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Paper Citation Record · LEDGER

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 4 inbound Pith citation observations for arXiv:2505.20047.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.20047 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:06:33.306043Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:45:12.671790Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T07:54:43.182673Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact3
  • verified fuzzy6
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 300f7ad8-e1ea-4eb0-9b14-e0b431907868 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 6

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source=pdf_text observed=2026-08-07T14:06:29.697285Z digest=sha256:4cc6201be7906d7cfb5fd36a6e07fc4e826b88cb9a318dc45473166411dc71ac

Observation 3535cf87-e067-4d87-bf36-c89ab0beacfb · outbound

This paper cites URLhttps://aclanthology.org/2021.tacl-1.21/.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks URLhttps://aclanthology.org/2021.tacl-1.21/

Reference 7

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source=pdf_text observed=2026-08-07T14:06:29.808546Z digest=sha256:3f81d69da18e3227290222170b31a9046ea38ba0d1962b82da375f4dc5ec65ac

Observation e34a6ebb-e18b-4f4b-81fe-53d10bae56a9 · outbound

This paper cites FOLIO: Natural language reasoning with first-order logic.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks FOLIO: Natural language reasoning with first-order logic

Reference 8

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:06:29.908396Z digest=sha256:b47aef531465e46acf31bfb94b8e145791b984cf5179f4a05dab552741b4f868

Observation 68bba2f4-5ba6-4154-bb67-951a0485e0aa · outbound

This paper cites tailedness.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks tailedness

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-07T14:06:34.476109Z

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.

source=pdf_text observed=2026-08-07T14:06:33.306043Z digest=sha256:4cab843d938f87fe91b7f00fdfd307f238ca2ecd9bd356d12ddb3034f0414dca

Observation 17ff4aad-6d54-4831-9c44-eac50c3acc1f · outbound

This paper cites Towards a Mathematics Formalisation Assistant using Large Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Towards a Mathematics Formalisation Assistant using Large Language Models

Reference 11

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source=pdf_text observed=2026-08-07T14:06:30.094004Z digest=sha256:81da41b3f3e0ca80426b85045996cb19651b05ff8ebea8350ee8bf4b2b8e1da8

Observation 87665c19-5bb5-4104-922c-890f80c4f568 · outbound

This paper cites FIMO: A Challenge Formal Dataset for Automated Theorem Proving.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks FIMO: A Challenge Formal Dataset for Automated Theorem Proving

Reference 12

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source=pdf_text observed=2026-08-07T14:06:30.120173Z digest=sha256:383f3bb4c906e10822d22fe033b90a023e8869f479f3980bd9c714e3f29713bb

Observation 5894d4ff-3ce2-452b-828a-ad0ab1158df2 · outbound

This paper cites Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving

Reference 13

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source=pdf_text observed=2026-08-07T14:06:30.223009Z digest=sha256:af1687d6b93c7f3130b10fd6ac2a4bc9afa701bf462be64b4910ea368d661cd2

Observation 59730723-51a4-461f-82a8-a0eeb2d107c7 · outbound

This paper cites DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data

Reference 14

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source=pdf_text observed=2026-08-07T14:06:30.321310Z digest=sha256:5606d8b4a765c5cdb91e7ce1f6e30ffd2e5aef385fbf12bf2b1f3fd2d763b05d

Observation f5f7fc77-3ba5-4e58-baec-1c94e59c31db · outbound

This paper cites Proving Theorems Recursively.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Proving Theorems Recursively

Reference 16

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source=pdf_text observed=2026-08-07T14:06:30.451938Z digest=sha256:95e5c42344313eedaf866174cf24a10a4dce4b9b9b24c0092f10ee6fa57e6988

Observation 84f816bf-9ae5-4b7d-a69b-8777ebb6bdc7 · outbound

This paper cites Large Language Models' Understanding of Math: Source Criticism and Extrapolation.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Large Language Models' Understanding of Math: Source Criticism and Extrapolation

Reference 17

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verified exact
local_arxiv, observed 2026-08-07T14:06:34.217293Z

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.

source=pdf_text observed=2026-08-07T14:06:30.524766Z digest=sha256:42a6203ca86e1c9a3d50ac35057871a9ffa6db5cf6a8fe13e7c64ae2bccca5a3

Observation 3af7d02f-b50c-4b84-8c2e-abe168312436 · outbound

This paper cites Experimental results from applying GPT-4 to an unpublished formal language.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Experimental results from applying GPT-4 to an unpublished formal language

Reference 18

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local_arxiv, observed 2026-08-07T14:06:34.087320Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:06:30.595514Z digest=sha256:2e5c544d6734ad2bc47f09633e3f586f714943e05355ac31a0518b1afd504b0f

Observation b7071168-7956-4444-926e-7f149b065854 · outbound

This paper cites Large Language Models for Mathematicians.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Large Language Models for Mathematicians

Reference 19

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source=pdf_text observed=2026-08-07T14:06:30.665664Z digest=sha256:41bb9eee6d7b327e82fe8b2d578dc664fabfbe8e9dca6262f9e32e97aea20922

Observation 33f465c2-ff0d-44f4-8428-488c028241e4 · outbound

This paper cites An In-Context Learning Agent for Formal Theorem-Proving.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks An In-Context Learning Agent for Formal Theorem-Proving

Reference 20

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source=pdf_text observed=2026-08-07T14:06:30.740697Z digest=sha256:e51bd11a2351392d4d486cf1ae3f1d02d69ceabd0fc0a7a44143bef47c88891b

Observation af203b79-7824-4211-af81-b60ed8f6bc56 · outbound

This paper cites Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:06:33.898352Z

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.

source=pdf_text observed=2026-08-07T14:06:30.809313Z digest=sha256:fdcf7570134898888d1c7b29b96786c586010b5929163c0902e277edeb1f9450

Observation 81c35cd2-6997-4824-8bc3-e5bde035bdf5 · outbound

This paper cites Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

Reference 22

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local_arxiv, observed 2026-08-07T14:06:33.826206Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:06:30.898686Z digest=sha256:aa2e877bab7f2dc775e33acf4b65d90f3149cdf297a26f4a7363308f6e73eba3

Observation d757dba6-40e5-45d3-922b-cec9bb3913d9 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 23

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source=pdf_text observed=2026-08-07T14:06:31.022782Z digest=sha256:e7543534aa48b8b7ca6ea16a3ad17528b058bda1b54a4092927b40d7696e88b1

Observation 117a72f0-ac6a-4a25-9b3a-786bc51021ee · outbound

This paper cites GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

Reference 24

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source=pdf_text observed=2026-08-07T14:06:31.207570Z digest=sha256:7cb8414f59c5b33e50f3721e245fdd57be45d50124bcd8367607a798a4fe6a07

Observation 9248041d-88a7-4cec-ae3d-9d377ef6af79 · outbound

This paper cites Uncertainty estimation in large language models to support biodiversity conservation.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Uncertainty estimation in large language models to support biodiversity conservation

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T14:06:35.475117Z

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.

source=pdf_text observed=2026-08-07T14:06:31.382488Z digest=sha256:6f5e228400c553a9e6bc90d8f80d651753bfdd9307487ce94435e5ba08e541ff

Observation 35d8ddcd-51b4-4bd8-913c-a753309add62 · outbound

This paper cites BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Reference 28

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source=pdf_text observed=2026-08-07T14:06:31.859886Z digest=sha256:67865ed5b00fe8f87e8c386faab4c2818385dbd8f8593ab44aa2c73c208eb4e3

Observation 7ca760b9-ce92-4066-84e9-27a2b8c60474 · outbound

This paper cites Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models

Reference 29

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source=pdf_text observed=2026-08-07T14:06:31.972141Z digest=sha256:cb26141bb3ac0dab8994f7c381f9f37a6921899b804e712fc0c1a3cc5f465b23

Observation 536404a5-66bb-4d0b-985c-7493657f9ddc · outbound

This paper cites Selectively Answering Ambiguous Questions.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Selectively Answering Ambiguous Questions

Reference 30

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source=pdf_text observed=2026-08-07T14:06:32.093530Z digest=sha256:e8c040616812afaa0a59a0baea3a1b418cc869a572816e4d431343257dc7f81f

Observation 1db8c8c1-f61c-4f55-9fd9-cb4334c94bc3 · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 31

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source=pdf_text observed=2026-08-07T14:06:32.166409Z digest=sha256:dd09f24c5db37deeae300e3b50e95cf33e8bc3a260f84d9123c3d929b441e7f4

Observation 6bd4ec45-2561-4467-870b-0a729ffb7ada · outbound

This paper cites URLhttps://aclanthology.org/2024.acl-long.283.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks URLhttps://aclanthology.org/2024.acl-long.283

Reference 32

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:06:32.231112Z digest=sha256:e9389209fccbd852c443b1b1c7f778bfec1814eda1848b39bf367d50e14fa8f0

Observation 36eeba6a-9133-40de-80d8-9cf8a91cda0c · outbound

This paper cites Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Reference 33

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source=pdf_text observed=2026-08-07T14:06:32.320811Z digest=sha256:bf29686e124423f0f08e22a816d1921f64807a22600757b4568e8543237138a0

Observation a7feac1b-7ee4-43bd-8ef0-431544bef318 · outbound

This paper cites an unresolved cited work.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:06:32.433013Z digest=sha256:89da6fe4b7320d9e296de182b5fedbcdf2b7426f8ffd303062bcae7dacb6fc72

Observation e3b164d0-649a-4c78-b2b4-debe8abd9ef4 · outbound

This paper cites Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes

Reference 36

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source=pdf_text observed=2026-08-07T14:06:32.571772Z digest=sha256:63c1011bfe2a0479a4e1da53834d7bf1a28b0d1c7c0751b941054bd1594d18eb

Observation 06224edf-f45c-4091-a543-53e800819559 · outbound

This paper cites Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Reference 37

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source=pdf_text observed=2026-08-07T14:06:32.643092Z digest=sha256:f8c98b56062c2fa916983732bd8e468fd79dc07921ef2e0b9c616ac07348309d

Observation ec1db97f-76d1-473b-bdd5-ef12a1fd1741 · outbound

This paper cites Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Reference 40

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source=pdf_text observed=2026-08-07T14:06:32.927335Z digest=sha256:30d619cbe5e4e1b873907b1279464f204d13e6c23629e05e69a2279a11b1368e

Observation 2daa234d-4abf-4815-b0d2-c7da57892170 · outbound

This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 41

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source=pdf_text observed=2026-08-07T14:06:33.003689Z digest=sha256:b9615e7d3e6250a8f82562f32c19d85be1c7bfe0b91587a5c2a0e76c2168e3c0

Observation c2e52b91-94f0-4570-b6e4-070ccba28b5d · outbound

This paper cites Tobias Groot and Matias Valdenegro-Toro.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Tobias Groot and Matias Valdenegro-Toro

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-07T14:06:34.900367Z

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.

source=pdf_text observed=2026-08-07T14:06:33.087549Z digest=sha256:08ee8de9770c11e484a782e5ba0159826366b325d4341fd9ef1106df03060ae7

Observation 26ca16a6-c72c-45b0-8734-7d08d3e75e18 · outbound

This paper cites URLhttp://www.jstor.org/stable/2236703.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks URLhttp://www.jstor.org/stable/2236703

Reference 1951

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source=pdf_text observed=2026-08-07T14:06:32.501189Z digest=sha256:89468454daf2e8edc80aac05cf84260093be2eecf6ebb4515076ba4afe8595da

Observation 2fb398df-0607-4eb5-b251-2efd24aba0ca · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958,.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958,

Reference 1965

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source=pdf_text observed=2026-08-07T14:06:31.530258Z digest=sha256:3f0e525ef166d0ef41b26cb20d7d812c383c99699858b40084442350f9760b4d

Observation 0322fa87-bd72-4352-92b1-d471be2480f1 · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Teaching Models to Express Their Uncertainty in Words

Reference 1996

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source=pdf_text observed=2026-08-07T14:06:32.818788Z digest=sha256:8579969cc841fce1a94121b85f6a677a0ecc053b8c58482d2a57d30feb648f07

Observation c1562269-ef36-462f-9eca-00d5ce2bc3ac · outbound

This paper cites Synthesizing finite-state protocols from scenarios and requirements.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Synthesizing finite-state protocols from scenarios and requirements

Reference 1998

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verified fuzzy
raw_fallback, observed 2026-08-07T14:06:34.751805Z

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.

source=pdf_text observed=2026-08-07T14:06:33.175591Z digest=sha256:e48e16fbcab18f5a114dc9c18b390fb1bb1fddf6e15f6ae682b8c7c3dddff3b9

Observation 7978093a-8da9-47ff-b710-e0b5b4aeedf3 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2009

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source=pdf_text observed=2026-08-07T14:06:29.203613Z digest=sha256:9935de066eff85da1377fb72253c2eba1341bf78a6d256ea832f9e94f997f9a2

Observation 147a9f44-3ac0-4450-8d3a-88f847a284b9 · outbound

This paper cites LM-Polygraph: Uncertainty Estimation for Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks LM-Polygraph: Uncertainty Estimation for Language Models

Reference 2017

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no resolver link, observed 2026-08-07T14:06:31.679535Z

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source=pdf_text observed=2026-08-07T14:06:31.679535Z digest=sha256:1bdfcfa71a0a34127de166f68e2f8f28d1578c91b4b4af74c5cad60e3b72d89b

Observation 4111abfd-0bbd-45ff-9d42-7042cc604811 · outbound

This paper cites Quantifying Uncertainties in Natural Language Processing Tasks.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Quantifying Uncertainties in Natural Language Processing Tasks

Reference 2018

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metadata mismatch
local_arxiv, observed 2026-08-07T14:06:33.482051Z

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.

source=pdf_text observed=2026-08-07T14:06:32.739732Z digest=sha256:6d02ee07af4b2a90f62bc6b82529960fd082df339f33954b5ce8dafd0ae3a69b

Observation 3d942f1d-7334-4118-8833-460ceb6f8036 · outbound

This paper cites Generative Language Modeling for Automated Theorem Proving.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Generative Language Modeling for Automated Theorem Proving

Reference 2019

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no resolver link, observed 2026-08-07T14:06:30.374808Z

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source=pdf_text observed=2026-08-07T14:06:30.374808Z digest=sha256:239be40038248346f6bbb5e3cd793ca1b5892540faf109a24e176f96c4f525ef

Observation 1a89ae7f-4259-4120-892d-42ff6f6bd11f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Evaluating Large Language Models Trained on Code

Reference 2020

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source=pdf_text observed=2026-08-07T14:06:29.257173Z digest=sha256:b61d1d54debe61461fc5eb8d186986cf37389453eeccf58282f0f9a1fc9753cc

Observation 184b0bb3-661b-4f22-bc44-f51240de8649 · outbound

This paper cites Mistral 7B.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Mistral 7B

Reference 2021

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no resolver link, observed 2026-08-07T14:06:29.362119Z

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source=pdf_text observed=2026-08-07T14:06:29.362119Z digest=sha256:74824a4c2b9c616aa02ac279a20266670678052b444b86e40dedec1dfdce64a4

Observation cf8a6316-ee8f-4984-b8ad-0f6c641219ac · outbound

This paper cites Language Models (Mostly) Know What They Know.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Language Models (Mostly) Know What They Know

Reference 2022

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no resolver link, observed 2026-08-07T14:06:29.584972Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:06:29.584972Z digest=sha256:bc47ae68247fc4a64d327a742eb6a851d6d345ee3f4515144b060eb23386ffef

Observation 5f2baf8d-bde2-4b56-8c9a-d70924117819 · outbound

This paper cites Emergent Abilities of Large Language Models.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks Emergent Abilities of Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-07T14:06:29.472186Z digest=sha256:ec3002c1de77b476892951cf1d3c8035fa6aee14b153aea81f8eaa335c3c53ab

Observation 72b19340-8d15-42e1-bb17-b4e2376a1c54 · outbound

This paper cites doi: 10.18653/v1/2024.

Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks doi: 10.18653/v1/2024

Reference 2024

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no resolver link, observed 2026-08-07T14:06:29.995705Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:06:29.995705Z digest=sha256:aa78f5c1e7707b08d6138a9a07276f91dedaa884cb16babdd129e81d7d869dc3

Pith citing papers

Observation a0a854e3-0c2d-4435-9dfe-59f31b2e00ba · inbound

AI4Research: A Survey of Artificial Intelligence for Scientific Research cites this paper.

AI4Research: A Survey of Artificial Intelligence for Scientific Research Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Reference 221

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no resolver link, observed 2026-08-06T20:45:12.671790Z

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source=pdf_text observed=2026-08-06T20:45:12.671790Z digest=sha256:374f42445d8a807ee0e855d55798d42280be699350e6e82f51d140b27bbb88ce

Observation aa54143a-4923-43ac-9b70-45b696456023 · inbound

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation cites this paper.

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Reference 6

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verified exact
arxiv_id, observed 2026-05-15T05:29:46.944740Z

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.

source=pdf_text observed=2026-05-15T05:29:17.744966Z digest=sha256:6d0869778fb25d2ccb3dfb413d7d7e9d3e37e5fc71da3ae1c0a54bec32cf5548

Observation 108fe26f-bc3f-46f9-91bb-e3da03d1e4a8 · inbound

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation cites this paper.

Reliability-Gated Source Anchoring for Continual Test-Time Adaptation Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Reference 6

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verified exact
arxiv_id, observed 2026-05-20T20:29:00.068897Z

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.

source=pdf_text observed=2026-05-20T20:24:01.811656Z digest=sha256:81d6dd479552ce1889e3401d0f31ba837da82cd9a219a1ada862a8051e066bc4

Observation 25f15a8b-2f9e-4019-80d3-9c5bbef526f5 · inbound

CausalGuard: Conformal Inference under Graph Uncertainty cites this paper.

CausalGuard: Conformal Inference under Graph Uncertainty Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks

Reference 22

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arxiv_id, observed 2026-05-22T07:54:43.185224Z

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.

source=pdf_text observed=2026-05-22T07:51:41.874143Z digest=sha256:4b56f7bbfcbafe90cfffacfca5134075439b1bb339a0150a26c617e8a28a212a