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Paper Citation Record · LEDGER

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2509.10753.

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

pith.paper-citation-record.v1
2509.10753 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:41:06.645062Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T15:44:01.229817Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T00:11:14.819100Z

Reference resolution

27 of 27 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved23
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b69ac8e4-1b27-49ef-a50d-73ef9bd21552 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Textbooks Are All You Need II: phi-1.5 technical report

Reference 1

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source=pdf_text observed=2026-08-04T17:41:04.809699Z digest=sha256:fd79c78a3254a14fdc53eaa129a4669d9d6988f74df997b13bb5bf9a0eddf738

Observation df14fd3a-251e-4f35-8e15-9f9adfbfc9db · outbound

This paper cites Each GPU is equipped with 96GB of HBM2 memory, ideal for handling large models and datasets.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Each GPU is equipped with 96GB of HBM2 memory, ideal for handling large models and datasets

Reference 3

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source=pdf_text observed=2026-08-04T17:41:06.560729Z digest=sha256:e2e08d720c03ba615163a77e362364a37a4d40d72056963c098d53c7b4f4edc0

Observation 385a94ea-d6f0-4069-9fd8-4d2e082ba3ae · outbound

This paper cites The Capacity for Moral Self-Correction in Large Language Models.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling The Capacity for Moral Self-Correction in Large Language Models

Reference 8

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source=pdf_text observed=2026-08-04T17:41:05.230225Z digest=sha256:4966e0361d4daf533a75d0455f5bd3b719a54326b136a3e4b9cbf2ba90e01741

Observation 000f6bed-fd34-4523-8b20-452299d7144f · outbound

This paper cites Mistral 7B.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Mistral 7B

Reference 9

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source=pdf_text observed=2026-08-04T17:41:05.310522Z digest=sha256:a222a34571ec3c34743418e623ff7fa9ebc632609d283b86ce5c89320e9147d8

Observation 7beba5fb-a0dc-475e-8bef-80be4bcf31eb · outbound

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

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Language Models (Mostly) Know What They Know

Reference 11

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source=pdf_text observed=2026-08-04T17:41:05.444914Z digest=sha256:b64a2754de5a2603af70889b62e415c851719091457225005d9c5bbbf3fcc6ae

Observation e83ffa5a-0066-4427-9fb3-337a76dce5ae · outbound

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

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:41:05.532203Z digest=sha256:da91bdb51385d408834e2ca162c5bf47fd374225eee76f18cdf45cd2280c710e

Observation 5e4797af-1bae-4cef-bd3f-32bdf1b56611 · outbound

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

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Teaching Models to Express Their Uncertainty in Words

Reference 14

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source=pdf_text observed=2026-08-04T17:41:05.669756Z digest=sha256:5c2d28295e9cbf459f39198bcd6d35828212eaf895a5b2b0d0a819e561b7655e

Observation 40e699ce-3cc9-4428-8b40-8efbc7962d2e · outbound

This paper cites In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering

Reference 15

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source=pdf_text observed=2026-08-04T17:41:05.783412Z digest=sha256:46db4907273c22037a8d5bd6fde4d46475d1ffd7b2b783fc0cf11d42e433d1ae

Observation d3015c2a-069f-404e-bf6b-e97fb291b3ba · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 18

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source=pdf_text observed=2026-08-04T17:41:05.990194Z digest=sha256:d523caff56bba266376605805a9bb0f58675e9e5da3771c0ca7f179dfdfb81cd

Observation 5a037301-62ca-4a06-9028-25d24be66df9 · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 19

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source=pdf_text observed=2026-08-04T17:41:06.030331Z digest=sha256:b154a1533ae6889210b3bbb457a19d34e5e9c8c015f365ad8641d564a43e6b0b

Observation 19cc7309-0a75-4058-b68a-2f9250200bdb · outbound

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

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

Reference 21

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source=pdf_text observed=2026-08-04T17:41:06.185564Z digest=sha256:c8d8678efd930f8d9b7398e164f332c6d2e60b80e5fae7b6c20909a475c02625

Observation cf6742d2-87a5-42ee-b867-9d6805780344 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

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source=pdf_text observed=2026-08-04T17:41:06.268989Z digest=sha256:1bea29e778e8df30e609d8793e9aa80bc9e6ea1b9495a9c4c1aefe8d02325614

Observation f7e1f32b-ae8b-4bdb-b0e2-c99877d8d701 · outbound

This paper cites We call this thefree energyF Q :T ∗ →Rand define it as a map such thatF Q is continuous, monotonic, and a thermodynamically extensive function of the token sequence.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling We call this thefree energyF Q :T ∗ →Rand define it as a map such thatF Q is continuous, monotonic, and a thermodynamically extensive function of the token sequence

Reference 23

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source=pdf_text observed=2026-08-04T17:41:06.469380Z digest=sha256:a3742c60403f27d86c33d19ce1fa76c86cacf0ee9b767c65b0bd6126e95769f9

Observation 1e98638d-f542-42af-8677-6640e2d84ea7 · outbound

This paper cites For HalluField, we generate 50 perturbations per temperature.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling For HalluField, we generate 50 perturbations per temperature

Reference 27

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source=pdf_text observed=2026-08-04T17:41:06.645062Z digest=sha256:c59aca1c182e7042254e83a9265b001bf3066b71f9c4f418f739c4a06635fca0

Observation 763519df-1e37-4d4a-a7c9-be4c76b26946 · outbound

This paper cites an unresolved cited work.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Unresolved cited work

Reference 1978

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source=pdf_text observed=2026-08-04T17:41:04.876432Z digest=sha256:9902044f5f5268462f694fd4c74f003e98073a37855a55da671514ac313941b4

Observation b5aab317-b1cd-4528-81ac-cf4ba00ad9ad · outbound

This paper cites On Faithfulness and Factuality in Abstractive Summarization.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling On Faithfulness and Factuality in Abstractive Summarization

Reference 2001

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source=pdf_text observed=2026-08-04T17:41:05.865883Z digest=sha256:b5835cfbfdbf266ab25714d639a917e445387bb4bd23830a1892150a459f309e

Observation ec49d2e4-3ded-41a6-aa80-86bbea3e21ef · outbound

This paper cites 10 Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling 10 Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and Yarin Gal

Reference 2007

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verified exact
doi, observed 2026-08-04T17:44:09.024790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T17:41:05.120600Z digest=sha256:e2f09d315ac941cacdce20c42a0c9546c08acb9b486e12656623eee669d8ce2f

Observation cf1c8232-8a1e-4ce3-a2d8-6eb0b51e9561 · outbound

This paper cites On Hallucination and Predictive Uncertainty in Conditional Language Generation.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling On Hallucination and Predictive Uncertainty in Conditional Language Generation

Reference 2010

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source=pdf_text observed=2026-08-04T17:41:06.415891Z digest=sha256:592d3a91a52022617b7f8b08380da189e2f35461626ab485d6bd67e6f40e809d

Observation b92854d8-3d78-4b48-9f3d-f77b14696bae · outbound

This paper cites Discovering Latent Knowledge in Language Models Without Supervision.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Discovering Latent Knowledge in Language Models Without Supervision

Reference 2015

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source=pdf_text observed=2026-08-04T17:41:04.927879Z digest=sha256:7c7d1c58509d63313c6684b9431dbba76dec5294a637b30f10cd9e11b5d31412

Observation d35c88ee-c2bb-439a-8f80-47caf66edce0 · outbound

This paper cites Self-Evaluation Improves Selective Generation in Large Language Models.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Self-Evaluation Improves Selective Generation in Large Language Models

Reference 2016

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source=pdf_text observed=2026-08-04T17:41:06.105394Z digest=sha256:cf445b4004f062a705d94b130dd81e26fbe54964d1e456164116a0a626feb98d

Observation 1a4507f6-c352-45dd-b9ab-e229af25c43e · outbound

This paper cites an unresolved cited work.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Unresolved cited work

Reference 2017

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source=pdf_text observed=2026-08-04T17:41:05.364328Z digest=sha256:f357fa50c4ab8aa57a5f4d998e6894f711608d7e5f75e5f18d1de7cf8deb9ee2

Observation f9a8beb4-9b82-4cd7-85fe-3cfee603c62c · outbound

This paper cites an unresolved cited work.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Unresolved cited work

Reference 2019

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source=pdf_text observed=2026-08-04T17:41:05.609459Z digest=sha256:1cbaff0805aa57d7e02b99fff6175b5fbe3551cc3ebc0107c6647b26e98a3bc5

Observation 361b5201-fb76-4ebe-a669-155c1eeb238d · outbound

This paper cites VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions

Reference 2020

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source=pdf_text observed=2026-08-04T17:41:05.919639Z digest=sha256:74df3d540cdd5f42535071af44a787b1617398d2dc048a36a545d911ab4d25fe

Observation 54682817-b1a9-48f9-a483-c90d461e4eb9 · outbound

This paper cites Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

Reference 2022

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source=pdf_text observed=2026-08-04T17:41:04.988614Z digest=sha256:88b5ef3ed2c25596e9df0636d73b3bbee2325b54f4ee54d8c2acb4bab8d339ad

Observation 2cb737a0-b2a2-4d02-9f69-79b5f7957ce6 · outbound

This paper cites LM vs LM: Detecting Factual Errors via Cross Examination.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling LM vs LM: Detecting Factual Errors via Cross Examination

Reference 2023

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source=pdf_text observed=2026-08-04T17:41:05.035830Z digest=sha256:1b0d8acb550735caa98a183bde68905c919c48d3af681ea63a70130131822bdf

Observation 7d9d4d2e-4eb5-4675-a87a-3d3f141414d0 · outbound

This paper cites Trapping LLM Hallucinations Using Tagged Context Prompts.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Trapping LLM Hallucinations Using Tagged Context Prompts

Reference 2024

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no resolver link, observed 2026-08-04T17:41:05.183010Z

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source=pdf_text observed=2026-08-04T17:41:05.183010Z digest=sha256:8a0f3c7e9e52e255d0e243e554105770f0218426eab9369c79edeca494051431

Observation 89c95b94-35f5-4707-8dde-bd25dbf35b43 · outbound

This paper cites Friedrich Sauvigny Ulrich Dierkes, Stefan Hildebrandt.Minimal Surfaces.

HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling Friedrich Sauvigny Ulrich Dierkes, Stefan Hildebrandt.Minimal Surfaces

Reference 2025

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verified exact
doi, observed 2026-08-04T17:44:08.745966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-04T17:41:06.338517Z digest=sha256:7cb30f5ce5adc4f77d232f3c10324130ea412658417dd1c7a915253f56857884

Pith citing papers

Observation 3f401e71-cddd-489b-8ca7-e1cdfec7f044 · inbound

The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive cites this paper.

The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling

Reference 35

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arxiv_id, observed 2026-05-12T00:11:14.826936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-07T15:44:01.229817Z digest=sha256:449cf63c6e9abab6b285ca3711a96ddcb188de6019061a8d42efa8dfaef769d6