Paper Citation Record · LEDGER

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2508.12905.

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

pith.paper-citation-record.v1
2508.12905 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:23:41.361418Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy11
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8193d3a-7119-4ba9-a946-e15e4860c21b · outbound

This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.774106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536c9bf0-e64e-4f66-8807-671a04274b11 · outbound

This paper cites Abstention budget adherence.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Abstention budget adherence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.574308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 08b8659e-ee71-4647-a341-e4a01e65283c · outbound

This paper cites QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML QUTE: Quantifying Uncertainty in TinyML with Early-exit-assisted ensembles for model-monitoring

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.789154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06c0085-72a0-4d05-8125-96698d551bc3 · outbound

This paper cites Adaptive Conformal Inference Under Distribution Shift.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Adaptive Conformal Inference Under Distribution Shift

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.845727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c47e2d6-2195-4987-89e6-cefaabef7e05 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Branchynet: Fast inference via early exiting from deep neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.099768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c63b0b9c-a7fd-4777-afe8-ccd11add85e7 · outbound

This paper cites (2019) in our discussion but do not optimize for them.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML (2019) in our discussion but do not optimize for them

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.586953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 030815cc-dc2a-4600-8dd7-031520457c21 · outbound

This paper cites Accessed: 2025-08-.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Accessed: 2025-08-

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:42.207066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ed230da-fd27-4c14-adf1-dd08fc3aa6ed · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.112637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7d7e9ce-40ea-4a3d-9847-4f9a53bad4c7 · outbound

This paper cites Hello Edge: Keyword Spotting on Microcontrollers.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Hello Edge: Keyword Spotting on Microcontrollers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.116882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cc2f8c-0c9a-48d4-be95-d124e1e93194 · outbound

This paper cites an unresolved cited work.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:23:42.080490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c168994-7a5e-4607-b697-7f8b5733e01c · outbound

This paper cites 14 Preprint A.2 D ATASETS We evaluate TCUQ and all baseline methods on four in-distribution datasets spanning both vision and audio, following standard TinyML evaluation practice.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML 14 Preprint A.2 D ATASETS We evaluate TCUQ and all baseline methods on four in-distribution datasets spanning both vision and audio, following standard TinyML evaluation practice

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:42.005186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 535de6bf-253c-49cd-b33c-b9e40f1ad255 · outbound

This paper cites For SpeechCmd, we convert raw WA V to Mel spectrograms (49×10×1) using a fixed front end; for vision datasets we apply standard per-dataset normalization.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML For SpeechCmd, we convert raw WA V to Mel spectrograms (49×10×1) using a fixed front end; for vision datasets we apply standard per-dataset normalization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.929279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b34b21c4-36d1-4e5c-b822-f0e678d66c47 · outbound

This paper cites While TS can improve calibration in static settings, it does not exploit temporal structure, streaming adaptation, or feature-level consistency, which are central to TCUQ.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML While TS can improve calibration in static settings, it does not exploit temporal structure, streaming adaptation, or feature-level consistency, which are central to TCUQ

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.917012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f164f0bd-1c45-492a-b09c-d9f4813e4175 · outbound

This paper cites Recent approaches such as Meronen et al.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Recent approaches such as Meronen et al

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.793493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 025d3649-a9b5-44f9-959b-df679f264ff0 · outbound

This paper cites PostN employs normalizing flows to model a predictive distribution for each input without increasing runtime memory requirements.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML PostN employs normalizing flows to model a predictive distribution for each input without increasing runtime memory requirements

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.778987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0c88c918-8ce3-409d-b51f-363f59a3ea39 · outbound

This paper cites an unresolved cited work.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:23:41.716887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de63658b-44ef-4a18-87e6-2ab4efafb43c · outbound

This paper cites wait for more context.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML wait for more context

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.704055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85400bc9-9c17-482f-92da-f15933d93621 · outbound

This paper cites Let the prediction confidence be c(x) = maxℓpϕ(y =ℓ| x).

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Let the prediction confidence be c(x) = maxℓpϕ(y =ℓ| x)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:41.648469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dbdd8811-fa03-48b3-819e-65b6fb64b18c · outbound

This paper cites Shiyu Liang, Yixuan Li, and R.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Shiyu Liang, Yixuan Li, and R

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.079341Z

Source-reported events for the cited work

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Observation 8ce848f2-1fd9-4603-9a1c-c5f10a63f3c3 · outbound

This paper cites Hydra: Preserving Ensemble Diversity for Model Distillation.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Hydra: Preserving Ensemble Diversity for Model Distillation

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.104754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e343966b-4383-475e-891f-2c9932558816 · outbound

This paper cites Learning multiple layers of features from tiny images.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Learning multiple layers of features from tiny images

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.014877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 501a1a1e-a052-4c23-b832-f6657edd6db6 · outbound

This paper cites Accessed: 2025-08-.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Accessed: 2025-08-

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:23:42.305566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fcaad608-e1e4-497f-817a-868c17c7f2f1 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.942849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9db4fe8-1c6f-4465-914e-7b98702413b9 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.108323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8b1abc1-ec61-4699-8b99-c115607f010c · outbound

This paper cites Getting a CLUE: A Method for Explaining Uncertainty Estimates.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Getting a CLUE: A Method for Explaining Uncertainty Estimates

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.779011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba7e2312-e135-44b7-aa2b-3c261a5544ea · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Imagenet: A large-scale hierarchical image database

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:40.784814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fba876e8-c4c8-407d-a16e-e8aaeda9a531 · outbound

This paper cites MNIST-C: A Robustness Benchmark for Computer Vision.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML MNIST-C: A Robustness Benchmark for Computer Vision

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T17:23:41.083891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5700d28-c554-4d94-8da9-50e629c10815 · outbound

This paper cites an unresolved cited work.

TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:23:42.160273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.