Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T19:24:07.449352Z
Paper Citation Record · LEDGER
What should an AI assessor optimise for?
As of 11 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2502.00365.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
68 of 68 outbound references displayed
- verified exact4
- verified fuzzy37
- unresolved27
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bc06a780-e6ff-40b9-9653-6a30edada75e · outbound
This paper cites write newline.
What should an AI assessor optimise for? write newline
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.220580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1cb619c-c37a-47e3-bef2-bac2a7360d3a · outbound
This paper cites and Alimoglu, F.
What should an AI assessor optimise for? and Alimoglu, F
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.225375Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3a56951e-7710-4deb-aa46-467a0d77d9c7 · outbound
This paper cites and Hoff, A.
What should an AI assessor optimise for? and Hoff, A
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.494359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8c6e7541-cf78-489e-b704-8eccd31c4e36 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.485341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bf1de2d0-87a5-4ad6-ab8b-043426033cbf · outbound
This paper cites and Kohavi, R.
What should an AI assessor optimise for? and Kohavi, R
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.476555Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 25cb8e31-2074-4a09-b1af-aef41b20c368 · outbound
This paper cites Robust optimization for deep regression.
What should an AI assessor optimise for? Robust optimization for deep regression
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.467507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3f3a58c6-e8b1-475e-96c0-d39e6efe0e0e · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.248029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56ac6a19-9e12-4743-ab8f-984b3251b77d · outbound
This paper cites MAGIC Gamma Telescope.
What should an AI assessor optimise for? MAGIC Gamma Telescope
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.453340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b494ed5b-7455-460a-9d9a-eab8947c729d · outbound
This paper cites On the Opportunities and Risks of Foundation Models.
What should an AI assessor optimise for? On the Opportunities and Risks of Foundation Models
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.255536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9dadde10-4a38-49e0-9022-f99b9b89a99d · outbound
This paper cites Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology.
What should an AI assessor optimise for? Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.259300Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9cabfe39-4174-405a-aa83-2097e0a5d013 · outbound
This paper cites A new typology design of performance metrics to measure errors in machine learning regression algorithms.
What should an AI assessor optimise for? A new typology design of performance metrics to measure errors in machine learning regression algorithms
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.444544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a682e3dd-0a4b-4aaf-b9e4-804c8330548c · outbound
This paper cites Classification and Regression Trees.
What should an AI assessor optimise for? Classification and Regression Trees
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.435458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation dc566bcd-3845-4b49-94f2-9777cdd793e5 · outbound
This paper cites Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula.
What should an AI assessor optimise for? Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.426258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e88cfe19-9591-4a37-8ee8-b8a7a1c4bb95 · outbound
This paper cites Loss functions for binary class probability estimation and classification: Structure and applications.
What should an AI assessor optimise for? Loss functions for binary class probability estimation and classification: Structure and applications
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.416193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8df7019c-b004-4186-aa9c-a8fe2a2a40af · outbound
This paper cites D., Martinez-Plumed, F., Tenenbaum, J.
What should an AI assessor optimise for? D., Martinez-Plumed, F., Tenenbaum, J
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.406276Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 021064d6-6f86-4f6f-8d92-278a833f2aad · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 16
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.397107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 44a2ecd3-2365-4daa-bcb8-1e9462eac07a · outbound
This paper cites and Lemaire, V.
What should an AI assessor optimise for? and Lemaire, V
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.387634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0d873ac9-886b-4415-9ccc-56e364c790be · outbound
This paper cites and Guestrin, C.
What should an AI assessor optimise for? and Guestrin, C
Reference 18
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-09T19:24:08.046046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ae69c9a7-cba2-4647-adb8-bc578c5efce9 · outbound
This paper cites J., and Jurman, G.
What should an AI assessor optimise for? J., and Jurman, G
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.378566Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e9991980-64d1-4545-94bc-0d3c527ea8f3 · outbound
This paper cites Support-vector networks.
What should an AI assessor optimise for? Support-vector networks
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.292405Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d9635ad-3036-4685-b8b3-47c47cef0f02 · outbound
This paper cites Learned lessons in credit card fraud detection from a practitioner perspective.
What should an AI assessor optimise for? Learned lessons in credit card fraud detection from a practitioner perspective
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.363947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8309e03e-28e5-44b7-b9ad-9260efbf3340 · outbound
This paper cites Bold: Dataset and metrics for measuring biases in open-ended language generation.
What should an AI assessor optimise for? Bold: Dataset and metrics for measuring biases in open-ended language generation
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.354727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f36df912-854e-40b9-b8d0-d525ae3e5d4c · outbound
This paper cites Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing.
What should an AI assessor optimise for? Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.302286Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation edc28fc9-099b-4614-91d0-f1176657afb2 · outbound
This paper cites Bootstrap Methods: Another Look at the Jackknife.
What should an AI assessor optimise for? Bootstrap Methods: Another Look at the Jackknife
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.306003Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a872dede-967f-4d00-ad13-417317b12585 · outbound
This paper cites Estimation and testing of forecast rationality under flexible loss.
What should an AI assessor optimise for? Estimation and testing of forecast rationality under flexible loss
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.345646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 97fc06c5-9e59-4a72-8cae-6903d7442ae1 · outbound
This paper cites Reward function design in reinforcement learning.
What should an AI assessor optimise for? Reward function design in reinforcement learning
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.336698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 2240c1a5-809f-49ba-ac15-5343b4f1c010 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 27
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.328293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 66df1865-44e0-48b9-ac6c-79724414719f · outbound
This paper cites Unveiling the robustness of machine learning families.
What should an AI assessor optimise for? Unveiling the robustness of machine learning families
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.319618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation af0ab72b-e4c3-4b3a-a0b7-18a79c1a9913 · outbound
This paper cites An introduction to roc analysis.
What should an AI assessor optimise for? An introduction to roc analysis
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.310624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c4a4fca1-7958-433a-a2ec-afda57794752 · outbound
This paper cites Regression towards mediocrity in hereditary stature.
What should an AI assessor optimise for? Regression towards mediocrity in hereditary stature
Reference 30
- Resolution
- verified exact
- raw_fallback, observed 2026-08-09T19:24:07.855256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 17af1824-cfde-4ae0-a70c-7eb8e3269118 · outbound
This paper cites and Raftery, A.
What should an AI assessor optimise for? and Raftery, A
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.302197Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 54ee706c-4cc4-4797-90e1-38133107410a · outbound
This paper cites and Ozhegov, E.
What should an AI assessor optimise for? and Ozhegov, E
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.293336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6068d844-0382-4ab1-a55a-fd76c8033ccf · outbound
This paper cites A unified view of performance metrics: Translating threshold choice into expected classification loss.
What should an AI assessor optimise for? A unified view of performance metrics: Translating threshold choice into expected classification loss
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.284152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3253b21e-7cd9-498e-b9ea-f065a89f8758 · outbound
This paper cites Training on the test set: Mapping the system-problem space in AI.
What should an AI assessor optimise for? Training on the test set: Mapping the system-problem space in AI
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.274387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ff4d94d5-8441-4f87-899d-9c0a8476ed8f · outbound
This paper cites Roc curves for regression.
What should an AI assessor optimise for? Roc curves for regression
Reference 35
- Resolution
- verified exact
- doi, observed 2026-08-09T19:24:07.504272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 2b2d1865-32c4-4a87-804b-a27e571781c9 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.344722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21db112c-1166-45f5-97c0-75e382e395bc · outbound
This paper cites RouterBench: A Benchmark for Multi-LLM Routing System.
What should an AI assessor optimise for? RouterBench: A Benchmark for Multi-LLM Routing System
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.347981Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f0bffc1-06f2-4a7b-b5ba-795dac859cd1 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.351447Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dde0cf6d-4bb9-4ee1-a28e-36065b86ecb1 · outbound
This paper cites and Waegeman, W.
What should an AI assessor optimise for? and Waegeman, W
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.255151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a7604de0-65fc-4e2b-894d-b154f0139155 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.246874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cbc4ec16-8042-45b0-9ce2-de6a24e15ce9 · outbound
This paper cites Language Models (Mostly) Know What They Know.
What should an AI assessor optimise for? Language Models (Mostly) Know What They Know
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.360812Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34b65ede-a3b1-445b-8515-19ffd1d4e7e0 · outbound
This paper cites Song popularity prediction dataset.
What should an AI assessor optimise for? Song popularity prediction dataset
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.238480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bdcda004-24b3-439b-889b-3a823886ff99 · outbound
This paper cites Lightgbm: A highly efficient gradient boosting decision tree.
What should an AI assessor optimise for? Lightgbm: A highly efficient gradient boosting decision tree
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.228165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 89a5517f-6a89-457f-b45e-fcb610fd766a · outbound
This paper cites and Barry, R.
What should an AI assessor optimise for? and Barry, R
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.371015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1eb34ae9-f65c-4de2-8011-a5e97dd83d85 · outbound
This paper cites H., Neumann, F., and Trautmann, H.
What should an AI assessor optimise for? H., Neumann, F., and Trautmann, H
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.374333Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 44eba3b0-0a69-4165-b2a6-a3e97df594af · outbound
This paper cites Making language models better reasoners with step-aware verifier.
What should an AI assessor optimise for? Making language models better reasoners with step-aware verifier
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.218998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 517ea66b-70f8-406e-be2c-9311ec9283b1 · outbound
This paper cites Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models.
What should an AI assessor optimise for? Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.381061Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de4c19e0-29fc-45e1-bf86-739017d0608b · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.209067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f2626513-d3e0-49ff-bf64-82c87d9809ac · outbound
This paper cites and Di Stefano, J.
What should an AI assessor optimise for? and Di Stefano, J
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.200586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8d780f16-0ef6-4d4a-a066-af7c3507bffe · outbound
This paper cites A data-driven approach to predict the success of bank telemarketing.
What should an AI assessor optimise for? A data-driven approach to predict the success of bank telemarketing
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.192025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 781aac97-894a-4da9-bebe-508d951c6f7b · outbound
This paper cites Abalone, 1995.
What should an AI assessor optimise for? Abalone, 1995
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.183089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cfa919e5-5bdd-4c43-bcbc-cbb938d8c5df · outbound
This paper cites Analyzing and predicting verification of data-aware process models–a case study with spectrum auctions.
What should an AI assessor optimise for? Analyzing and predicting verification of data-aware process models–a case study with spectrum auctions
Reference 52
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-09T19:24:07.711415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 07f84bf2-3f9e-4b67-b4e8-232338f45ba3 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 53
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.174543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 85be6463-29c7-4331-8188-af8664c70f2f · outbound
This paper cites 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances.
What should an AI assessor optimise for? 100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.403555Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b999616-a6fe-426c-96e5-dc4bc85b8229 · outbound
This paper cites Bleu: a method for automatic evaluation of machine translation.
What should an AI assessor optimise for? Bleu: a method for automatic evaluation of machine translation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.165847Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8c855313-784f-4963-8bdf-e8610faa3204 · outbound
This paper cites V., and Gulin, A.
What should an AI assessor optimise for? V., and Gulin, A
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.156405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation baa94ab7-9aa7-4ce8-8115-8c5485408bd2 · outbound
This paper cites PMLB v1.0: An open source dataset collection for benchmarking machine learning methods.
What should an AI assessor optimise for? PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.413079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7eddb7c8-4be9-4f2e-ab7a-7dcab32d13bf · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 58
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.147109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c1898210-e063-415f-b22b-3753f2849864 · outbound
This paper cites A proposal for scaling the scaling laws.
What should an AI assessor optimise for? A proposal for scaling the scaling laws
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.137753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c9503ed6-d078-4cba-9c74-c0648e231e80 · outbound
This paper cites Analysing the predictability of language model performance.
What should an AI assessor optimise for? Analysing the predictability of language model performance
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.128556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7b5876a9-c16f-4e69-9d71-a48e00bc9db8 · outbound
This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
What should an AI assessor optimise for? Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.426125Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ebe9feb-0d52-42e1-a058-6cf4b740462d · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:24:07.429740Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c1ee8f8-6974-4c61-b7ee-8fc3a3f87455 · outbound
This paper cites A., McSharry, P.
What should an AI assessor optimise for? A., McSharry, P
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.119412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4a7f802e-54e2-4f43-81e8-7c5f4e2b5a93 · outbound
This paper cites Facial and oral temperature data from a large set of human subject volunteers, 2023.
What should an AI assessor optimise for? Facial and oral temperature data from a large set of human subject volunteers, 2023
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.109410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1a3ba552-8ec0-453b-9e7b-7e8399c44ec5 · outbound
This paper cites an unresolved cited work.
What should an AI assessor optimise for? Unresolved cited work
Reference 65
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T19:24:08.100312Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9d7b61d0-a598-4c12-a678-6251aa2d3c15 · outbound
This paper cites Global health observatory data repository, 2015.
What should an AI assessor optimise for? Global health observatory data repository, 2015
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.091617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation db3b2850-bf1a-41ca-90e5-056ce4618d7b · outbound
This paper cites Team formation through an assessor: choosing marl agents in pursuit--evasion games.
What should an AI assessor optimise for? Team formation through an assessor: choosing marl agents in pursuit--evasion games
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.082225Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b4bab923-8d4b-4991-b0e3-69d723ff6fea · outbound
This paper cites Reject before you run: Small assessors anticipate big language models.
What should an AI assessor optimise for? Reject before you run: Small assessors anticipate big language models
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T19:24:08.072387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
No inbound Pith citation observations are available.