Paper Citation Record · LEDGER

Sampling Preferences Yields Simple Trustworthiness Scores

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.03399.

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

pith.paper-citation-record.v1
2506.03399 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:10:00.244402Z

measured 23 of 23 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

23 of 23 outbound references displayed

verified exact0
verified fuzzy13
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fcfbdf54-318e-4cd7-aaca-970f18209af3 · outbound

This paper cites Publications Office, 2019.

Sampling Preferences Yields Simple Trustworthiness Scores Publications Office, 2019

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:03.907834Z

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 f6be92c6-1b46-4d8d-9c53-f9f35840025b · outbound

This paper cites Explaining models: an empirical study of how explanations impact fairness judgment.

Sampling Preferences Yields Simple Trustworthiness Scores Explaining models: an empirical study of how explanations impact fairness judgment

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:03.739942Z

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 fa6df046-b87e-4257-8fa7-5a25b10c48e3 · outbound

This paper cites an unresolved cited work.

Sampling Preferences Yields Simple Trustworthiness Scores Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.040537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45f371d7-2e7a-4cc4-9bf0-200eb12c5aab · outbound

This paper cites Interpreting black-box models: a review on explainable artificial intelligence.Cognitive Computation, 16(1):45–74, 2024.

Sampling Preferences Yields Simple Trustworthiness Scores Interpreting black-box models: a review on explainable artificial intelligence.Cognitive Computation, 16(1):45–74, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.118183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21d7b94e-2b5b-4936-adf3-1bad913060c1 · outbound

This paper cites an unresolved cited work.

Sampling Preferences Yields Simple Trustworthiness Scores Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:10:03.567588Z

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 6a96b76c-d32c-4e71-8a1e-8b88cf5224d4 · outbound

This paper cites MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning.

Sampling Preferences Yields Simple Trustworthiness Scores MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.289301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a97b16b3-6bb3-4e28-bf3f-b22c0f228596 · outbound

This paper cites an unresolved cited work.

Sampling Preferences Yields Simple Trustworthiness Scores Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:10:03.432512Z

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 4969d747-c468-4db5-ba6f-827c07aef6fb · outbound

This paper cites Weighted sum model with partial preference information: Application to multi-objective optimization.European Journal of Operational Research, 260(2):665–679, 2017.

Sampling Preferences Yields Simple Trustworthiness Scores Weighted sum model with partial preference information: Application to multi-objective optimization.European Journal of Operational Research, 260(2):665–679, 2017

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:03.274982Z

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 7065bb9b-dab6-462c-8177-f0b175e334f2 · outbound

This paper cites Continuous multivariate distributions, Volume 1: Models and applica- tions, volume 334.

Sampling Preferences Yields Simple Trustworthiness Scores Continuous multivariate distributions, Volume 1: Models and applica- tions, volume 334

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:03.108698Z

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 e32b590a-9c0c-4c4c-bfd6-a6aeb567050b · outbound

This paper cites It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization.

Sampling Preferences Yields Simple Trustworthiness Scores It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.584898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6f2733-f7b9-4925-bcdf-fadc90644138 · outbound

This paper cites Trustworthy ai: From principles to practices.ACM Computing Surveys, 55(9):1–46, 2023.

Sampling Preferences Yields Simple Trustworthiness Scores Trustworthy ai: From principles to practices.ACM Computing Surveys, 55(9):1–46, 2023

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.657306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1096277a-8aa6-4600-bc47-529e7f6c55c0 · outbound

This paper cites Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment.

Sampling Preferences Yields Simple Trustworthiness Scores Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:57.754343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514e8791-97dd-4b3f-8f98-e0f0162d31ef · outbound

This paper cites Multi-objective decision making for trustworthy ai.

Sampling Preferences Yields Simple Trustworthiness Scores Multi-objective decision making for trustworthy ai

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.948868Z

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 d0e3d917-94b1-4876-9bc3-4427c1a48967 · outbound

This paper cites The weighted sum method for multi-objective optimization: new insights.Structural and multidisci- plinary optimization, 41:853–862, 2010.

Sampling Preferences Yields Simple Trustworthiness Scores The weighted sum method for multi-objective optimization: new insights.Structural and multidisci- plinary optimization, 41:853–862, 2010

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.797128Z

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 b0acc849-d8cd-4806-9053-489e09f77f24 · outbound

This paper cites Dependable intrusion detection system for iot: A deep transfer learning based approach.IEEE Transactions on Industrial Informatics, 19(1):1006–1017, 2022.

Sampling Preferences Yields Simple Trustworthiness Scores Dependable intrusion detection system for iot: A deep transfer learning based approach.IEEE Transactions on Industrial Informatics, 19(1):1006–1017, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.671988Z

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 23e12c89-35ee-4773-bbfd-f016ad928910 · outbound

This paper cites From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, 2023.

Sampling Preferences Yields Simple Trustworthiness Scores From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai.ACM Computing Surveys, 55(13s):1–42, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.508554Z

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 117d8fb9-e1e9-47c2-8ed6-f9d9c8094d6b · outbound

This paper cites Fairness optimisation with multi-objective swarms for explain- able classifiers on data streams.Complex & Intelligent Systems, pages 1–14, 2024.

Sampling Preferences Yields Simple Trustworthiness Scores Fairness optimisation with multi-objective swarms for explain- able classifiers on data streams.Complex & Intelligent Systems, pages 1–14, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.312806Z

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 5d2296b0-d269-4f5e-a070-00204827a518 · outbound

This paper cites Artificial intelligence risk management framework (ai rmf 1.0), 2023-01-26 05:01:00 2023.

Sampling Preferences Yields Simple Trustworthiness Scores Artificial intelligence risk management framework (ai rmf 1.0), 2023-01-26 05:01:00 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.170731Z

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 e0136162-3521-4dc8-95df-b6e3d35222a4 · outbound

This paper cites Robustness may be at odds with accuracy.

Sampling Preferences Yields Simple Trustworthiness Scores Robustness may be at odds with accuracy

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:02.022538Z

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 5a9679b4-1e71-411c-baad-7fa81b720d2e · outbound

This paper cites Explainable Artificial Intelligence: a Systematic Review.

Sampling Preferences Yields Simple Trustworthiness Scores Explainable Artificial Intelligence: a Systematic Review

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:59.721873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71763391-d6bf-4948-ac25-eaf6a4444fce · outbound

This paper cites Decodingtrust: A comprehensive assessment of trustworthiness in gpt models.

Sampling Preferences Yields Simple Trustworthiness Scores Decodingtrust: A comprehensive assessment of trustworthiness in gpt models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:59.851964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2304e760-74d8-404b-9824-f5c501ffa202 · outbound

This paper cites Arithmetic control of LLMs for diverse user preferences: Directional preference alignment with multi- objective rewards.

Sampling Preferences Yields Simple Trustworthiness Scores Arithmetic control of LLMs for diverse user preferences: Directional preference alignment with multi- objective rewards

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:01.807194Z

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 3288c91c-2f3f-477d-97e1-d41570581ab0 · outbound

This paper cites To be robust or to be fair: Towards fairness in adversarial training.

Sampling Preferences Yields Simple Trustworthiness Scores To be robust or to be fair: Towards fairness in adversarial training

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:01.542301Z

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.