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Paper Citation Record · LEDGER

MOSLIM:Align with diverse preferences in prompts through reward classification

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2505.20336.

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

pith.paper-citation-record.v1
2505.20336 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:31:05.857015Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T20:33:46.647842Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 06d0a058-c3cb-4990-abfd-c649518d201e · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

MOSLIM:Align with diverse preferences in prompts through reward classification Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 1

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unresolved
no resolver link, observed 2026-08-07T14:31:04.575775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:04.575775Z digest=sha256:701c738fb0ee9f61717e83dc6d17695e298d6ea0033bf6ca7823201c6950d65b

Observation 82af8d40-cac6-48eb-9022-41cfdd1e25c5 · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

MOSLIM:Align with diverse preferences in prompts through reward classification UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 3

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unresolved
no resolver link, observed 2026-08-07T14:31:04.758999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:04.758999Z digest=sha256:88173a85389c5a97770f29619b0c764a3e6d2681a4b836ddb95b4f69ae7f926c

Observation 70ae6733-b1b2-4dfb-88e2-ee7e74a226c5 · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

MOSLIM:Align with diverse preferences in prompts through reward classification Direct Language Model Alignment from Online AI Feedback

Reference 4

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unresolved
no resolver link, observed 2026-08-07T14:31:04.853691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:04.853691Z digest=sha256:3f3fabf99e4c5f9c5239a2855dc44a3245d8b7a785209289e4ab3cced1b62bde

Observation 10a44876-4afa-4080-af6c-b2b9eea30c26 · outbound

This paper cites Aligning to Thousands of Preferences via System Message Generalization.

MOSLIM:Align with diverse preferences in prompts through reward classification Aligning to Thousands of Preferences via System Message Generalization

Reference 5

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unresolved
no resolver link, observed 2026-08-07T14:31:04.948809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:04.948809Z digest=sha256:bff83e14bc7557f158b6a557573d157de04813c562df460a3f3ed05d98d05f4a

Observation 62f35081-5fa3-4a89-8e4b-d5af209df259 · outbound

This paper cites Aligning Crowd Feedback via Distributional Preference Reward Modeling.

MOSLIM:Align with diverse preferences in prompts through reward classification Aligning Crowd Feedback via Distributional Preference Reward Modeling

Reference 6

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metadata mismatch
local_arxiv, observed 2026-08-07T14:31:06.429572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:31:05.044777Z digest=sha256:f6eb50f35e42b2446bfdce25652efe9685436ce3ce4ad3d07bfdc7ebbdc6cd69

Observation 44297587-a9cb-4257-854d-c403c37fff08 · outbound

This paper cites Training language models to follow instructions with human feedback.

MOSLIM:Align with diverse preferences in prompts through reward classification Training language models to follow instructions with human feedback

Reference 7

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unresolved
no resolver link, observed 2026-08-07T14:31:05.137376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.137376Z digest=sha256:29ae3c592296e7b82fc17017294a06eeb6f3a4617b71b0f4ffd561c5f2d1ac46

Observation bcc5c1fd-e4c7-47db-96c4-153fe6678fa9 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

MOSLIM:Align with diverse preferences in prompts through reward classification Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 9

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no resolver link, observed 2026-08-07T14:31:05.324933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.324933Z digest=sha256:a64ed191e086da37c9941e70155d26ed72043fe794a6a861013c2ef266e000c3

Observation 3f5dbaf0-6f40-460e-90b1-29500f891c01 · outbound

This paper cites Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition.

MOSLIM:Align with diverse preferences in prompts through reward classification Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Reference 11

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no resolver link, observed 2026-08-07T14:31:05.462525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.462525Z digest=sha256:581024a178a49975696da571557b1e375ba0469b55e74f40a85b6dec23867445

Observation 6b969249-ef59-424f-acf7-a4c47ca8d4b7 · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

MOSLIM:Align with diverse preferences in prompts through reward classification Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 14

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no resolver link, observed 2026-08-07T14:31:05.773320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.773320Z digest=sha256:05e6f8043909367f463c53fac94a4da73273d5812fd9d8bb020ce837671d71ce

Observation b27f801b-3457-4fb5-8e2f-571b173eed9a · outbound

This paper cites In the Table 5 in Ap- pendix B, <preference n > represents a preference intensity of n (1 ≤ n < nmax), where a larger n indicates a higher intensity.

MOSLIM:Align with diverse preferences in prompts through reward classification In the Table 5 in Ap- pendix B, <preference n > represents a preference intensity of n (1 ≤ n < nmax), where a larger n indicates a higher intensity

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T14:31:06.696901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:31:05.857015Z digest=sha256:19b21e991170a6f3b21bacd631f7d59d6a39965b708d8a75ffcf60787a8922b6

Observation 6ce1a30f-7a2f-4ed8-8f29-b91fe03a5eff · outbound

This paper cites Learning to summarize from human feedback.

MOSLIM:Align with diverse preferences in prompts through reward classification Learning to summarize from human feedback

Reference 2017

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no resolver link, observed 2026-08-07T14:31:05.558867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.558867Z digest=sha256:266556b44a95822c25e547bc5a9793fdfc0c5e7170b06d88f90fcd707471ee81

Observation 7323d348-b0fc-4847-9203-2a850d839a04 · outbound

This paper cites Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts.

MOSLIM:Align with diverse preferences in prompts through reward classification Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts

Reference 2020

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unresolved
no resolver link, observed 2026-08-07T14:31:05.677821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.677821Z digest=sha256:e7c9b3d78ecd8eb6cadcf82091bd4302d3fe7714af6398749e410b43a50f9fb8

Observation 310957cd-3dfd-4981-9928-d4c190692c08 · outbound

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

MOSLIM:Align with diverse preferences in prompts through reward classification RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation

Reference 2022

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unresolved
no resolver link, observed 2026-08-07T14:31:05.229990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:05.229990Z digest=sha256:b76f867890d28ed35c8b8ee0c3a5c2ace349ccc52c5318ce8fc6d2b6902e284f

Observation 1ee2d541-a903-4e3b-a6b8-2481a81d7726 · outbound

This paper cites Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.

MOSLIM:Align with diverse preferences in prompts through reward classification Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

Reference 2023

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metadata mismatch
local_arxiv, observed 2026-08-07T14:31:06.167627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:31:05.386852Z digest=sha256:116dbcc7cec07b54d1e8a72f8ec8669641d35ed2ba3a320f68b646c2f15f0411

Observation 69706b5c-63c6-4108-9952-3e4aa2897442 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

MOSLIM:Align with diverse preferences in prompts through reward classification Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:04.657355Z digest=sha256:50b97e0daaf68d3e0e5347b00fea7d3806e7d27043d4ffd7e342538f883a9804

Pith citing papers

Observation 2de5b500-11de-4a72-8e35-5b040521bc9c · inbound

One Model for All: Multi-Objective Controllable Language Models cites this paper.

One Model for All: Multi-Objective Controllable Language Models MOSLIM:Align with diverse preferences in prompts through reward classification

Reference 38

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arxiv_id, observed 2026-05-10T20:35:45.814908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T20:33:46.647842Z digest=sha256:f51eee9944496fa354f225dd5c4d1834a9d96e4b76b2e868121242710a376b6c