e7b4e5a3cf1317796d5b680937d5ced8d4ca3798453b70e2bfeb8c7c5b","observation_id":"01bfaf51-e788-4ac2-aa6a-bd83bc86b0b8","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"URLhttps://static6.arrow.com/aropdfconversion/fc2b144ccf06116 0504edd742d01cb58fdda91bb/ddr5_sdram_core.pdf","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:55752f02d5de3d3bbaaf63491ba5b872227afcb5fc3e21d0f618641d014c3548","observation_id":"e7843903-c460-4a5c-8baf-431dd7c05621","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"URLhttps://www.databricks.com/blog/introducing-dbrx-new-state -art-open-llm","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:a1bf0ccec46813b636aee913b7b06beb0d4357ca2d1f00fbedc9d6c2cf623044","observation_id":"359d603a-f62e-4ce0-b827-89c62c7f0a41","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04434","last_updated":"2024-06-19T06:04:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-07T15:56:43Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04434","snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"cited_paper":"/paper/2405.04434","citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:0a742ad72881dffe4d77c8c2db573ac05a9f3cd594f11027de4b304e63b037bf","observation_id":"2f7c6191-a995-4d10-ac3b-915a2409eec4","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:ffb21f14f04479ef0de24ddaf39663e9d2f9844c4c17bdb2c5add61147b3373c","observation_id":"be034083-5537-447b-a175-11b7d7a7ad83","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"URLhttps://mistral.ai/news/mixtral-8x22b","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:2cdeadfece19769ca350462ec0becf60c7536847eab94761743f530fb5e9a442","observation_id":"fb497462-fc15-4b63-b0c7-b7ea33b280d0","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T04:35:53.249194Z","title":"URLhttps://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B Y","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T04:35:53.249194Z"},"links":{"citing_paper":"/paper/2607.11586"},"observation_digest":"sha256:e380b390f78e2f6e12d9b64a124d299186ed95342bc7c12c7061aecb942b0ba2","observation_id":"39ad8150-01d7-4920-b09f-7fdfa3320a9d","resolution":{"observed_at":"2026-07-14T04:35:53.249194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11586","last_updated":"2026-07-13T14:05:55Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-16T23:19:54.710114Z","submitted_at":"2026-07-13T14:05:55Z","title":"HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":35},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.11586."}