Paper Citation Record · LEDGER

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications

As of 9 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.05141.

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

pith.paper-citation-record.v1
2507.05141 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:40:21.835629Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39f28ebc-2994-4ca0-8cde-f2e27b8fe018 · outbound

This paper cites On calibration of modern neural networks,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications On calibration of modern neural networks,

Reference 1

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Observation 9880b38c-8329-4b22-8c59-7bff95ec2bb7 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 2

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Observation 54a9ebd6-9efb-47c5-b878-12838eb67161 · outbound

This paper cites Neurosymbolic ai: The 3 rd wave,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Neurosymbolic ai: The 3 rd wave,

Reference 3

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Observation 482791e2-134f-4d3b-af23-dcd9d9e0dbbc · outbound

This paper cites On the robustness and reliability of late multi-modal fusion using probabilistic circuits,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications On the robustness and reliability of late multi-modal fusion using probabilistic circuits,

Reference 4

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Observation c88cc2bc-d5f1-4114-b5f6-f4a8ff32b108 · outbound

This paper cites A Scalable Approach to Probabilistic Neuro-Symbolic Robustness Verification.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications A Scalable Approach to Probabilistic Neuro-Symbolic Robustness Verification

Reference 5

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Observation dbf6fc1b-aba0-40bb-b102-881940e69348 · outbound

This paper cites Einsum net- works: Fast and scalable learning of tractable probabilistic circuits,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Einsum net- works: Fast and scalable learning of tractable probabilistic circuits,

Reference 6

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Observation 400d857d-ad05-4592-a88e-3460742918d0 · outbound

This paper cites COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications COLEP: Certifiably Robust Learning-Reasoning Conformal Prediction via Probabilistic Circuits

Reference 7

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Observation 0eae516d-ecaf-488a-8016-aec2bb04a828 · outbound

This paper cites MLPerf Tiny Benchmark.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications MLPerf Tiny Benchmark

Reference 8

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Observation c8b6af03-00e1-4f89-bcc8-59b604506b3d · outbound

This paper cites Darwiche, Modeling and reasoning with Bayesian networks.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Darwiche, Modeling and reasoning with Bayesian networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:25.461753Z

Source-reported events for the cited work

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Observation 6c3572e1-570d-4d49-9634-321ded306056 · outbound

This paper cites Probabilistic circuits: A unifying framework for tractable probabilistic models,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Probabilistic circuits: A unifying framework for tractable probabilistic models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:25.237636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f1faac5-01c5-4da3-84ef-630d0c4a8b5e · outbound

This paper cites On relaxing determinism in arithmetic circuits,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications On relaxing determinism in arithmetic circuits,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:25.048660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e63d9bf-8bfe-4785-9cb1-308da811a2f2 · outbound

This paper cites Problp: A framework for low-precision probabilistic inference,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Problp: A framework for low-precision probabilistic inference,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.874225Z

Source-reported events for the cited work

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Observation 6a2ecdec-dad3-4450-8efc-c1520e5bbe68 · outbound

This paper cites FPGA implementation of bayesian network inference for an embedded diag- nosis,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications FPGA implementation of bayesian network inference for an embedded diag- nosis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.710280Z

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Observation 71d69b95-292b-4e97-a17b-2f18385ad0bc · outbound

This paper cites Towards real-time, on-board, hardware-supported sen- sor and software health management for unmanned aerial systems,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Towards real-time, on-board, hardware-supported sen- sor and software health management for unmanned aerial systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.494042Z

Source-reported events for the cited work

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Observation 6a366548-d908-4c7f-a46f-ea683575dd20 · outbound

This paper cites Comparison of arithmetic number formats for inference in sum-product networks on fpgas,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Comparison of arithmetic number formats for inference in sum-product networks on fpgas,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.366287Z

Source-reported events for the cited work

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Observation 097d44aa-8663-4b3e-bd37-67c7947bb94d · outbound

This paper cites On the use of bayesian networks for resource-efficient self-calibration of analog/rf ics,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications On the use of bayesian networks for resource-efficient self-calibration of analog/rf ics,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.246240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b654979f-ef8b-4ac1-a447-38aaf7774bb5 · outbound

This paper cites Klay: Accelerating arithmetic circuits for neurosymbolic ai,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Klay: Accelerating arithmetic circuits for neurosymbolic ai,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:24.019552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ef26aa0-be5b-4998-89d1-fb600983b9b7 · outbound

This paper cites Local computations with prob- abilities on graphical structures and their application to expert systems,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Local computations with prob- abilities on graphical structures and their application to expert systems,

Reference 18

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Observation 583e39ac-49ed-4dd5-8c01-fb54faa03fd9 · outbound

This paper cites Compiling bayesian networks using vari- able elimination,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Compiling bayesian networks using vari- able elimination,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:23.660792Z

Source-reported events for the cited work

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Observation 3aba66b2-113e-411b-8603-7ee206cf3edc · outbound

This paper cites Compiling relational bayesian networks for exact inference,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Compiling relational bayesian networks for exact inference,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:23.481904Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0573f97d-3110-41d6-8aee-24c1e665a1dd · outbound

This paper cites A differential approach to probabilistic inference,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications A differential approach to probabilistic inference,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:23.297551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c82542ca-0312-496f-aadd-4e9ef059ef9c · outbound

This paper cites Dpu: Dag processing unit for irregular graphs with precision-scalable posit arithmetic in 28 nm,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Dpu: Dag processing unit for irregular graphs with precision-scalable posit arithmetic in 28 nm,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:23.132236Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 56d8e071-be82-4d07-824f-9813e1694791 · outbound

This paper cites [Online].

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications [Online]

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:22.931355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 566a04a2-09c2-4a82-a241-bc745e6c6436 · outbound

This paper cites Systems, ESP32 Series Datasheet , January 2023, https://www.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Systems, ESP32 Series Datasheet , January 2023, https://www

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:22.740269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ecee4384-2ddb-4612-8283-d693dd9c066e · outbound

This paper cites Automatic mapping of the sum-product network inference problem to fpga-based accelerators,.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications Automatic mapping of the sum-product network inference problem to fpga-based accelerators,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:40:22.500462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.