{"as_of":"2026-08-10T13:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e97412d5b7cf8c43d176ab847ed9b2f89fa08c34f9378a53588e2286d6334a5","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:51:53.154620Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2505.17420/citation-record","integrity":"/paper/2505.17420/integrity","json":"/paper/2505.17420/citation-record.json","paper":"/paper/2505.17420"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T14:51:51.340639Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.340639Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:b62c2070ed65bead02090a47e953b748a7b5c30090544e945d224f3359b52596","observation_id":"e01a6648-6985-4ca2-8f98-16d4141d8053","resolution":{"observed_at":"2026-08-07T14:51:51.340639Z","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":"2026-08-07T14:51:51.362700Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.362700Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:64739b66a347fc1717963652209161b8c990c3c7f27e530ca10df9a8554a2628","observation_id":"c38aa4c6-73ca-4688-8c6c-c156e5555b90","resolution":{"observed_at":"2026-08-07T14:51:51.362700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-07T14:51:51.422845Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.422845Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:a317314d7796b6e74ee9f540b6a49ae805dcf2444bda30e20a18fc7a461b5f67","observation_id":"8d4a2d47-2c56-452f-9eb4-b5de198700b0","resolution":{"observed_at":"2026-08-07T14:51:51.422845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02628","last_updated":"2023-07-05T19:59:09Z","snapshot_observed_at":"2026-08-10T10:34:53.296935Z","submitted_at":"2023-07-05T19:59:09Z","title":"SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02628","snapshot_observed_at":"2026-08-07T14:51:51.501090Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.501090Z"},"links":{"cited_paper":"/paper/2307.02628","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:c52a0dadb1f17e31d315b4b5130e3831ae67c8289a9bbe854c53078c8f5028b7","observation_id":"76a8c7e2-caa3-4204-8c28-77bb60f2d59f","resolution":{"observed_at":"2026-08-07T14:51:51.501090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16710","last_updated":"2024-10-18T04:02:31Z","snapshot_observed_at":"2026-08-08T01:15:44.293937Z","submitted_at":"2024-04-25T16:20:23Z","title":"LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16710","snapshot_observed_at":"2026-08-07T14:51:51.597620Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.597620Z"},"links":{"cited_paper":"/paper/2404.16710","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:a59622e8ed45581acaed9983790ace4b8b39aa6e571bf84fa14702f59262d215","observation_id":"73ed9899-7c75-4117-a10b-8539a60bc3e4","resolution":{"observed_at":"2026-08-07T14:51:51.597620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02181","last_updated":"2024-07-09T11:59:01Z","snapshot_observed_at":"2026-08-09T22:31:51.167662Z","submitted_at":"2024-03-04T16:23:58Z","title":"Not All Layers of LLMs Are Necessary During Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02181","snapshot_observed_at":"2026-08-07T14:51:51.721100Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.721100Z"},"links":{"cited_paper":"/paper/2403.02181","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:7c6b9193cdb4893932a78eab26bf3c679204921ce7a7da8dc29e41a9d77d876e","observation_id":"65a5f1c8-cb1f-41ac-8afe-021d6ac540f2","resolution":{"observed_at":"2026-08-07T14:51:51.721100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-07T14:51:51.855677Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.855677Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:4d691066f05149bfd5216c61c4520cea26df5a155a7d2a665fd7785d488029d4","observation_id":"d0d265e9-9b28-40c8-9e1e-20c73c06c549","resolution":{"observed_at":"2026-08-07T14:51:51.855677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T14:51:51.962063Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:51.962063Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:7191fcfb4620e413dae07a764455a884ec44762f2acc4bc9a801066ed0983b7d","observation_id":"73d78bdd-b6f6-4b71-863d-41687c3c861e","resolution":{"observed_at":"2026-08-07T14:51:51.962063Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:51:53.934264Z","title":null,"venue":null,"work_id":"5ce20e5d-b688-4123-84db-08522decc78f","year":2022},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.086524Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:e8e72b37e41f3ae3378aa34b43bee9cb93aa931e6cad090f96e6387e2df514ca","observation_id":"fd1e5ce3-886b-4861-9a6d-d54f080ccb42","resolution":{"observed_at":"2026-08-07T14:51:54.034180Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.02336","last_updated":"2025-01-04T17:01:30Z","snapshot_observed_at":"2026-07-06T20:16:35.984578Z","submitted_at":"2025-01-04T17:01:30Z","title":"AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.02336","snapshot_observed_at":"2026-08-07T14:51:52.225072Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.225072Z"},"links":{"cited_paper":"/paper/2501.02336","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:d041df49acc076c978d1e267af3f9fd443404c354070fcb89af2b5316bfb5c13","observation_id":"18ed080e-0432-4a98-8c6b-f03e02882084","resolution":{"observed_at":"2026-08-07T14:51:52.225072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-08-10T12:35:09.020030Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-07T14:51:52.345584Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.345584Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:7a77c74daad05896ff4ffba4cb09f3d1a26e2e22fd523bd3e84af0db0c2f1375","observation_id":"5647efe6-cb62-4864-9568-14b5cf30957d","resolution":{"observed_at":"2026-08-07T14:51:52.345584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.03865","last_updated":"2024-04-05T02:35:43Z","snapshot_observed_at":"2026-07-06T17:55:57.562339Z","submitted_at":"2024-04-05T02:35:43Z","title":"FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.03865","snapshot_observed_at":"2026-08-07T14:51:52.466772Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.466772Z"},"links":{"cited_paper":"/paper/2404.03865","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:307270efeab181e50b34c9c448bd390694dd894f729d608b29eb922bcc8f30b1","observation_id":"df9dcbfb-2cff-4c29-b1ba-04daa3e9a950","resolution":{"observed_at":"2026-08-07T14:51:52.466772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06954","last_updated":"2024-04-10T12:12:07Z","snapshot_observed_at":"2026-07-06T17:58:12.766206Z","submitted_at":"2024-04-10T12:12:07Z","title":"Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.06954","snapshot_observed_at":"2026-08-07T14:51:52.490908Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.490908Z"},"links":{"cited_paper":"/paper/2404.06954","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:ba8a8ef3e114af9a98e668406fd6f475e670eb758209eb2a0eec0805e871279a","observation_id":"4c017860-a3d9-485e-91f7-1e1be7523979","resolution":{"observed_at":"2026-08-07T14:51:52.490908Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:51:53.766443Z","title":null,"venue":null,"work_id":"efe9ff79-639a-4d92-8f32-a301aed9c787","year":2023},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.498739Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:5ec1fac1ed5504ce82db9d306df2e703821ef0052552f2b8244c81151fc76d9d","observation_id":"16070eb8-038d-4783-9b7f-478af00109b5","resolution":{"observed_at":"2026-08-07T14:51:53.858909Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03853","last_updated":"2024-10-11T09:43:32Z","snapshot_observed_at":"2026-08-03T01:48:18.654934Z","submitted_at":"2024-03-06T17:04:18Z","title":"ShortGPT: Layers in Large Language Models are More Redundant Than You Expect","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03853","snapshot_observed_at":"2026-08-07T14:51:52.519822Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.519822Z"},"links":{"cited_paper":"/paper/2403.03853","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:24cabef5c017d30bdf1851fb9b46f10118c65e8c2b54f7e306ca372d4a37b235","observation_id":"eb5cbf14-7e40-4a43-b3cd-e3f72118b92b","resolution":{"observed_at":"2026-08-07T14:51:52.519822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.07843","last_updated":"2016-09-26T04:06:13Z","snapshot_observed_at":"2026-07-06T05:12:10.387914Z","submitted_at":"2016-09-26T04:06:13Z","title":"Pointer Sentinel Mixture Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.07843","snapshot_observed_at":"2026-08-07T14:51:52.592199Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.592199Z"},"links":{"cited_paper":"/paper/1609.07843","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:bd5ad4532d7d2aa8ade4797bafd613bca7c49e7c67fa57edf149f59f2e646692","observation_id":"03767f2f-ddb6-4a35-ad71-b4fc76ee627c","resolution":{"observed_at":"2026-08-07T14:51:52.592199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04368","last_updated":"2017-04-25T05:47:50Z","snapshot_observed_at":"2026-07-06T05:37:49.239583Z","submitted_at":"2017-04-14T07:55:19Z","title":"Get To The Point: Summarization with Pointer-Generator Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04368","snapshot_observed_at":"2026-08-07T14:51:52.681122Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.681122Z"},"links":{"cited_paper":"/paper/1704.04368","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:33c4ac0302626b47179d73bb2728d0580a3175f5cce7b3c4e6e494e3aee0d0b7","observation_id":"39859a22-f103-42b3-83f8-b417879e57a2","resolution":{"observed_at":"2026-08-07T14:51:52.681122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-07T14:51:52.760222Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.760222Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:29fd519789c66188e49025b4d58c505b4cb348c3b93cc2bcc17047c77453820f","observation_id":"1334dc08-0bfb-416d-9eae-d010615e7abb","resolution":{"observed_at":"2026-08-07T14:51:52.760222Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:51:53.599647Z","title":null,"venue":null,"work_id":"43284e95-ccd9-4534-8f1b-3a074b90002d","year":2023},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.857897Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:c4e9b852250723aad0db7600b8d35bca6bb1d604cffa5c6cc662fb640c2a99f0","observation_id":"aa018563-c17f-483f-9b5a-025aa500af79","resolution":{"observed_at":"2026-08-07T14:51:53.652559Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:51:53.407105Z","title":null,"venue":null,"work_id":"530a77a3-af21-4a11-9bbd-4aa8e51eb732","year":2022},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.927099Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:f727589167cac56f7360e1f369df4504b443d67664898865623bd09c57ba664d","observation_id":"39088ef9-e16d-4a31-8e9d-4d56750a9c15","resolution":{"observed_at":"2026-08-07T14:51:53.478609Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T14:51:52.969311Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:52.969311Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:70629a89fc6c530200bed84c82d3eef9c2efe3de74e6fe11596e2156cf8df388","observation_id":"4e3742c7-26a1-4820-beaa-8c220cde2144","resolution":{"observed_at":"2026-08-07T14:51:52.969311Z","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":"2026-08-07T14:51:53.071698Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:53.071698Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:6cfbeb7b157a1fff41b73d9e4ffb1272e6355c0932f0d8afe5c24f0f15f963cf","observation_id":"1189219a-5dd6-4dd8-a199-69fe0222df11","resolution":{"observed_at":"2026-08-07T14:51:53.071698Z","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":"2026-08-07T14:51:53.154620Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T14:51:53.154620Z"},"links":{"citing_paper":"/paper/2505.17420"},"observation_digest":"sha256:59463ee4c95ed367c52d0553295cf949ede4e6f7190a2a0a8d17ffa2fe8152d6","observation_id":"7412d5f8-21d9-4ceb-a8d5-560e5a6d7dca","resolution":{"observed_at":"2026-08-07T14:51:53.154620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.17420","last_updated":"2025-05-23T03:10:11Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T16:45:40.294440Z","submitted_at":"2025-05-23T03:10:11Z","title":"DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":23},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.17420."}