Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:56:34.350227Z
Paper Citation Record · LEDGER
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
As of 9 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2506.02058.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-10T15:40:18.133637Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T10:06:01.278099Z
Reference resolution
65 of 65 outbound references displayed
- verified exact3
- verified fuzzy29
- unresolved31
- parse uncertain0
- malformed identifier2
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 72ab1322-29cf-4754-a082-ab21a584d42e · outbound
This paper cites GPT-4 Technical Report.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? GPT-4 Technical Report
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.406168Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6856b69a-28b4-4b10-85a5-ef242ebea546 · outbound
This paper cites Physics of Language Models: Part 3.1, Knowledge Storage and Extraction.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.482658Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 483df34c-5857-4588-a5f8-e33033e45f75 · outbound
This paper cites Claude3.7 Sonnetsystemcard.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Claude3.7 Sonnetsystemcard
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.145924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 310dca0d-c683-4f36-8c64-c3e4099af996 · outbound
This paper cites On the Opportunities and Risks of Foundation Models.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? On the Opportunities and Risks of Foundation Models
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.637691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 688d1427-ca52-42e9-8eeb-76a76e84da90 · outbound
This paper cites Eight things to know about large language models.Critical AI, 2(2), 2024.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Eight things to know about large language models.Critical AI, 2(2), 2024
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.701569Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 52c15b9c-a07c-4c0d-8f30-eec444771e7d · outbound
This paper cites Language models are few-shot learners.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Language models are few-shot learners
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.768209Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb8c60f9-0d8a-4f75-b3b3-1ac1710b9d85 · outbound
This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Sparks of Artificial General Intelligence: Early experiments with GPT-4
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:29.866099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7bf8963-f614-4700-8a85-8fd0267fb6ab · outbound
This paper cites Quantifying memorization across neural language models.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Quantifying memorization across neural language models
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.124339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation acbe91c0-5b91-48fb-a285-504003286703 · outbound
This paper cites How do large language models acquire factual knowledge during pretraining? In Neural Information Processing Systems, 2024.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? How do large language models acquire factual knowledge during pretraining? In Neural Information Processing Systems, 2024
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.114386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3dcb6d7e-dcd2-4478-b92e-73d7c8488979 · outbound
This paper cites A survey on evaluation of large language models.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? A survey on evaluation of large language models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:30.089145Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6a89fa4-efc2-4f5e-abc6-b7a8f00ec216 · outbound
This paper cites Estimating the number of species in a stochastic abundance model.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Estimating the number of species in a stochastic abundance model
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.099450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c10ddbc8-4a61-4e07-a370-281b9cbf1b2d · outbound
This paper cites A new statistical approach for assessing similarity of species composition with incidence and abundance data.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? A new statistical approach for assessing similarity of species composition with incidence and abundance data
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.090536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f6da8e46-f8f9-4d7d-9a97-54c0cf7f2fed · outbound
This paper cites Gonzalez, and Ion Stoica.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Gonzalez, and Ion Stoica
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.081757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b03eeb4f-30c9-42f9-96c8-d6df7e737740 · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Training Verifiers to Solve Math Word Problems
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:30.466711Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 378f9079-eb0f-42d9-8c91-8c2c76031828 · outbound
This paper cites Forgetwhat you know about LLMs evaluations—LLMs are like a chameleon.arXiv preprint arXiv:2502.07445, 2025.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Forgetwhat you know about LLMs evaluations—LLMs are like a chameleon.arXiv preprint arXiv:2502.07445, 2025
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:30.563192Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48ea9d4c-b2af-4788-8e7a-be70489ca6f4 · outbound
This paper cites Springer US, Boston, MA, 2009.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Springer US, Boston, MA, 2009
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:30.691748Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1127a67f-eacf-47b3-8e4f-5f31f4a9b94c · outbound
This paper cites CURIE: Evaluating LLMs on multitask scientific long-context understanding and reasoning.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? CURIE: Evaluating LLMs on multitask scientific long-context understanding and reasoning
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.072802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3dd36863-3d46-41a1-b651-f92f6d11ca24 · outbound
This paper cites Data science at the singularity.Harvard Data Science Review, 6(1), 2024.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Data science at the singularity.Harvard Data Science Review, 6(1), 2024
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:30.902979Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b88b454a-e470-4841-8662-022324c9d8d5 · outbound
This paper cites Estimating the number of unseen species: How many words did Shakespeare know?Biometrika, 63(3):435–447, 1976.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Estimating the number of unseen species: How many words did Shakespeare know?Biometrika, 63(3):435–447, 1976
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.057578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fec3978d-2ff7-424c-a75c-b4eb4fbd38ac · outbound
This paper cites Near-optimal estimation of the unseen under regularly varying tail populations.Bernoulli, 29(4):3423–3442, 2023.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Near-optimal estimation of the unseen under regularly varying tail populations.Bernoulli, 29(4):3423–3442, 2023
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.047540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ca5772c1-a696-4352-a239-2cbaa2b9bfed · outbound
This paper cites Good-turing frequency estimation without tears.Journal of quantitative linguistics, 2(3):217–237, 1995.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Good-turing frequency estimation without tears.Journal of quantitative linguistics, 2(3):217–237, 1995
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:31.201554Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2f94198c-03f9-46b7-83f1-1ac2be5258f3 · outbound
This paper cites The population frequencies of species and the estimation of population parameters.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? The population frequencies of species and the estimation of population parameters
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.031401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f11c90b2-fe19-45a4-9762-1131b0048e9b · outbound
This paper cites Estimating knowledge in large language models without generating a single token.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Estimating knowledge in large language models without generating a single token
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.021476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f17a9559-781b-4bc9-8727-ba3f6e3ed67c · outbound
This paper cites The Llama 3 Herd of Models.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? The Llama 3 Herd of Models
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:31.486703Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aae552b3-fbaa-4cbf-9c85-4b0dfad527ba · outbound
This paper cites Optimal prediction of the number of unseen species with multiplicity.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Optimal prediction of the number of unseen species with multiplicity
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:35.011026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e1b4f6e4-a378-4ed5-83a4-01e165bbcbb1 · outbound
This paper cites Measuring massive multitask language understanding.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Measuring massive multitask language understanding
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:31.701733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2d78d870-0ef7-45a6-8018-4b91dc929097 · outbound
This paper cites The curious case of neural text degeneration.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? The curious case of neural text degeneration
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:31.806850Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3369593b-ec38-4dc2-93f2-35b1cbc63d97 · outbound
This paper cites GPT-4o System Card.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? GPT-4o System Card
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:31.905024Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b7ece32-5645-4610-a2fb-3ebc3a7c75dc · outbound
This paper cites Mistral 7B.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Mistral 7B
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:32.057825Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6655ea8-530c-4da3-ae41-b5589d52714f · outbound
This paper cites Calibrated language models must hallucinate.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Calibrated language models must hallucinate
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.989799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b677d7cb-2225-45af-b630-69fe5edaf29d · outbound
This paper cites Line of duty: Evaluating LLM self-knowledge via consistency in feasibility boundaries.arXiv preprint arXiv:2503.11256, 2025.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Line of duty: Evaluating LLM self-knowledge via consistency in feasibility boundaries.arXiv preprint arXiv:2503.11256, 2025
Reference 31
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T11:56:34.654800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation aec70103-f245-46c7-a6cd-500493c24cc3 · outbound
This paper cites Too many AIs.https://dev.to/leeaao/too-many-ais-24nb, 2025.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Too many AIs.https://dev.to/leeaao/too-many-ais-24nb, 2025
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.979147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 57c732cf-9d26-4733-8cec-29c15d6a59d4 · outbound
This paper cites BioASQ-QA: A manually curated corpus for biomedical question answering.Scientific Data, 10 (1):170, 2023.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? BioASQ-QA: A manually curated corpus for biomedical question answering.Scientific Data, 10 (1):170, 2023
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.969869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1fa5d269-dc34-47e8-bc34-dcdc8e918390 · outbound
This paper cites How pre-trained language models capture factual knowledge? A causal-inspired analysis.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? How pre-trained language models capture factual knowledge? A causal-inspired analysis
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.960897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 95f36e0b-4d3d-4373-b917-47032910befc · outbound
This paper cites ROUGE: A package for automatic evaluation of summaries.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? ROUGE: A package for automatic evaluation of summaries
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.951469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a9b5a12a-3641-4fcb-9030-656528f8c9ac · outbound
This paper cites DeepSeek-V3 Technical Report.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? DeepSeek-V3 Technical Report
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:32.742702Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ab7b408-c554-4834-8f88-2094453ab49a · outbound
This paper cites Feder Cooper, Daphne Ippolito, Christopher A.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Feder Cooper, Daphne Ippolito, Christopher A
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.942557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9bdadb55-1361-4762-aa38-54a9fcac97a8 · outbound
This paper cites ChatGPT-3.5-turbo.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? ChatGPT-3.5-turbo
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.933728Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 85ce83e6-eff3-4976-9842-f6f8c64f84f4 · outbound
This paper cites Competitive distribution estimation: Why is Good-Turing good.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Competitive distribution estimation: Why is Good-Turing good
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.925398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f17f5189-d0c5-46f6-8727-8bcb583d3a86 · outbound
This paper cites Always Good Turing: Asymptotically optimal probability estimation.Science, 302(5644):427–431, 2003.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Always Good Turing: Asymptotically optimal probability estimation.Science, 302(5644):427–431, 2003
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.916852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f2c6e254-e81a-4a3b-8fbc-06a456d11fd1 · outbound
This paper cites Optimal prediction of the number of unseen species.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Optimal prediction of the number of unseen species
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.907642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ce2a9fe4-7ca7-4a92-98d4-b5a1a592c3c0 · outbound
This paper cites BLEU: A method for automatic evaluation of machine translation.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? BLEU: A method for automatic evaluation of machine translation
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.897789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0faef811-19d8-4ae0-9219-45c3b109bcb2 · outbound
This paper cites an unresolved cited work.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Unresolved cited work
Reference 43
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:56:34.888538Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 68e09110-55f4-421b-9086-8ebd96634a3c · outbound
This paper cites Humanity's Last Exam.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Humanity's Last Exam
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:33.693512Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 900d1761-f576-45ef-b239-387891575e95 · outbound
This paper cites Do large language models know how much they know?arXiv preprint arXiv:2502.19573, 2025.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Do large language models know how much they know?arXiv preprint arXiv:2502.19573, 2025
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:33.840301Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96fb3030-8cd4-4bf9-90ff-ba9b27161418 · outbound
This paper cites AI and the everything in the whole wide world benchmark.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? AI and the everything in the whole wide world benchmark
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.879529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4b1c0bfa-8e40-4db1-82b9-0aa6c63ca32c · outbound
This paper cites NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.870380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation af1bb3c2-8c34-48f4-ba7e-f3450f84ede4 · outbound
This paper cites NeurIPS 2023 LLM Efficiency Fine-tuning Competition.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? NeurIPS 2023 LLM Efficiency Fine-tuning Competition
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.073212Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fadec12e-09ec-48bd-bdad-4678fb372d40 · outbound
This paper cites Human disease ontology 2022 update.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Human disease ontology 2022 update
Reference 49
- Resolution
- verified exact
- doi, observed 2026-08-07T11:56:34.393035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bfa37a32-4556-4365-bde5-612215903355 · outbound
This paper cites Auto- Prompt: Eliciting knowledge from language models with automatically generated prompts.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Auto- Prompt: Eliciting knowledge from language models with automatically generated prompts
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.860670Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4278205c-6193-4b12-809a-f2a1b2779fd5 · outbound
This paper cites Welcome to the era of experience.Google AI, 2025.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Welcome to the era of experience.Google AI, 2025
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.304140Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb6f8d71-aa20-4602-a8c0-b521e391c82c · outbound
This paper cites The Leaderboard Illusion.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? The Leaderboard Illusion
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.307356Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0714b062-436d-42fa-8830-9680c3342321 · outbound
This paper cites Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Large language models encode clinical knowledge.Nature, 620(7972):172–180, 2023
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.311072Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63154cd9-7896-4b3b-bc9f-231e38059ce7 · outbound
This paper cites The bitter lesson.Incomplete Ideas (blog), 13(1):38, 2019.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? The bitter lesson.Incomplete Ideas (blog), 13(1):38, 2019
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.314529Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdf82a61-b4c1-4319-99ba-e27ea41fb45c · outbound
This paper cites Galactica: A Large Language Model for Science.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Galactica: A Large Language Model for Science
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.317573Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf256b5f-6e75-449f-bc4e-52fb3deaba4d · outbound
This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.320882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18c493fd-f9b1-4e8b-9689-78de7603398e · outbound
This paper cites an unresolved cited work.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Unresolved cited work
Reference 57
- Resolution
- malformed identifier
- doi_truncated, observed 2026-08-07T11:56:34.382001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 64e7e780-3939-43a2-953a-1324e6ab1c40 · outbound
This paper cites Language Models are Open Knowledge Graphs.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Language Models are Open Knowledge Graphs
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.327247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60d4733b-45b1-4bef-880f-000b5a15117c · outbound
This paper cites Can AI Be as Creative as Humans?.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Can AI Be as Creative as Humans?
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.330717Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d323f19-07f3-41a9-9f95-676aaa7aae99 · outbound
This paper cites Chi, Quoc V.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Chi, Quoc V
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.830980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 40608028-8344-4c9d-bbfe-42717f898ea3 · outbound
This paper cites Estimating the probabilities of rare outputs in language models.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Estimating the probabilities of rare outputs in language models
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.822545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8f4f37ea-0c4b-4110-a534-f91081bc8a5e · outbound
This paper cites Qwen2.5 Technical Report.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Qwen2.5 Technical Report
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:34.340659Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 893d72c8-1572-424b-9370-94f79a7c44b2 · outbound
This paper cites A careful examination of large language model performance on grade school arithmetic.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? A careful examination of large language model performance on grade school arithmetic
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:56:34.812963Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 88fcaddc-d668-4a60-87fd-a06a821f89a5 · outbound
This paper cites Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions
Reference 64
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T11:56:34.413661Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 29d200ca-a50b-460f-8047-70ddb01d209f · outbound
This paper cites Test your knowledge of mathematical theorems by listing 20 theorem names, separated by commas.
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know? Test your knowledge of mathematical theorems by listing 20 theorem names, separated by commas
Reference 65
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T11:56:34.803679Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
Observation 4f39711d-05df-4367-bdf0-2987ca8ae6db · inbound
UCS: Estimating Unseen Coverage for Improved In-Context Learning cites this paper.
UCS: Estimating Unseen Coverage for Improved In-Context Learning Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:06:01.283344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.