{"as_of":"2026-08-09T06:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7efc326ad12d27334aa96f9a83815b7a518d15941fe5f98ab607149fce15317e","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:38:37.134674Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T00:52:51.509014Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T08:36:26.806213Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"cited_work":{"arxiv_id":"2506.08060","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08060","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv:2506.08060 [cs.LG]","venue":null,"work_id":"3a103a62-1283-43b8-aba9-f680850f7b0b","year":null},"citing_paper":{"arxiv_id":"2605.08368","last_updated":"2026-05-08T18:23:25Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T18:23:25Z","title":"On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-12T00:52:51.509014Z"},"links":{"cited_paper":"/paper/2506.08060","citing_paper":"/paper/2605.08368"},"observation_digest":"sha256:a7d95a4c1a741deb87d458a5109c160c7c2b2c8216c006fe6811a2e5ea84d199","observation_id":"551ac4eb-0ca3-4a46-b459-109590c23739","resolution":{"observed_at":"2026-05-12T08:36:26.808917Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.08060/citation-record","integrity":"/paper/2506.08060/integrity","json":"/paper/2506.08060/citation-record.json","paper":"/paper/2506.08060"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-07T05:38:34.137344Z","title":"arXiv:1706.03762","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.137344Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:ed28e474e0ad59e371498570ee182d55cea2e02784396f7aebdac93e31d4286b","observation_id":"462ba245-4802-4f0f-af19-bca856a0f78c","resolution":{"observed_at":"2026-08-07T05:38:34.137344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09286","last_updated":"2020-10-10T13:34:20Z","snapshot_observed_at":"2026-08-07T08:43:04.975683Z","submitted_at":"2020-06-16T16:27:56Z","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09286","snapshot_observed_at":"2026-08-07T05:38:34.215539Z","title":"arXiv:2006.09286.https://arxiv.org/abs/2006.09286","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.215539Z"},"links":{"cited_paper":"/paper/2006.09286","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:c6d472e4a7c84ea8f7c026ee3f0ea5773832535b84733cfad808e0f1bc13f6a4","observation_id":"f7b1667b-9d5d-4fe2-941b-6cd2ac4243da","resolution":{"observed_at":"2026-08-07T05:38:34.215539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T05:38:34.305444Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.305444Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:50a48b81f6c12d09abb70a9f2f7cf27a47e065097545c68ca1f0722f14ae63c6","observation_id":"fde1f927-c147-4a02-adf9-ae1b8164b69a","resolution":{"observed_at":"2026-08-07T05:38:34.305444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:41.279014Z","title":null,"venue":null,"work_id":"dba1aa42-15ad-4dbf-a676-a248617b97d5","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.367214Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:469b5ca597bc0963a5de691d6f58b1b2099fceb2ef9ba2cc3cd8a365dbdeda56","observation_id":"79ae7b2b-497f-4202-9d9e-ed7d9a05f340","resolution":{"observed_at":"2026-08-07T05:38:41.383955Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11916","last_updated":"2023-01-29T05:14:17Z","snapshot_observed_at":"2026-08-07T04:02:08.445660Z","submitted_at":"2022-05-24T09:22:26Z","title":"Large Language Models are Zero-Shot Reasoners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11916","snapshot_observed_at":"2026-08-07T05:38:34.489476Z","title":"S., Reid, M., Matsuo, Y ., Iwasawa, Y ., 2022.Large Language Models are Zero-Shot Reasoners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.489476Z"},"links":{"cited_paper":"/paper/2205.11916","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:a2c27610d95bb6a49dd4578577ea835cf29ae4f6d425c3f2b936ef6976dcaaaa","observation_id":"bfd91ce2-f9ac-4b4a-bbf2-89c908dace72","resolution":{"observed_at":"2026-08-07T05:38:34.489476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.01652","last_updated":"2022-02-08T20:26:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-09-03T17:55:52Z","title":"Finetuned Language Models Are Zero-Shot Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.01652","snapshot_observed_at":"2026-08-07T05:38:34.546048Z","title":"Y ., et al., 2021.Finetuned Language Models are Zero-Shot Learners","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.546048Z"},"links":{"cited_paper":"/paper/2109.01652","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:abd30fadb159d6cc71de610610d5d19cf28458cd050c70c551d06056383513d9","observation_id":"a23ce5df-3401-4fef-86c9-900490a8d71e","resolution":{"observed_at":"2026-08-07T05:38:34.546048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:41.095422Z","title":null,"venue":null,"work_id":"b0620904-799d-407d-9d36-b9cd74983413","year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.595507Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:3a04b22c25248cdb7c4fb1a3398e1f079026dd0e96660c4528426b6360a85f29","observation_id":"bf28f25e-3998-4813-90ba-e62b3fc2e942","resolution":{"observed_at":"2026-08-07T05:38:41.160354Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.947085Z","title":"K., Ginsberg, E","venue":null,"work_id":"a5cae3ba-15b9-4c90-a87b-99b670363426","year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.687576Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:4012b0577d6e971e3525bf4701b6ad2a35e7fc2771dc495617c44def10a216d4","observation_id":"63ea0ed6-9565-4e66-9323-8146c41347a1","resolution":{"observed_at":"2026-08-07T05:38:41.015404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.02080","last_updated":"2022-07-21T07:44:13Z","snapshot_observed_at":"2026-07-30T03:45:35.558793Z","submitted_at":"2021-11-03T09:12:33Z","title":"An Explanation of In-context Learning as Implicit Bayesian Inference","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.02080","snapshot_observed_at":"2026-08-07T05:38:34.748484Z","title":"M., Raghunathan, A., Liang, P., Ma, T., 2021.An Explanation of In-context Learning as Implicit Bayesian Inference","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.748484Z"},"links":{"cited_paper":"/paper/2111.02080","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:aefb1d0a57b8db792c7fdb9e0171619ddfea5b3c9852d4bedfc2c7b8e0138ee6","observation_id":"2e46f2de-d363-4175-9b36-a05d56940c1f","resolution":{"observed_at":"2026-08-07T05:38:34.748484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.08406","last_updated":"2021-09-20T17:21:18Z","snapshot_observed_at":"2026-07-06T11:48:38.440102Z","submitted_at":"2021-09-17T08:32:41Z","title":"Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers","version":2},"cited_work":{"arxiv_id":"2109.08406","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.08406","snapshot_observed_at":"2026-08-07T05:38:38.344566Z","title":"Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers","venue":"cs.CL","work_id":"674f4cdf-8b38-49ab-8e43-b1baf0233952","year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.820544Z"},"links":{"cited_paper":"/paper/2109.08406","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:a106c28be8408554702dba2b923e2d20f1131366c638d72a22dcab88e49b121e","observation_id":"7ce40b1d-4168-4b2b-a4f1-c7cb8c548470","resolution":{"observed_at":"2026-08-07T05:38:38.534612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08112","last_updated":"2025-11-11T01:13:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-14T17:47:09Z","title":"Eliciting Latent Predictions from Transformers with the Tuned Lens","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08112","snapshot_observed_at":"2026-08-07T05:38:34.910116Z","title":"arXiv:2303.08112.https://arxiv.org/abs/2303.08112","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:34.910116Z"},"links":{"cited_paper":"/paper/2303.08112","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:7c1adfdbc555d861a0c7af0aaae19299dc1cc08cd5ebdd799f276fd1a32875df","observation_id":"12891052-5e46-4f88-8ef0-1e497c35a52f","resolution":{"observed_at":"2026-08-07T05:38:34.910116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-07T05:38:35.025542Z","title":"arXiv:2201.11903.https://arxiv.org/abs/2201.11903","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.025542Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:7fa75705f5e6e862cb8736551ed47bc39fa83e1beb0c1c58fc88c7ebe8d07274","observation_id":"565b6225-4cbb-4bad-b428-b6c40f3aa216","resolution":{"observed_at":"2026-08-07T05:38:35.025542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-07T07:43:16.294957Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-07T05:38:35.108517Z","title":"J., Shen, Y ., Wallis, P., et al., 2021.LoRA: Low-Rank Adaptation of Large Language Models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.108517Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:132663d1fbd00155366315b29f089970ccd019d12d9c9b8f25a30480a020a2a4","observation_id":"0ae773d6-c2a8-4f19-b2da-d8677023a547","resolution":{"observed_at":"2026-08-07T05:38:35.108517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.748655Z","title":null,"venue":null,"work_id":"663bcb07-075a-4302-abd6-2588fb2797e0","year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.223335Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:ed66aa8c06ee7361f3042c894254a9fda932073b41787f1aba9333ad53293fc5","observation_id":"0cff4e16-ad2a-46d4-ba3d-61f462485682","resolution":{"observed_at":"2026-08-07T05:38:40.849753Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.10077","last_updated":"2020-02-25T03:12:57Z","snapshot_observed_at":"2026-07-06T08:46:09.705605Z","submitted_at":"2019-12-20T19:49:32Z","title":"Are Transformers universal approximators of sequence-to-sequence functions?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.10077","snapshot_observed_at":"2026-08-07T05:38:35.295805Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.295805Z"},"links":{"cited_paper":"/paper/1912.10077","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:8795a4f2529f74497321ef35748ab5375134ded919236f222e3c785c3cc51f3e","observation_id":"036772fa-2944-4984-b8d5-206c295f6300","resolution":{"observed_at":"2026-08-07T05:38:35.295805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.01066","last_updated":"2023-08-11T19:27:58Z","snapshot_observed_at":"2026-08-08T13:08:09.726847Z","submitted_at":"2022-08-01T18:01:40Z","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.01066","snapshot_observed_at":"2026-08-07T05:38:35.365797Z","title":"arXiv:2208.01066.https://arxiv.org/abs/ 2208.01066","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.365797Z"},"links":{"cited_paper":"/paper/2208.01066","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:c5864aed7d1250d4ec688fadd663491f66d08fcfbd1e3e42fc95d3b988caeef5","observation_id":"024cab8f-b061-4b05-92cd-9b2032aa1505","resolution":{"observed_at":"2026-08-07T05:38:35.365797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-07T05:38:35.457268Z","title":"arXiv:2408.03314.https://arxiv.org/ abs/2408.03314","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.457268Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:b029a5348d60a6a67083c75765afcad995e851140dd2a7f951007b8eaa6130e3","observation_id":"f53505b2-cede-4056-8040-acfc15085046","resolution":{"observed_at":"2026-08-07T05:38:35.457268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07123","last_updated":"2025-02-10T23:36:41Z","snapshot_observed_at":"2026-08-08T13:42:39.119191Z","submitted_at":"2025-02-10T23:36:41Z","title":"A nested MLMC framework for efficient simulations on FPGAs","version":1},"cited_work":{"arxiv_id":"2502.07123","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.07123","snapshot_observed_at":"2026-08-07T05:38:38.045504Z","title":"A nested MLMC framework for efficient simulations on FPGAs","venue":"q-fin.CP","work_id":"e7c2b245-3b7b-4004-be96-e9555f5a7a7f","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.512582Z"},"links":{"cited_paper":"/paper/2502.07123","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:047db0ad89c8ca5348cd5fc1248477c3930f5a4e1d9d3e1b029a0eab1b9bf82f","observation_id":"444aab0f-4dd9-49b9-882d-5d583580fb56","resolution":{"observed_at":"2026-08-07T05:38:38.162661Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.623420Z","title":null,"venue":null,"work_id":"6e204dc9-a10d-40a8-a9dd-4837f3009148","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.590119Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:feb86f32dc12bffa3e5e8d16ac0f0a5b78a1d18a33cde552d00698c08fb5cd65","observation_id":"dfecb220-7198-4b08-9bd0-d258255cd90c","resolution":{"observed_at":"2026-08-07T05:38:40.672051Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.5253327","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:37.278289Z","title":"SSRN 5253327.http://dx.doi.org/10.2139/ssrn.5253327","venue":null,"work_id":"baf14fbd-f031-40da-8b6a-0cd711626dd0","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.694756Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:08b1dd68c7eff64f7068e5c0796e14963ca87ba943016cae0d475678c448fb46","observation_id":"e977dde5-7213-4110-aa3e-5d92ad9e6efc","resolution":{"observed_at":"2026-08-07T05:38:37.342593Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18521","last_updated":"2025-04-29T23:31:59Z","snapshot_observed_at":"2026-08-08T16:19:07.449066Z","submitted_at":"2024-07-26T05:34:34Z","title":"Patched MOA: optimizing inference for diverse software development tasks","version":4},"cited_work":{"arxiv_id":"2407.18521","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.18521","snapshot_observed_at":"2026-08-07T05:38:37.861490Z","title":"Patched MOA: optimizing inference for diverse software development tasks","venue":"cs.SE","work_id":"3c698c35-aac3-4fef-bef3-c44d5f92a69b","year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.813978Z"},"links":{"cited_paper":"/paper/2407.18521","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:674ed6fcde9311e866c14d488bf2bea9b34d27b402dd8e4e6959102f73321bfc","observation_id":"129666af-5872-474d-8876-81c115be9ded","resolution":{"observed_at":"2026-08-07T05:38:37.921589Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16557","last_updated":"2025-04-29T23:30:03Z","snapshot_observed_at":"2026-08-05T19:43:10.353864Z","submitted_at":"2024-07-23T15:12:14Z","title":"Patched RTC: evaluating LLMs for diverse software development tasks","version":3},"cited_work":{"arxiv_id":"2407.16557","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.16557","snapshot_observed_at":"2026-08-07T05:38:37.700756Z","title":"Patched RTC: evaluating LLMs for diverse software development tasks","venue":"cs.SE","work_id":"4e652222-ebe1-4d4d-87ea-7e8f2f8a3b48","year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.864125Z"},"links":{"cited_paper":"/paper/2407.16557","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:514bbc527525cb7503dd89d1ddf56467eedad743a99c570a1e56b3b0b0ee8842","observation_id":"0bbe7218-344e-4926-9150-bf5cdb7b65e4","resolution":{"observed_at":"2026-08-07T05:38:37.767944Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.462873Z","title":"GitHub.https://github.com/codelion/adaptive-classifier","venue":null,"work_id":"9558680f-4da6-4c99-9c8f-031d0cac319f","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:35.919981Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:b4b90bc12abbf853a7aea243461c355e647f1d1605531687e8109b092cf979ef","observation_id":"7790cbe4-a170-44a8-b46e-ec2b6eb966f5","resolution":{"observed_at":"2026-08-07T05:38:40.542395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.311002Z","title":null,"venue":null,"work_id":"0c2d92a7-784f-438b-9545-b694c287e738","year":2024},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.017179Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:1c9d751de937a6bbbe65a0bb4659dbf1b447639e6a6957772d3efd6545f08bb9","observation_id":"4463206d-8f44-4170-acbb-d11e74173876","resolution":{"observed_at":"2026-08-07T05:38:40.400743Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T05:38:36.102091Z","title":"arXiv:2410.12345.https://arxiv.org/abs/2410.12345","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.102091Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:f5c72e98cbfada6578c23804a3f67bdfa3c405b28168e02f2865b6e25c186b13","observation_id":"f0bc58ca-3b4f-4f89-b79a-670ced7faca0","resolution":{"observed_at":"2026-08-07T05:38:36.102091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11401","last_updated":"2021-04-12T15:42:18Z","snapshot_observed_at":"2026-08-07T05:44:30.677502Z","submitted_at":"2020-05-22T21:34:34Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11401","snapshot_observed_at":"2026-08-07T05:38:36.163579Z","title":"arXiv:2005.11401.https://arxiv.org/abs/2005.11401","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.163579Z"},"links":{"cited_paper":"/paper/2005.11401","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:53c93a719e12998d047d36f7abb96f90c14463c63705239d1f70716e1dd6dd11","observation_id":"eb843ab6-f906-4e05-82b0-c73a573b0673","resolution":{"observed_at":"2026-08-07T05:38:36.163579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-07T05:38:36.273599Z","title":"B., Mann, B., Ryder, N., et al., 2020.Language Models are Few-Shot Learners","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.273599Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:c62bd5dfa55091839c68e804e614fd89816335d1aaf32adf8aa2f97c93089ddf","observation_id":"b721c68a-ed7d-4ce3-93e9-a48db87c78b9","resolution":{"observed_at":"2026-08-07T05:38:36.273599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:40.148100Z","title":"GitHub.https://github.com/codelion/ pts 10","venue":null,"work_id":"faa262c6-a5fc-47c3-a6f8-ebd641aea30e","year":2025},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.345640Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:70212ad4d78c9493cfcac5d56e5adfc24f704986f8b43d61270dbc2bf8833bc9","observation_id":"5130a06f-9d92-4ecd-8787-3eff8960d462","resolution":{"observed_at":"2026-08-07T05:38:40.215236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.06423","last_updated":"2017-02-06T15:11:30Z","snapshot_observed_at":"2026-07-06T04:57:02.729338Z","submitted_at":"2016-05-20T16:26:45Z","title":"Coresets for Scalable Bayesian Logistic Regression","version":3},"cited_work":{"arxiv_id":"1605.06423","doi":null,"metadata_source":"pith","pith_arxiv_id":"1605.06423","snapshot_observed_at":"2026-08-07T05:38:37.452879Z","title":"Coresets for Scalable Bayesian Logistic Regression","venue":"stat.CO","work_id":"7ee7facd-c570-4f6a-8428-6bd00234b3bd","year":2016},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.427870Z"},"links":{"cited_paper":"/paper/1605.06423","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:eb4e60b82e1835ef0e98e3b8ede1d78ae90defbfbf3f6befcfb43c0f7fa4d628","observation_id":"37c09233-0ebe-4b60-9ce9-bbf6b6b029db","resolution":{"observed_at":"2026-08-07T05:38:37.508249Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.09384","last_updated":"2020-11-18T16:31:34Z","snapshot_observed_at":"2026-08-06T18:15:51.917590Z","submitted_at":"2020-11-18T16:31:34Z","title":"Introduction to Core-sets: an Updated Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.09384","snapshot_observed_at":"2026-08-07T05:38:36.501100Z","title":"arXiv:2011.09384.https://arxiv.org/abs/2011.09384","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.501100Z"},"links":{"cited_paper":"/paper/2011.09384","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:99a9ec18435444081f32100865ae235f41dfe0ba8ecdaa6975617b47576baf53","observation_id":"ca015fff-a586-45dc-873c-7d30d75f4d02","resolution":{"observed_at":"2026-08-07T05:38:36.501100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:36.571427Z","title":"Springer","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.571427Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:9483e0bc92a7936b3721909ddf1670c7db39e5a8d378b3b64a048471f5898809","observation_id":"eb39c7ea-34d7-46ec-9020-e8afdc45ea20","resolution":{"observed_at":"2026-08-07T05:38:36.571427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.10360","last_updated":"2022-03-17T11:49:55Z","snapshot_observed_at":"2026-07-06T10:51:12.871009Z","submitted_at":"2021-03-18T16:30:26Z","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.10360","snapshot_observed_at":"2026-08-07T05:38:36.638733Z","title":"J., 2021.Pre-trained Models for Natural Language Processing: A Survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.638733Z"},"links":{"cited_paper":"/paper/2103.10360","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:8353641c3069e38930085cabbed528c3ae24f9730cffce4981260470597d8203","observation_id":"1b50d77b-0a30-4f5b-845d-795734fca848","resolution":{"observed_at":"2026-08-07T05:38:36.638733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11457","last_updated":"2020-04-08T22:39:42Z","snapshot_observed_at":"2026-08-08T17:40:57.148833Z","submitted_at":"2020-02-25T06:14:59Z","title":"A short note on learning discrete distributions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.11457","snapshot_observed_at":"2026-08-07T05:38:36.718814Z","title":"C., 2020.A short note on learning discrete distributions","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.718814Z"},"links":{"cited_paper":"/paper/2002.11457","citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:c9cd8e0e86974fa8d6d633b4fde62360faf15f9704094c8e86bee8df6edbef5e","observation_id":"8e8e865d-b68c-4282-bafb-3da90f7e8e2c","resolution":{"observed_at":"2026-08-07T05:38:36.718814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:39.889165Z","title":"[SEP]\",","venue":null,"work_id":"68f5a606-8e84-43bc-ae14-13abf33286d9","year":1998},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.793605Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:8dfbc60efc1ee30b8acddb854a74dce0bd7ca902990fdbf756da56167001382a","observation_id":"d12de946-04b3-4691-9a08-b456dae50d48","resolution":{"observed_at":"2026-08-07T05:38:40.036286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:39.507612Z","title":null,"venue":null,"work_id":"620a7b0c-ef9f-4393-b82d-4d2f3c7bd467","year":null},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.872164Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:b2728a881613a18b251aff64be9895b58276b16be5b305770ba1492a5601d6ff","observation_id":"211b9d19-3ec6-435b-8d94-8073e018b5f7","resolution":{"observed_at":"2026-08-07T05:38:39.680340Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:39.198460Z","title":"Append the input x_i to p b","venue":null,"work_id":"4bd7d1c1-81a4-4573-960d-aa5e97fc5300","year":null},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:36.958029Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:9de0cb423d80153064c1a9873b651866f140062511c1e9ac2d55628a1ae19d50","observation_id":"f279a3fd-4bd8-43e0-a6f1-e8f65e0c9be5","resolution":{"observed_at":"2026-08-07T05:38:39.334583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:38.966832Z","title":null,"venue":null,"work_id":"743a22d9-fb7a-40ac-ab4e-4072b759070d","year":null},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:37.064347Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:c09e41aa7a793bc3a3d4d6edbb1910677729fa2558f7c2bcb53934d38890b0b9","observation_id":"7056ff61-a9f3-4b4b-addc-9100ad918f75","resolution":{"observed_at":"2026-08-07T05:38:39.066573Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:38:38.748027Z","title":"Great␣movie!","venue":null,"work_id":"9bf770d3-51e7-4950-b8d7-f6b5d333cf10","year":null},"citing_paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:37.134674Z"},"links":{"citing_paper":"/paper/2506.08060"},"observation_digest":"sha256:e4c8da683506877692f6f2aa567dc30038373b9feb8f973bf233088b9dbdfba5","observation_id":"a364e8b8-3f78-42b4-b265-6356f3d2d12e","resolution":{"observed_at":"2026-08-07T05:38:38.835562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.08060","last_updated":"2025-06-09T08:37:19Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T03:11:41.553573Z","submitted_at":"2025-06-09T08:37:19Z","title":"Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":26,"verified_exact":2,"verified_fuzzy":6},"total_outbound_references":38},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2506.08060."}