{"as_of":"2026-08-07T03:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:62b7b948387091864a434bf38e0d97983b5c9dcc3b54161f03f6a3ebd5f541b9","coverage":[{"denominator":169,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T23:28:12.790404Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2603.16867/citation-record","integrity":"/paper/2603.16867/integrity","json":"/paper/2603.16867/citation-record.json","paper":"/paper/2603.16867"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"The claude 3 model family: Opus, sonnet, haiku","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:fcbf4f391437bc71f47a890cb16bd17748ec85b48333827b87c5386fba13699c","observation_id":"e733315b-49b9-4d8d-ab82-b165f36eacd4","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16720","last_updated":"2026-04-30T02:46:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-21T18:04:31Z","title":"OpenAI o1 System Card","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16720","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Openai o1 system card","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:b9e9ff71b930e9eca816f1325542e4a6dbaa58fa7a52e03b9758a3215da66f1f","observation_id":"5ca74055-f30c-45d4-8d74-027fad47d768","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:edbcfcc7e749e72cc14a128269721a17b026d88431fa0d3d834567879884c2c6","observation_id":"db947c1d-2fe2-4a2b-9c35-351b60fbfd5e","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-07-13T23:28:12.790404Z","title":"First proof.arXiv preprint arXiv:2602.05192, 5 February 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:e6dde3a2a1cc9f85dd5008e80db3a6a5b96a1c85c01c4cba9b30fa4908efbbdd","observation_id":"2fc2f093-d3c2-4311-8256-bfe399a15cea","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Evaluating frontier LLMs on PhD-level mathematical reasoning: A benchmark on a textbook in theoretical computer science about randomized algorithms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:fdd1f5fb0c3b9f53dceaef19f46ecc33d9499bb9fc0ecefb60e1e265c161f85a","observation_id":"0eb6f198-ad5b-41ec-8b7b-2ecbbc93e5bd","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"T owards autonomous mathematics research","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:2de76662e2e1e08a3a7e2976110be98c445f7b0f75506c00f979ba7f54ab5670","observation_id":"287a434b-05de-4602-97b8-43e53cb71613","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:b5d175e4565b448b0409fd4edcc90927d699f3fc2c50a8d85fe7a607c7f62f22","observation_id":"8e8c3d63-f63c-4a62-b4d5-307d82fa78a1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"ReAct: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a4ed9bad16dc0758569c51ac9328e87bb71f627e59b453ca7524e01620067b92","observation_id":"56395c5d-8a41-44d9-9602-4b5d65420f76","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"MAI-UI technical report: Real-world centric foundation GUI agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:52c9d5dbb0aceb31d8c72e3695c95a7aa5ece7046bc84e8afcfd8c06b0d8b662","observation_id":"e3bd099f-b0a2-4e4f-a38f-95b279343b48","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"26 Chaolin Jin, Chen Li, Hao Chen, Haoli Chen, Jian Chen, Qinghao Zhao, and Guang Shi","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:96bdb006c7873f7d6e65d03484d36b73e63ff17d672966fdbffd41ee9bc933c4","observation_id":"603594aa-1ba9-40eb-98e7-a4ee0e5cf224","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"UI-venus-1.5 technical report","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:41d72ca3c01df1e9bafb2e36f76e99e3a864fe39ada2bc2bc3b1709f46ce90f3","observation_id":"147e2bdc-78b5-4d96-aad7-dc3e9b64364b","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"https://nousresearch","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:ed0387954a30b1e0da2d5118e82f7c0a74b8175d36220f8f28734ea65e70992a","observation_id":"ab4a6150-1794-4317-878d-f71f4f8c33e0","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"LLM in a flash: Efficient large language model inference with limited memory","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:04f6af70949f3c8a02ccfa998d908e38299ea7794a35ce1f0e3e4793e41d6f0d","observation_id":"669b24ec-b00b-464b-8951-d2a6e3b25ad9","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Understanding large language models in your pockets: Performance study on COTS mobile devices","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:b7f9ff8f4bccf53bcdebd61e1aca92859a7baca861e3fd3250c1adf72d3fd767","observation_id":"48a6e8cd-0e6f-4933-8ce3-4a61f0400909","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:68b925a5275d657cbd20924c24705e3d4a9f0d6a8f333980d64c72d0915849dc","observation_id":"03189118-44c6-4d81-9f5f-1ab1367da937","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:9dec06e854bed0b8c0cec7b3c65ff99563004549eee75ea900577b5de777d039","observation_id":"3082d675-eecc-4b5d-b8db-52476bc36cfb","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19393","last_updated":"2025-03-01T06:07:39Z","snapshot_observed_at":"2026-07-06T20:29:11.710285Z","submitted_at":"2025-01-31T18:48:08Z","title":"s1: Simple test-time scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19393","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"s1: Simple test-time scaling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2501.19393","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:d7a3981a28f67d90fffd98546de6e22d068eb22f8b6c2c13275cdcfcf823c55f","observation_id":"7a31780e-6c1b-4a3b-9f3a-7fa8cbb5f597","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.13752","last_updated":"2025-06-16T17:57:05Z","snapshot_observed_at":"2026-08-07T00:24:26.783413Z","submitted_at":"2025-06-16T17:57:05Z","title":"Steering LLM Thinking with Budget Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.13752","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Steering llm thinking with budget guidance","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2506.13752","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:0adfde69488e113e161ae7b706ff98591270b51f78b2a004b7e2a0c6ec253daf","observation_id":"67313d49-0389-4646-a396-9575b30f074c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"https://github.com/ Qualcomm-AI-research/fastforward","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:c1510ec62745898ece01a646659fd031e674844e1b288076e1473877c27410e7","observation_id":"aa71bfda-5cf2-4155-90e9-508a16a17ab6","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"https://www.qualcomm.com/developer/ software/gen-ai-inference-extensions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:70921646712894b7e81f2d02939b48b4aad32be6b2408940f9f1b58ed09b1a8d","observation_id":"8a3c10b9-3586-4dcb-b7b9-33a1ebf60822","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Large language models are zero-shot reasoners","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:6cf0db701f3c683b70dfbce5660499a46383d1d7e698c00dab443d993c1cbc8e","observation_id":"94318df7-127d-47d0-8e0d-fb8313eeac0f","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Show your work: Scratchpads for intermediate computation with language models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:d5486c575ef76f9669b905d78875507a05ef73da5ab41ec39b73b9732c38e8ef","observation_id":"4ae2751e-12ec-4a3d-be42-c3521ff448c3","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Chain-of-thought prompting elicits reasoning in large language mod- els","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:9b12646c0af35b89882e75bf9609c3dd5ba438a110c52d91f0ed3f7b8ec71864","observation_id":"52961f33-b697-4f8a-a5fb-fe03b6caef02","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15777","last_updated":"2025-04-22T10:38:00Z","snapshot_observed_at":"2026-07-06T21:13:00.237924Z","submitted_at":"2025-04-22T10:38:00Z","title":"Tina: Tiny Reasoning Models via LoRA","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15777","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Tina: Tiny reasoning models via lora","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2504.15777","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:26233edcfd2abce068be1fbceb9ef562577a68d204b36c41738be757b9ee5e8a","observation_id":"f803da0e-4eef-40ce-bd83-f7d3c4e7a549","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21233","last_updated":"2025-04-30T00:04:35Z","snapshot_observed_at":"2026-07-06T21:16:46.592336Z","submitted_at":"2025-04-30T00:04:35Z","title":"Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21233","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Phi-4-mini-reasoning: Exploring the limits of small reasoning lan- guage models in math","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2504.21233","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:f0ad16be347a1e9cfe6e6dd1f5b28970d745d1ed41c6e34c4a4eaa7865660da9","observation_id":"63751b55-7c61-468e-9330-42c61068092e","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-07-06T21:27:13.399006Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Think only when you need with large hybrid-reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4fdb3c08124ac809fefa711d8fbff8c5a885b9bfd6d2753f1f981cacaad1a64b","observation_id":"9f7b5512-257c-4ef5-bd9d-7048e409a4fc","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Lora without regret, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:2dbdaa2bad5a8601bdcc3d2ec0795c51ddc6953307916cdb60476cee730fe111","observation_id":"decf824f-a0a1-413c-9d2a-aeb9bb6d5564","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.16400","last_updated":"2025-06-05T17:59:12Z","snapshot_observed_at":"2026-07-06T21:28:24.644692Z","submitted_at":"2025-05-22T08:50:47Z","title":"AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.16400","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Acereason-nemotron: Advancing math and code reasoning through reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2505.16400","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:74a748e680f6ac551965addda10a9dc5d8f788c6d6c082c4a06446860acfbd7a","observation_id":"604cc654-1611-494a-a9f5-b122ba7c947b","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Reinforcement learning for reasoning in small llms: What works and what doesn’t","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:12906a8def57055b7e46c9c662338f8d501f1e0763fb6e2523942db26f154055","observation_id":"c40d8ab2-0327-4ace-891f-93d85c41112e","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.02076","last_updated":"2025-07-02T18:27:42Z","snapshot_observed_at":"2026-08-06T20:36:24.314132Z","submitted_at":"2025-07-02T18:27:42Z","title":"Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.02076","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Reasoning on a budget: A survey of adaptive and controllable test-time compute in llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2507.02076","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:86aa38a61bf491883a39a5ee23881b54b385c5c114414ecf040afa3093e9a88f","observation_id":"a621ebbc-27c2-40d2-a369-2f58274ed11d","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language mod- els","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:40bd3817091f6d9bec4a522c2e1a80cb79bec7cd629ccb3b2db14cf2265fd9e3","observation_id":"e64fd363-a7f0-4157-a401-aeae21a5137a","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Qwq-32b: Embracing the power of reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:fa1f2361066a6edd5ffd01e2426cb81ab33e6a4b63e438d6f0d9159f0a484335","observation_id":"53443657-dd35-43c9-896d-137d31f1a7f1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04178","last_updated":"2025-06-05T02:21:52Z","snapshot_observed_at":"2026-08-03T02:41:13.331563Z","submitted_at":"2025-06-04T17:25:39Z","title":"OpenThoughts: Data Recipes for Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.04178","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Openthoughts: Data recipes for reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2506.04178","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:e2e4880825cfd6646a788307c6c721ae5d6bbd5cb00e88e455e0fb7b7dc743b8","observation_id":"0e5c09ac-b62f-4466-a2a8-806c3efc2e86","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Qwen2.5 technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:c9776d018f58b10bce9b71948c679e14691c6c3dd31cc1b84bdf7557f38ead0e","observation_id":"9a8b0d4b-b8a6-466e-99e0-7ef62afdfa49","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Open r1: A fully open reproduction of deepseek-r1, January 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:103a59b5faf22215a8e48918c234440b50eee734e43adbb60052391ee95bc45b","observation_id":"40e1dc2f-d761-4f06-87e2-c0e28f5d45df","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"aime problems and solutions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4f3227386aa0615e68d52b44d43cf5b0e84dbf4b00e2ea9924e1386f3bfbfa7f","observation_id":"cc38a614-7dde-4f02-9c41-2c37a21bb2b6","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"amc problems and solutions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:c502b384964b5951d27753b57cd9bfff7fe2a8a7c16b060ae91f3ce021a41217","observation_id":"b4fa6a9a-91d2-4349-8153-60494a74c1d4","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a7bea4e543d05198171129b8167fbd818070fd5f340339f9cd93199361487068","observation_id":"4d94c9fd-8080-4bb1-802d-ab0feded36e4","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Gpqa: A graduate-level google-proof q&a benchmark","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4ee91f2842c4ea08031e4d21e6002f7834f03835983e998506ea57cd7a31225a","observation_id":"ac32b727-7e3a-4795-95f3-ed083128158b","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"LiveCodeBench: Holistic and contamination free evaluation of large language models for code","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:99617bb6698b9b1e938b38f7c49084f05564abf696bfb68da8e5ab17ce735d48","observation_id":"68b27006-879e-4e39-94ac-ef9530d97847","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Evaluating large language models trained on code","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:e40fcf1d06ad058993963ecf43ab6cbbf18e821e434df60dc57e1baf49ff71df","observation_id":"43d5eec6-e30e-4942-adb5-8cf740270f71","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Program synthesis with large language mod- els","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:57f60ecd65be84ff5bc0ab099b32f7f2f484802d676641dbf0335ba07f328f46","observation_id":"1b82bae7-2b84-46ec-be29-5b22b168a3a6","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Is your code generated by ChatGPT really correct? rigorous evaluation of large language models for code generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a6cd0d9bced0cf16905374028b3fc7c1329463073369b12fec9a294d9f872c4c","observation_id":"eca9aa0e-066b-45ff-8d02-2e31d4433932","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Lighteval: A lightweight framework for llm evaluation, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:67f0061b1e813c91c60754360b240e601e0075558f5ab87012daec4994288c52","observation_id":"21960035-40b2-48c0-9c25-6e78852cb395","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Gonzalez, Hao Zhang, and Ion Stoica","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:944e8a9f178f5cfd7d1f6afd1da567a1a19eca8e806eed4df847ebd55bdbe98e","observation_id":"20c83f12-dc98-45ff-8891-159e1401235c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.00432","last_updated":"2025-10-20T14:27:09Z","snapshot_observed_at":"2026-07-06T21:50:16.465983Z","submitted_at":"2025-07-01T05:23:05Z","title":"Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.00432","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Does math reasoning improve general llm capabilities? understanding transferability of llm reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2507.00432","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:371a95537fac68e3591ef7114e4f822dd89f6244557cb7868dd33ad2f71759c9","observation_id":"620fd849-106b-4381-b137-7bbea98b681c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.05386","last_updated":"2026-06-29T17:47:49Z","snapshot_observed_at":"2026-08-06T19:25:14.929252Z","submitted_at":"2025-07-07T18:17:06Z","title":"Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.05386","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Reinforcement fine-tuning naturally mitigates forgetting in continual post-training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2507.05386","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:ea9a127c363442a09cf33869bbdfe2e459a1f6361bf480ae884668ac190ef5f7","observation_id":"0fd74f86-2dce-4167-9895-984cf0bb598c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Know what you don’t know: Unanswerable questions for squad,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:06f8dd17b2d6a7aae9433a63984693ea8e3edf3880ea2e1c3871a275ec00f9a9","observation_id":"7c6fe474-2374-40ac-8641-6f246140a297","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.03822","last_updated":"2018-06-11T06:10:11Z","snapshot_observed_at":"2026-08-02T00:06:16.208560Z","submitted_at":"2018-06-11T06:10:11Z","title":"Know What You Don't Know: Unanswerable Questions for SQuAD","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.03822","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"(Cited on page 10)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/1806.03822","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:b297d60b14d9f365c09643fc06fb58dd8c1791abc7e62cacd6a6e586c0e70c9f","observation_id":"0f556a92-6e30-47f0-b4bb-f56b5baf61de","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.02235","last_updated":"2021-01-06T19:14:23Z","snapshot_observed_at":"2026-08-06T08:22:03.965877Z","submitted_at":"2021-01-06T19:14:23Z","title":"Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.02235","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2101.02235","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a58259d66ad4dbaac2f926bfc04749d637115b727e45f8d2919b56ac1fad01d9","observation_id":"e63a581c-c5a3-4bb0-8532-cd832533d1ec","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"When reasoning meets its laws","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:65fb3e13227adb847ddb7dbc9a4f873e9c596c8de987f5619d9cd908b09a89f1","observation_id":"91906a3d-5b09-4321-affd-48cc2bb02609","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18600","last_updated":"2025-03-03T17:08:21Z","snapshot_observed_at":"2026-08-06T08:00:07.411397Z","submitted_at":"2025-02-25T19:36:06Z","title":"Chain of Draft: Thinking Faster by Writing Less","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18600","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Chain of draft: Thinking faster by writing less","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2502.18600","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:26e12e6eabb5461041de754b513c06f7828e5c13ba1a89d18e5dec5fce3c9226","observation_id":"d8c8e6f9-4d5d-497e-9c1a-6247b8595bf0","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05618","last_updated":"2024-10-19T19:37:40Z","snapshot_observed_at":"2026-08-06T16:36:39.506339Z","submitted_at":"2024-01-11T01:52:25Z","title":"The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05618","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"The benefits of a concise chain of thought on problem-solving in large lan- guage models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2401.05618","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:7f70b72019995535846a8fc06ddbb9908150a2e96e88cf0fd04d9c690fb9160d","observation_id":"f4c2649e-1462-4acb-a940-a5eba97eabb4","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Think or not? selective reasoning via reinforcement learning for vision-language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:799f1557ed628a7ecfb59f9041a171d377e0db8e219faf81ea0fcdb4d4dcecee","observation_id":"8eb4aff5-af2e-4da2-9bb5-3ccdc82e88e7","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Hapo: Training language models to reason concisely via history-aware policy optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:8bdd71541ef2e0ae61c95848e9839fa80a483b65b0145f1f623b5a26970e7921","observation_id":"5df31b0a-db6a-4e66-bcf7-844a07bb6c8c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04697","last_updated":"2025-10-03T01:55:58Z","snapshot_observed_at":"2026-08-06T08:53:09.095000Z","submitted_at":"2025-03-06T18:43:29Z","title":"L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04697","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"L1: Controlling how long a reasoning model thinks with reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2503.04697","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:0c50fb7b6b29cbf0acbaa084950e1c93711694a0f700caf00781b13104b7151f","observation_id":"0c860a27-a842-400f-b339-ab2fce5648d3","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Dler: Doing length penalty right-incentivizing more intelligence per token via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:0924ad5c0f87ba7a95631536493179a38b1e5f2b300e272c0ea6f9919520d585","observation_id":"5d028423-1037-4878-940d-f3655809889b","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Deepscaler: Surpassing o1-preview with a 1.5 b model by scaling rl","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:72d5246dca67e37c44b89ba7a2d8f369872e69bba3f4bd431ad0871b218cb08c","observation_id":"3f3970d3-a399-407e-a1ab-abdda6a3d876","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"TRL: Transformers Reinforcement Learning, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:070eed552f1fb0acfdacb26ea08d06edb9b84bd44c934d30961bda952e40f22a","observation_id":"2a4e8277-cb1e-43cf-b151-8c494d4815f2","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Let’s verify step by step","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:31affe6bc54109bd90e5835d8dacdc184d9b078bf08c2f9bbe0647c4ee123269","observation_id":"0f1734c6-e0b5-4ad0-9728-b31d55a9274e","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:8f7c63e56eaec1d5b4fd2b22083acc52ee4f12e9a4b9bb7adc7d2729e9e74325","observation_id":"2cc111fa-a85f-4eab-8929-d1b76fb8db91","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-07-06T12:50:22.773056Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Self-consistency improves chain of thought reasoning in language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:14dc6014720ff7af43703a7239aa79a204397122e3cb773f413167fef45c57d7","observation_id":"890d3489-79a1-448c-b367-45138775f34e","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21787","last_updated":"2024-12-30T19:03:24Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:57:25Z","title":"Large Language Monkeys: Scaling Inference Compute with Repeated Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21787","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Large language monkeys: Scaling inference compute with repeated sampling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2407.21787","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:3bab38c8f014359630af7cd2c8fc085b29372c642b374e39a8ac6a2f2f3147db","observation_id":"28f36b6c-47a4-484a-8758-80322b7c40bb","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Inference scaling laws: An empirical analysis of compute-optimal inference for llm problem-solving","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:03f973181d709279d147a08ae49651fc92bae2df608277d412beb85b4aeccd50","observation_id":"78bdc90a-6a8e-4c01-b608-e6dfdff2c3e7","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:e8b9cb8c1e24a828481a5ec297e5ce54b1cd1cc3d20e20d0794b2b561add703e","observation_id":"a21caa80-883c-4f89-88b1-af74688f7381","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"The effect of sampling temperature on problem solving in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a9243ca722063bd22ae7c46c76cf92f152193581022315b8fc9b2a65c1317e45","observation_id":"653bcf11-d0c6-439f-aeec-05dd36f3fc49","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11107","last_updated":"2025-05-16T10:40:35Z","snapshot_observed_at":"2026-08-05T04:01:13.119857Z","submitted_at":"2025-05-16T10:40:35Z","title":"Group Think: Multiple Concurrent Reasoning Agents Collaborating at Token Level Granularity","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11107","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Group think: Multiple concurrent reasoning agents collaborating at token level granularity","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2505.11107","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:1d75e1ca67ff01e993e89ca561f97175dc4fd77bdeac914d6440966b9f566c61","observation_id":"3a019654-f2fe-416c-b407-61425f831e0a","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15466","last_updated":"2025-08-17T18:23:42Z","snapshot_observed_at":"2026-08-01T19:32:09.217382Z","submitted_at":"2025-04-21T22:29:02Z","title":"Learning Adaptive Parallel Reasoning with Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15466","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Learning adaptive parallel reasoning with language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2504.15466","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:3c91abb35c5361b8be3b1db3bbc886682e06ec06647e75c443a7202888ec10d8","observation_id":"2bb5e1b6-5395-473f-8513-d23079c4eccd","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Hogwild! inference: Parallel llm generation via concurrent attention","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:0e70638d94421d43426c4895ca49bcfb76b22061cf9b98edd8d059e039c28ea9","observation_id":"43dfcd84-07b1-4546-bcad-f90e32d8abf9","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.07980","last_updated":"2025-09-12T17:15:56Z","snapshot_observed_at":"2026-08-04T21:28:47.405179Z","submitted_at":"2025-09-09T17:59:35Z","title":"Parallel-R1: Towards Parallel Thinking via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.07980","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Parallel-r1: T owards parallel thinking via reinforcement learning.arXiv preprint arXiv:2509.07980, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2509.07980","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:079e9f5f22a95f8c50da562df2349b2c62242454a039027c7800804da40e6e88","observation_id":"0050ce8a-a01e-4356-bc8e-2e84d34e485d","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Tree of thoughts: Deliberate problem solving with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:6d52cbfcd4699f76602b37ad99005f962c05b5ec1d7b1cea87fc377e256eb9e5","observation_id":"af23d762-111f-4c8b-88cd-38e87e82533b","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15337","last_updated":"2024-03-02T02:45:33Z","snapshot_observed_at":"2026-07-06T15:59:34.968253Z","submitted_at":"2023-07-28T06:31:34Z","title":"Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15337","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Skeleton-of-thought: Prompt- ing llms for efficient parallel generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2307.15337","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:c2ffddac44178901a1a3852402e347d9d3281dba9e06a334c82dc7e3c3442c26","observation_id":"a0b158dd-92e1-40bf-b462-cac9a5412099","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01478","last_updated":"2025-01-02T12:09:17Z","snapshot_observed_at":"2026-07-06T20:15:54.645357Z","submitted_at":"2025-01-02T12:09:17Z","title":"Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01478","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Enhancing reasoning through process supervision with monte carlo tree search","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2501.01478","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:f2b05abba5bce88c4c6d54b1ab376194a43d48433b682023af92a165ae21839e","observation_id":"4d112cfe-7240-4bba-8276-a021b17a72c6","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18451","last_updated":"2024-10-24T06:06:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-24T06:06:26Z","title":"Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18451","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Skywork-reward: Bag of tricks for reward modeling in llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2410.18451","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:81197f7e2429fe981c85c50080afdf877a255cf8985383633914c6d26494e247","observation_id":"6c4f3d44-97bf-4f48-a071-66c4657fdfe6","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Helpsteer 2: Open-source dataset for training top-performing reward mod- els, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:2e2110ccbaef96cd6866e091958a12880c7dcbffbed333cee9d49e3ee3ee5e03","observation_id":"cffc8c92-0fa5-4bd2-b4a0-b69de0e0c4e8","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08935","last_updated":"2024-02-19T14:07:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-14T13:41:54Z","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08935","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Math-shepherd: Verify and reinforce llms step-by-step without human annotations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2312.08935","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:442813b71f79eff4fd50c684f5acd734965a2617f8bbeac32cd31c0792919bb2","observation_id":"d26e2556-e31d-4142-be99-e1970e870d10","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07301","last_updated":"2025-06-05T16:34:24Z","snapshot_observed_at":"2026-08-03T11:11:25.359494Z","submitted_at":"2025-01-13T13:10:16Z","title":"The Lessons of Developing Process Reward Models in Mathematical Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.07301","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"The lessons of developing process reward models in mathematical reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2501.07301","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:bb01f43fdf33ccf3ffd22c633820a5b1a16e3fa2cf546237de5df5d15cf835c4","observation_id":"6dbbc7d3-1efe-4d43-8cfc-7879358682da","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Inference-time scaling for generalist reward modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4c6f0e141ee670443af00747dbd6d83dd013f2b838a41ed59bd034b03af19a52","observation_id":"5c3e535a-9a59-44c7-bd86-6e64cb14c995","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Web-shepherd: Advancing prms for reinforcing web agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:9f26bb58eae942ed41a72c26a178b69015ff4306f292d65f453743043d39a381","observation_id":"fb3854e8-5c6d-46b5-89ba-91beba9debe1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12130","last_updated":"2025-02-17T18:49:25Z","snapshot_observed_at":"2026-07-06T20:38:00.177933Z","submitted_at":"2025-02-17T18:49:25Z","title":"Scaling Autonomous Agents via Automatic Reward Modeling And Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12130","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Scaling autonomous agents via auto- matic reward modeling and planning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2502.12130","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:08663d1ca5289eded32124e1836295837683fa7fabd79c651c5e41f08dbe6c0e","observation_id":"07822275-97af-4142-bdd4-eb905c7359f1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Making, not taking, the best of n","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:aed08a5d7c00770ff67dfc81a622c43c54b9e3a89227b81f986ad92dfd9cb3a0","observation_id":"491ccb90-2d2a-4ef4-b76a-80cba5656485","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11877","last_updated":"2025-01-21T04:11:59Z","snapshot_observed_at":"2026-07-06T20:23:40.374376Z","submitted_at":"2025-01-21T04:11:59Z","title":"From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11877","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"From drafts to answers: Unlocking llm potential via aggregation fine-tuning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2501.11877","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4135c7c770dedd0b4c9558a7cb8cda60f0329f53ccbcbd0f545d3b71c3b99b48","observation_id":"b5042fc9-5c2b-4edf-9eef-47b7a1dc899c","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Learning to reason across parallel samples for llm reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:0fe511cf7571207eb627855d4a6a34a94a5b2454ebb69edf55b0ade9430bada6","observation_id":"0181e7f1-cfbc-4fac-88ba-d29e1fa36b47","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.06870","last_updated":"2025-09-08T16:39:38Z","snapshot_observed_at":"2026-08-04T23:02:26.154530Z","submitted_at":"2025-09-08T16:39:38Z","title":"The Majority is not always right: RL training for solution aggregation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.06870","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"The ma- jority is not always right: Rl training for solution aggregation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2509.06870","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:de7693e6b7c0a041dee70c3e9478715db2ed575a045bfa076a857380c69a33d6","observation_id":"ddb9fb00-fec4-4199-aca8-14bcbad2a379","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Scaling llm test-time compute with mobile npu on smartphones","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:4a4f5bb0d10b721cfe211584f37f7612fb5d12b3f00b855b1b5c558bc8113206","observation_id":"c9c4c203-9902-46cf-8560-c90541c73bae","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Fast best-of-n decoding via speculative rejection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:e31a3f0dbdc8fee1bf769bd0d4777902b6803f26d9eb498884c283128ad0c5a6","observation_id":"7351712a-8cd3-48e9-bbba-2dd588160040","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13575","last_updated":"2025-06-11T21:59:20Z","snapshot_observed_at":"2026-07-06T20:39:06.470648Z","submitted_at":"2025-02-19T09:30:38Z","title":"ETS: Efficient Tree Search for Inference-Time Scaling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13575","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Ets: Efficient tree search for inference- time scaling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2502.13575","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:27925f7401b3a8ef4b40daa3e19fbfbfd63ac012e9c4de0ca8088a6b9f08343f","observation_id":"fd1d5b12-f903-4037-9b86-3ab0bb2e3fe5","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Generative verifiers: Reward modeling as next-token prediction","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:dde2c344d7a80311ce8df84d07a86451e16297394d6690d91abfccb1ea595151","observation_id":"b73203f1-7699-4386-a6be-f1adeebd2e62","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.08342","last_updated":"2018-06-21T17:32:46Z","snapshot_observed_at":"2026-07-06T06:46:08.396066Z","submitted_at":"2018-06-21T17:32:46Z","title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.08342","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/1806.08342","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:718b60fd8b58b4dcd977d79074df806f66341eb56e43130e95bc58ac1ea0a3f4","observation_id":"ad940002-949c-4e9c-a54a-955c988afce0","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08295","last_updated":"2021-06-15T17:12:42Z","snapshot_observed_at":"2026-08-02T11:19:40.664702Z","submitted_at":"2021-06-15T17:12:42Z","title":"A White Paper on Neural Network Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08295","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"A white paper on neural network quantization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2106.08295","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:43f6ff7a45494d80a29e35cda88a9f389bdd4ccb907e4d8cc4b5825518cac32e","observation_id":"ec2e3b21-9140-4034-8804-1397f2bb97be","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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/isscc","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T23:30:04.172648Z","title":"Horowitz","venue":null,"work_id":"78939fc1-d498-4bbc-b30c-ecd0be259434","year":2014},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:c480b9ab2c1c1549d2f8cdd6f8e1c9f79993bdbdd316b1a52152ec9f27335607","observation_id":"5dd8ba84-4151-4aff-9e06-134981008b83","resolution":{"observed_at":"2026-07-13T23:30:04.176644Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-07-13T23:28:12.790404Z","title":"Quantized neural net- works: Training neural networks with low precision weights and activations.The Journal of Machine Learning Research, 18(1):6869–6898, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:b9b92f81db0f92b9c4e414d16cfdd914e89e90665c2fc2c1bb5af181266399e2","observation_id":"9a97f3a7-1a19-4d57-8491-c933e366f7f1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.06160","last_updated":"2018-02-02T01:43:54Z","snapshot_observed_at":"2026-07-06T05:00:35.763958Z","submitted_at":"2016-06-20T15:02:31Z","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.06160","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/1606.06160","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:838342d50eceda5f555b49853c8f3408526ea7940e1afe400dc6ec3f6f44a48b","observation_id":"66a2169b-c494-4265-bd04-813697149cc1","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05723","last_updated":"2019-05-29T08:45:02Z","snapshot_observed_at":"2026-08-06T12:27:39.329564Z","submitted_at":"2018-10-02T15:10:44Z","title":"Post-training 4-bit quantization of convolution networks for rapid-deployment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.05723","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Post-training 4-bit quantization of convolution networks for rapid-deployment","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/1810.05723","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:08683b9ebdf82003519435b29b01750422543e4e87c2af8d01d37adaa4c31e73","observation_id":"0aaf7fb7-37b4-4d9b-ae67-2e7c939c4e53","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Zeroq: A novel zero shot quantization framework","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:74f6d240a91a581c7eafc55c3fb4a92c8c92dd8eb1ad3a15da8c98537966fc56","observation_id":"c5e25081-515a-4547-bee6-cac4b2625573","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Low-bit quantization of neural networks for efficient inference","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:1d7f667b9d58d5b66b5fc76cc7293bdb5e36327cf861994467385087a578b559","observation_id":"fce9ce7a-3e1c-4831-ab00-1a61918b6700","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10518","last_updated":"2020-12-14T15:55:05Z","snapshot_observed_at":"2026-07-06T09:30:23.305109Z","submitted_at":"2020-06-14T16:07:55Z","title":"Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10518","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Improving post training neural quan- tization: Layer-wise calibration and integer programming","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2006.10518","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:3e395852a28c9c746e1772643a7ae9d6e0cb024e812e0710648e26600ba526d1","observation_id":"cddf0d51-aea2-4801-a1d0-e2d0603ba883","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Same, same but different: Recover- ing neural network quantization error through weight factorization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:504fefe93de841a24fb552ce940e0ca3e87e4e063be5b8f8201bde8a3bf70b81","observation_id":"2d8a4a23-0a8b-4e2d-8df1-ef0a0c3f8093","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Improving neural network quantization without retraining using outlier channel splitting","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:a6f209072a577dcd7b605a9394cdc3f3ad728256f74d1462049909f532d2ed99","observation_id":"19a68a84-dbcc-4e0b-be3f-2e84106d09b4","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","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":"2026-07-13T23:28:12.790404Z","title":"Data-free quantization through weight equalization and bias correction","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:394cf7f61e2fbb6273447ade3470977755b13ff7526005f11d6d67ed30a7c427","observation_id":"44e6df82-5957-42e6-aa8a-76089d9a8021","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":98,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":169},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 169 outbound references and 0 inbound Pith citation observations for arXiv:2603.16867."}