Paper Citation Record · LEDGER

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2506.07035.

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

pith.paper-citation-record.v1
2506.07035 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:47:21.920389Z

measured 18 of 18 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 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

18 of 18 outbound references displayed

verified exact1
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e230158e-d9f8-474f-9577-93182e6e412c · outbound

This paper cites Guiding generative pro- tein language models with reinforcement learning.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Guiding generative pro- tein language models with reinforcement learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.392741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e3a6be-1575-4161-b0aa-d1fc6e42461b · outbound

This paper cites Saprot: Protein language modeling with structure-aware vocabulary.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Saprot: Protein language modeling with structure-aware vocabulary

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:23.037812Z

Source-reported events for the cited work

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

Observation 0cce96fb-6f87-44c8-bf1b-47cabfec4727 · outbound

This paper cites Aligning protein generative models with experimental fitness via direct preference optimization.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Aligning protein generative models with experimental fitness via direct preference optimization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:22.737356Z

Source-reported events for the cited work

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

Observation 43348961-396d-47d9-b7bd-1a31a60c7432 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.619389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb4a2363-412a-44da-98fa-a8a3cae7000a · outbound

This paper cites RRHF: Rank Responses to Align Language Models with Human Feedback without tears.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.717895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d06950e3-aa5f-4e06-87ce-3bb124a3dfb5 · outbound

This paper cites OntoProtein: Protein Pretraining With Gene Ontology Embedding.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization OntoProtein: Protein Pretraining With Gene Ontology Embedding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.783284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83c208dc-075b-4955-9cfb-625fdae660a2 · outbound

This paper cites Decoding the molecular language of proteins with evolla.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Decoding the molecular language of proteins with evolla

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:22.526889Z

Source-reported events for the cited work

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

Observation 6d074d5a-0045-44fa-9da1-ca76a3a4ef20 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Fine-Tuning Language Models from Human Preferences

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.920389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7fc07ef-37c0-4ecf-8406-ac0e3d9eb7b0 · outbound

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

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:20.296473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95abaa59-ef2e-489d-afe8-9d151204e6df · outbound

This paper cites Improving alignment of dialogue agents via targeted human judgements.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Improving alignment of dialogue agents via targeted human judgements

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:20.522484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 998e4fe7-f916-4953-b0e4-569012ae9acb · outbound

This paper cites RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:20.398463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980cfff8-9240-45bc-91ff-15cf896c17c1 · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.311124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acd71fa8-76be-4dc6-b46e-5717aec335d6 · outbound

This paper cites and Consortium, U.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization and Consortium, U

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:47:23.363316Z

Source-reported events for the cited work

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

Observation b576ddf5-c069-4db5-b88c-d3126faa2b27 · outbound

This paper cites Proximal Policy Optimization Algorithms.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Proximal Policy Optimization Algorithms

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.248673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44131e8-95ac-4c9b-991f-64554fd390d4 · outbound

This paper cites GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:20.679825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94071797-2ba7-4f93-af11-be65da36226d · outbound

This paper cites Controllable Protein Sequence Generation with LLM Preference Optimization.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Controllable Protein Sequence Generation with LLM Preference Optimization

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:47:22.322892Z

Source-reported events for the cited work

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

Observation cbcaa8fe-2360-4a59-8d31-ee40a228821a · outbound

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

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization Policy Optimization in RLHF: The Impact of Out-of-preference Data

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:20.823057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4301f0d-e67f-4db0-bc1f-60f5b22cdf68 · outbound

This paper cites ProGen: Language Modeling for Protein Generation.

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization ProGen: Language Modeling for Protein Generation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:47:21.080984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.