{"as_of":"2026-08-09T13:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1a3cd0a4a747601fc86633a7b9f1e6b6bfbb4ce9be409ae1b707d45fb99ae9d5","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T17:26:28.891630Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T06:29:38.231668Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2505.06708","last_updated":"2025-05-10T17:15:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-10T17:15:49Z","title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-12T09:04:34.807225Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2505.06708"},"observation_digest":"sha256:b8f572119c5cdf2b620d40dc5eedef31f19f988a9f47797c2882593bb356fd8b","observation_id":"e8f5c733-853d-4c28-a2db-c92d08ce8e6e","resolution":{"observed_at":"2026-05-12T09:04:34.915567Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_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},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-09T06:35:27.813995Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2505.09388"},"observation_digest":"sha256:1df360d399b3c08c8cd0f582cb4c604f9b418f2b462c8bcc2ec6b0e251e4d7e3","observation_id":"3fb5fb12-d4c3-4807-a719-3be2e722a6dd","resolution":{"observed_at":"2026-05-09T06:35:28.577251Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-08-05T17:26:28.891630Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.16279","last_updated":"2025-08-22T10:35:56Z","snapshot_observed_at":"2026-08-07T15:35:45.566152Z","submitted_at":"2025-08-22T10:35:56Z","title":"AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-05T17:26:28.891630Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2508.16279"},"observation_digest":"sha256:53393b763cacb5a26f3bea70478ea7bb00ff2d7821d42ec038e1c17a057fa0bf","observation_id":"a55a3517-79b9-4275-87dd-907dd808aa42","resolution":{"observed_at":"2026-08-05T17:26:28.891630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2509.19349","last_updated":"2025-09-17T17:49:02Z","snapshot_observed_at":"2026-08-08T12:04:51.587933Z","submitted_at":"2025-09-17T17:49:02Z","title":"ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution","version":1},"reference_index":246,"source":"arxiv_source","source_observed_at":"2026-05-16T13:58:58.627748Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2509.19349"},"observation_digest":"sha256:25225fac883de285703e0d39e65abdedc8e85f8d09ab874401c0318b12790009","observation_id":"37873acc-e3d7-48bd-8a28-adbbf0a517ba","resolution":{"observed_at":"2026-05-16T13:58:59.050128Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2512.05564","last_updated":"2026-04-12T02:52:57Z","snapshot_observed_at":"2026-07-06T22:37:50.431948Z","submitted_at":"2025-12-05T09:39:26Z","title":"ProPhy: Progressive Physical Alignment for Dynamic World Simulation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-17T01:05:08.087136Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2512.05564"},"observation_digest":"sha256:55ae2ce36ac0b9bb0f56fe0e95c37bc4c26307543de752027519d4dbeae59b7a","observation_id":"c0f700ef-2573-4db8-80a5-f9fbd5a97679","resolution":{"observed_at":"2026-05-17T01:08:47.973460Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2605.08738","last_updated":"2026-05-18T06:29:11Z","snapshot_observed_at":"2026-07-06T23:20:57.084438Z","submitted_at":"2026-05-09T06:50:35Z","title":"SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-12T03:34:10.370956Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2605.08738"},"observation_digest":"sha256:97fd9c282ccd55ae4c9123ed963abc448d75db5fb39fa6dfefa00d60c5767759","observation_id":"f4f170e1-5c34-4fc9-82d0-295786056337","resolution":{"observed_at":"2026-05-12T07:16:28.419367Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2605.08738","last_updated":"2026-05-18T06:29:11Z","snapshot_observed_at":"2026-07-06T23:20:57.084438Z","submitted_at":"2026-05-09T06:50:35Z","title":"SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-20T23:22:51.808346Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2605.08738"},"observation_digest":"sha256:93308213f93e76f0e82e21889e3274bf6740631bac11db95362250430171ae0b","observation_id":"a4d7779a-5cac-47e0-9902-a895b78ff088","resolution":{"observed_at":"2026-05-20T23:23:51.260199Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2605.16690","last_updated":"2026-05-15T23:06:59Z","snapshot_observed_at":"2026-07-06T23:27:48.303599Z","submitted_at":"2026-05-15T23:06:59Z","title":"UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-20T19:06:08.951475Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2605.16690"},"observation_digest":"sha256:04388e819a1adcdf4bc94f3fdea6340789e287af940248647bd91d0fb1b9083a","observation_id":"099bf210-82e4-4240-8e4b-af340a8dc30a","resolution":{"observed_at":"2026-05-20T19:08:54.313335Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2605.31463","last_updated":"2026-05-29T15:52:58Z","snapshot_observed_at":"2026-08-03T10:19:30.254575Z","submitted_at":"2026-05-29T15:52:58Z","title":"PithTrain: A Compact and Agent-Native MoE Training System","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T23:12:14.193089Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2605.31463"},"observation_digest":"sha256:2d7e94d350ca48d33e77ed2de4e027889de77b0714778a75303df9bef54e8dc7","observation_id":"3e42793d-2843-4518-a6e6-d98f4fe431bc","resolution":{"observed_at":"2026-06-28T23:12:46.524678Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2606.01062","last_updated":"2026-05-31T07:08:16Z","snapshot_observed_at":"2026-07-06T23:41:39.172310Z","submitted_at":"2026-05-31T07:08:16Z","title":"DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-06-28T17:14:53.648013Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2606.01062"},"observation_digest":"sha256:6ded826febd9be2bc315607b7791b23ac719d101b044a093851683c311f7c24b","observation_id":"58a1a656-1b11-4128-b2d9-0c3fb1c34225","resolution":{"observed_at":"2026-07-01T21:16:14.421840Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2606.08814","last_updated":"2026-06-07T20:07:24Z","snapshot_observed_at":"2026-08-02T23:58:50.946844Z","submitted_at":"2026-06-07T20:07:24Z","title":"STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T18:28:35.162934Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2606.08814"},"observation_digest":"sha256:fd271afd0943811904ab9b276c3eff3c6f738a55980e5cd0eb5c078e905a9e5b","observation_id":"8e376c96-e784-4433-b402-e7650c3cbfc6","resolution":{"observed_at":"2026-07-02T23:07:26.934730Z","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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2606.21228","last_updated":"2026-06-23T04:40:39Z","snapshot_observed_at":"2026-08-02T19:24:45.644999Z","submitted_at":"2026-06-19T08:47:40Z","title":"Sakana Fugu Technical Report","version":2},"reference_index":207,"source":"arxiv_source","source_observed_at":"2026-06-26T14:22:37.596720Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2606.21228"},"observation_digest":"sha256:73e6ff0cac407bd7e870748b574d5e31d40831dc5a550df6d04d2275afd401a1","observation_id":"0c5e3505-ab35-4928-9905-18b88a6de820","resolution":{"observed_at":"2026-07-04T06:29:38.233271Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":"2501.11873","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-07-04T06:29:38.231668Z","title":"Demons in the detail: On implementing load balancing loss for training specialized mixture-of-expert models, 2025 a","venue":null,"work_id":"1625236e-e6d2-4e4d-bc26-db51815098e7","year":2025},"citing_paper":{"arxiv_id":"2606.28835","last_updated":"2026-06-27T09:45:52Z","snapshot_observed_at":"2026-08-01T12:29:02.113079Z","submitted_at":"2026-06-27T09:45:52Z","title":"Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-30T10:19:16.463961Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2606.28835"},"observation_digest":"sha256:896c757a0459258b4f157ba75e173726b356d62fd22da146c7a8549884bb149c","observation_id":"834c0362-1b70-4fbe-9cf8-2149a4c4dd79","resolution":{"observed_at":"2026-06-30T12:04:39.217496Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11873","snapshot_observed_at":"2026-08-05T00:38:33.233904Z","title":"arXiv:2501.11873","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00574","last_updated":"2026-08-01T10:23:04Z","snapshot_observed_at":"2026-08-07T09:45:34.726642Z","submitted_at":"2026-08-01T10:23:04Z","title":"Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T00:38:33.233904Z"},"links":{"cited_paper":"/paper/2501.11873","citing_paper":"/paper/2608.00574"},"observation_digest":"sha256:4a4259e78d1f1cb372c21228b709b9004463c6e99bc604990c25e2dc78f6290b","observation_id":"a6bb9d1a-5b2b-484e-952f-d80ec9951353","resolution":{"observed_at":"2026-08-05T00:38:33.233904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2501.11873/citation-record","integrity":"/paper/2501.11873/integrity","json":"/paper/2501.11873/citation-record.json","paper":"/paper/2501.11873"},"outbound":[],"paper":{"arxiv_id":"2501.11873","last_updated":"2025-02-04T02:07:37Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:04:39Z","title":"Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2501.11873."}