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Paper Citation Record · LEDGER

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2506.12350.

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

pith.paper-citation-record.v1
2506.12350 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:04:33.188878Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T12:45:56.736538Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:49:37.712152Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved22
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 5b11d9d1-86ef-42c9-8247-4415029ad08c · outbound

This paper cites Therefore, a preference matching distribution exists.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Therefore, a preference matching distribution exists

Reference 1

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d6efb15f-2b56-4a38-b874-2a29f44bad6b · outbound

This paper cites Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 4

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Observation a701d99e-19f1-4c20-b602-d115cabc1f85 · outbound

This paper cites Mapping Social Choice Theory to RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Mapping Social Choice Theory to RLHF

Reference 5

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Observation 37cfa10b-53f2-4d10-807e-fffd6a485e01 · outbound

This paper cites Policy Optimization in RLHF: The Impact of Out-of-preference Data.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 8

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source=pdf_text observed=2026-08-07T01:04:31.048119Z digest=sha256:4dbd56b41563f82e1bb6a62208c8d1b8235f36fa487436dd981c326f9ac187e5

Observation e1530569-83dd-4e7b-9891-ac9968612789 · outbound

This paper cites Jackpot! Alignment as a Maximal Lottery.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Jackpot! Alignment as a Maximal Lottery

Reference 10

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source=pdf_text observed=2026-08-07T01:04:31.269934Z digest=sha256:ef435c8193ab7a04b3eecede4b447021f87e176690d928c9f44d57adfe661c07

Observation f056459f-c02d-4a49-91fb-13dadd438e7c · outbound

This paper cites AI Alignment and Social Choice: Fundamental Limitations and Policy Implications.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory AI Alignment and Social Choice: Fundamental Limitations and Policy Implications

Reference 11

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Observation af2407b8-2d5e-41c8-a244-5ddb9e26359a · outbound

This paper cites Nash Learning from Human Feedback.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Nash Learning from Human Feedback

Reference 12

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Observation 88259934-527d-46dc-b40b-0a6c150812d2 · outbound

This paper cites RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation

Reference 14

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source=pdf_text observed=2026-08-07T01:04:31.735488Z digest=sha256:83cf1962b0596f51e61d091a890249b39670a41f5db62b412ec4645b5a9637e7

Observation d60b0dca-4b57-46e9-82f0-746fb3ec9b80 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Proximal Policy Optimization Algorithms

Reference 15

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Observation 699e2fc7-0744-4fe0-9bf4-189127a6bc72 · outbound

This paper cites Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF

Reference 17

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Observation 92b86db4-ffbe-4594-be5f-5f1bfa313a7d · outbound

This paper cites Understanding the performance gap between online and offline alignment algorithms.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Understanding the performance gap between online and offline alignment algorithms

Reference 18

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source=pdf_text observed=2026-08-07T01:04:32.093625Z digest=sha256:ff75a697720be719f73fd1176de317a8208819e2f4930c6bd4a69eb696b5cb32

Observation f2767bef-1860-470f-b3ee-a9578e512d76 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Gemini: A Family of Highly Capable Multimodal Models

Reference 19

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Observation e894c17b-8779-4d88-b5c3-7ae66709f369 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory LLaMA: Open and Efficient Foundation Language Models

Reference 20

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Observation c8b38568-b696-4339-b24e-92ad0dda7007 · outbound

This paper cites Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, et al.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, et al

Reference 21

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Observation 5f3c4032-9f7c-458e-964b-d3b0f333e6ba · outbound

This paper cites On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 22

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Observation 7a2cbe65-6812-4d08-8b31-c3678f0730a1 · outbound

This paper cites Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Restoring calibration for aligned large language models: A calibration-aware fine-tuning approach

Reference 23

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Observation 7defe16f-f701-4713-bf0f-45a0706a7933 · outbound

This paper cites Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl- constraint.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Iterative preference learning from human feedback: Bridging theory and practice for rlhf under kl- constraint

Reference 24

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T01:04:32.636118Z digest=sha256:497bbb5d98d948ecb574600f68712909c31f3f343e626481cce889aa9237d9bf

Observation b33ced5a-b656-45c1-8310-f2c9ba8bd1ee · outbound

This paper cites Asymptotics of language model alignment.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Asymptotics of language model alignment

Reference 25

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Observation 3fcb66fe-0fc2-42c9-a6cf-fd8b5072d6a7 · outbound

This paper cites Provable Multi-Party Reinforcement Learning with Diverse Human Feedback.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 26

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Observation c87aacea-1141-4d61-b508-6f6cba18e980 · outbound

This paper cites an unresolved cited work.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Unresolved cited work

Reference 27

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Observation 6ff25476-08d1-443b-8c00-679588b028d9 · outbound

This paper cites The work by Tang et al.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory The work by Tang et al

Reference 29

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source=pdf_text observed=2026-08-07T01:04:33.030370Z digest=sha256:259e3bc022071784fb4f6cf550cb9068fbffa429ae25488e2665fbf5f77df638

Observation 861998dd-ec03-4cda-bb5b-b6c69066a382 · outbound

This paper cites Outside of fine-tuning, model editing has emerged as a complementary strategy to modify LLM behavior across tasks [Jin et al., 2025].

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Outside of fine-tuning, model editing has emerged as a complementary strategy to modify LLM behavior across tasks [Jin et al., 2025]

Reference 30

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Observation 7aa67290-0996-43c2-8634-c003f6d7a217 · outbound

This paper cites [2024], which constrain its robustness relative to reinforcement learning techniques like PPO.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory [2024], which constrain its robustness relative to reinforcement learning techniques like PPO

Reference 31

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Observation cad5854e-c103-4d4f-8d73-ebc7127bc9aa · outbound

This paper cites Learn Your Reference Model for Real Good Alignment.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Learn Your Reference Model for Real Good Alignment

Reference 2006

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Observation 1b4f020e-eabd-446e-9266-f8a868b6dc13 · outbound

This paper cites Learning Linear Utility Functions From Pairwise Comparison Queries.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Learning Linear Utility Functions From Pairwise Comparison Queries

Reference 2009

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local_arxiv, observed 2026-08-07T01:04:33.883031Z

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Observation 80dbad57-95b5-4a4b-a3b3-b85a5bc8976e · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2016

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source=pdf_text observed=2026-08-07T01:04:30.242850Z digest=sha256:1939a7fe0be26ebaef61d7267e4d46af0ae973c38ded1859cdced1fa4091626a

Observation 5f332d57-5d8f-4179-ba1e-cbc868b75180 · outbound

This paper cites Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching

Reference 2017

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local_arxiv, observed 2026-08-07T01:04:33.572321Z

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Observation 0093612e-68fb-4bcc-999e-531b37b21540 · outbound

This paper cites GPT-4 Technical Report.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory GPT-4 Technical Report

Reference 2020

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source=pdf_text observed=2026-08-07T01:04:31.655495Z digest=sha256:f4c3ce55eeaee2d120f34e922d519d94ba4500d6819dad0fb5069454a18f1394

Observation 074dd878-1664-4c9d-88dc-fa80b3d653da · outbound

This paper cites MaxMin-RLHF: Alignment with Diverse Human Preferences.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory MaxMin-RLHF: Alignment with Diverse Human Preferences

Reference 2023

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source=pdf_text observed=2026-08-07T01:04:30.349050Z digest=sha256:e5344592239941473decc370e696e7239fac61ee5d9d1be6bf951abcfff30f94

Observation 19322493-ba98-49d0-bc1a-954f3af1adf5 · outbound

This paper cites Dataset Reset Policy Optimization for RLHF.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Dataset Reset Policy Optimization for RLHF

Reference 2024

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source=pdf_text observed=2026-08-07T01:04:30.492218Z digest=sha256:b0882f463ee374cffc5a550cb3c67cd7a3107998986f4e088800be5705f532dd

Observation 8a24506e-5f96-4b25-8720-621acdde4d0f · outbound

This paper cites Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

Reference 2025

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Pith citing papers

Observation 381d7141-7902-4833-9a51-9c632f59e13b · inbound

Transitivity in Inhomogeneous Random Tournaments cites this paper.

Transitivity in Inhomogeneous Random Tournaments Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

Reference 51

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arxiv_id, observed 2026-07-02T01:06:24.252873Z

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source=pdf_text observed=2026-06-28T12:45:56.736538Z digest=sha256:125be26d195d4012743a6b8e9ab19c128e0e1fbfbf8e2e690e41ce269afc7d7b

Observation c78663c3-2f78-44a3-b449-3e9eaf0d3262 · inbound

AI Alignment From Social Choice Perspectives cites this paper.

AI Alignment From Social Choice Perspectives Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory

Reference 81

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arxiv_id, observed 2026-07-04T06:49:37.714149Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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