Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T05:44:33.099337Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 100 of 178 outbound references and 0 inbound Pith citation observations for arXiv:2607.02972.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T05:44:33.099337Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 178 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 472c6724-4233-4e58-8c1e-0e4866eaeef3 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Are Language Models Sensitive to Morally Irrelevant Distractors?
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Proceedings of the AAAI Conference on Artificial Intelligence , volume =
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Neuro-Symbolic Models of Human Moral Judgment:
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Planning for New Threats to Online Research Data Validity: The Issue of Computer-Using Agents , issn =
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Applied Economic Perspectives and Policy , author =
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Behavior Research Methods , author =
Reference 19
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs and Lazar, Seth , date =
Reference 21
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Observation 0b3c60fc-7283-43e8-a2cb-ee8769d3e2ac · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Essays on moral development: Vol
Reference 22
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Reference 23
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Survey-taking
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Recognising, Anticipating, and Mitigating
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Reference 26
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Reference 27
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Reference 28
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
Reference 30
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Reference 31
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Reference 33
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Observation 6428c5a6-68c8-48ee-93c1-84e9cacf270a · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Scientific Reports , publisher =
Reference 34
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Moral Foundations of Large Language Models , url =
Reference 36
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Differences in the Moral Foundations of Large Language Models , url =
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , publisher =
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Observation 337d4bfa-d892-43ce-a0f9-23cb728047f4 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Aligning AI With Shared Human Values
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Is It Good to Cooperate?: Testing the Theory of Morality-as-Cooperation in 60 Societies , volume =
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Measuring and Improving Consistency in Pretrained Language Models
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Reference 93
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A Scalable Approach to Evaluating Moral Sensitivity in LLMs Towards Measuring the Representation of Subjective Global Opinions in Language Models
Reference 94
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Observation 92dbbc41-48e2-4f2e-a54f-310df839bbb1 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Steering Language Models with Weight Arithmetic , publisher =
Reference 95
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Observation 8839097b-0031-4c03-b138-6450072eb42b · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Artificial Intelligence, Values, and Alignment , volume=
Reference 96
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Observation 19fe2151-bb84-4007-92b8-b1cf5b89fdce · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs A matter of principle? AI alignment as the fair treatment of claims , volume=
Reference 97
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Observation b3308365-28e4-4d31-8cde-7ce2408f14d0 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Scaling Synthetic Data Creation with 1,000,000,000 Personas
Reference 98
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Observation 18da3e37-f0bd-495f-b826-7288dbc0102c · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
Reference 99
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Observation 2027fbe2-968d-44fc-a947-bb1138174ce4 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Noah’s Substack , author =
Reference 100
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Observation 37234499-3b82-4983-b18d-185326f6cb64 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs Would you buy a used car from this artificial agent?
Reference 101
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Observation 420d7980-25a7-42ef-b6eb-e19d863721c4 · outbound
A Scalable Approach to Evaluating Moral Sensitivity in LLMs 2510.05465 , archivePrefix =
Reference 102
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No inbound Pith citation observations are available.