Paper Citation Record · LEDGER

Understanding the Logic of Direct Preference Alignment through Logic

As of 12 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2412.17696.

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

pith.paper-citation-record.v1
2412.17696 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:24:21.201297Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:40:08.618174Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy10
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0a1f17e-f51c-40d3-8ab6-001ac9e1eba5 · outbound

This paper cites DPO and reference approaches For DPO we see a simi- lar derivation.

Understanding the Logic of Direct Preference Alignment through Logic DPO and reference approaches For DPO we see a simi- lar derivation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.006297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0111fb54-4587-4207-b997-8cb7afef2f24 · outbound

This paper cites Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Understanding the Logic of Direct Preference Alignment through Logic Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.962220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf35de68-9326-4805-99dc-8c8d70e18ea5 · outbound

This paper cites Prompting is programming: A query language for large language models.

Understanding the Logic of Direct Preference Alignment through Logic Prompting is programming: A query language for large language models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.584053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6ad0fa82-4bd7-4133-908a-cde191a52c5e · outbound

This paper cites However, the semantics of the resulting formulas are less transparent and often hidden in the weights.

Understanding the Logic of Direct Preference Alignment through Logic However, the semantics of the resulting formulas are less transparent and often hidden in the weights

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.269988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2d8a9011-4bef-4877-8b94-0c90a79650be · outbound

This paper cites an unresolved cited work.

Understanding the Logic of Direct Preference Alignment through Logic Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:24:22.251827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5e46e22c-3252-447b-9901-8dcdbbbb3437 · outbound

This paper cites (2024)), all of which were originally implemented using the logistic log-loss, i.e., each ℓx = − log σ(βρθ).

Understanding the Logic of Direct Preference Alignment through Logic (2024)), all of which were originally implemented using the logistic log-loss, i.e., each ℓx = − log σ(βρθ)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.299157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c66a9439-ef3d-441a-b65a-e1e8c1fb8e9e · outbound

This paper cites Declarative Design of Neural Predicates in Neuro-Symbolic Systems.

Understanding the Logic of Direct Preference Alignment through Logic Declarative Design of Neural Predicates in Neuro-Symbolic Systems

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:24:21.814063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c66f357-b5a3-40e5-9fa6-e302bcf1e5d7 · outbound

This paper cites New Desiderata for Direct Preference Optimization.

Understanding the Logic of Direct Preference Alignment through Logic New Desiderata for Direct Preference Optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.955189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb00165-1fc0-45e3-902b-a15b12efbb6e · outbound

This paper cites an unresolved cited work.

Understanding the Logic of Direct Preference Alignment through Logic Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:24:22.094340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bce74ebd-ad1f-496b-b3b1-031884d5bdb2 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Understanding the Logic of Direct Preference Alignment through Logic DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.967825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eafd6341-2023-40b8-b3e6-2f8de101f702 · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Understanding the Logic of Direct Preference Alignment through Logic What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.973467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abec1c88-e4f0-44a8-b698-8f6fa80a6cc9 · outbound

This paper cites Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive.

Understanding the Logic of Direct Preference Alignment through Logic Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.993315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06962f4c-82bd-4b5b-bec6-339adcad19d5 · outbound

This paper cites Online DPO: Online Direct Preference Optimization with Fast-Slow Chasing.

Understanding the Logic of Direct Preference Alignment through Logic Online DPO: Online Direct Preference Optimization with Fast-Slow Chasing

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.999144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a751ee2-beb9-44da-abe4-69ee1e1a6251 · outbound

This paper cites Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization.

Understanding the Logic of Direct Preference Alignment through Logic Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.004610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f5e94b0-964b-422b-95cc-a1d42b3718ab · outbound

This paper cites Logic of Differentiable Logics: Towards a Uniform Semantics of DL.

Understanding the Logic of Direct Preference Alignment through Logic Logic of Differentiable Logics: Towards a Uniform Semantics of DL

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.018605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e3b6cb-bd84-427e-8659-3fdf57c1791d · outbound

This paper cites On the Independence Assumption in Neurosymbolic Learning.

Understanding the Logic of Direct Preference Alignment through Logic On the Independence Assumption in Neurosymbolic Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.031248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 284ccd89-8ef2-440e-bfc6-ee470834b2dd · outbound

This paper cites Aligning Large Language Models with Human: A Survey.

Understanding the Logic of Direct Preference Alignment through Logic Aligning Large Language Models with Human: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.038122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4901e4e7-b1e8-459a-bf61-8b71d7051ea0 · outbound

This paper cites Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

Understanding the Logic of Direct Preference Alignment through Logic Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.054154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40eda98e-d5ba-4dc1-a581-bec958921811 · outbound

This paper cites Direct Preference Knowledge Distillation for Large Language Models.

Understanding the Logic of Direct Preference Alignment through Logic Direct Preference Knowledge Distillation for Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.060498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 850d7631-4d98-485c-b575-5144a537d229 · outbound

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

Understanding the Logic of Direct Preference Alignment through Logic RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.066176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 288e9c2b-27e3-4ba6-a0e5-93f5ba2565a2 · outbound

This paper cites SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Understanding the Logic of Direct Preference Alignment through Logic SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.081152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07cb74ed-9771-4d1b-9b57-abe0b32ff339 · outbound

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

Understanding the Logic of Direct Preference Alignment through Logic Fine-Tuning Language Models from Human Preferences

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.087673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08ac64bf-a997-4ce5-8d86-31eba4898ee9 · outbound

This paper cites Original losses Further details of the original losses in Table 2, along with other variants such as R-DPO (Park et al., 2024), ODPO (Amini et al.,.

Understanding the Logic of Direct Preference Alignment through Logic Original losses Further details of the original losses in Table 2, along with other variants such as R-DPO (Park et al., 2024), ODPO (Amini et al.,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.561090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79cc82fc-f00b-456e-ae12-ce0e46fa8dbc · outbound

This paper cites an unresolved cited work.

Understanding the Logic of Direct Preference Alignment through Logic Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:24:22.322172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 760683a3-978d-4327-ae60-b26e18cb20ff · outbound

This paper cites an unresolved cited work.

Understanding the Logic of Direct Preference Alignment through Logic Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:24:22.231084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dddd4f5a-9429-49cc-bc0b-28ae4e408065 · outbound

This paper cites Figure 8 shows the Boolean semantics of DPO/SimPO and some novel variants based on the ref- erence form of ORPO (ℓORPO-ref), qfUNL (ℓqfUNL-ref) and l5 (ℓl5-ref).

Understanding the Logic of Direct Preference Alignment through Logic Figure 8 shows the Boolean semantics of DPO/SimPO and some novel variants based on the ref- erence form of ORPO (ℓORPO-ref), qfUNL (ℓqfUNL-ref) and l5 (ℓl5-ref)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.203410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df7b99b7-a451-476c-a0ac-ecfe48709ca6 · outbound

This paper cites Specifically, we focus on losses around the known lossℓCPO, which we treat as a natural baseline to compare against.

Understanding the Logic of Direct Preference Alignment through Logic Specifically, we focus on losses around the known lossℓCPO, which we treat as a natural baseline to compare against

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.180401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2df5a295-1b62-4a41-8878-ca9d34ff8621 · outbound

This paper cites While these experiments are small scale and limited in scope, they are merely meant to suggest possible uses our frame- work and open questions.

Understanding the Logic of Direct Preference Alignment through Logic While these experiments are small scale and limited in scope, they are merely meant to suggest possible uses our frame- work and open questions

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.148719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 277d8756-1265-4cb3-90e4-4bf1a4c3bef1 · outbound

This paper cites To avoid repeating the process of instruction tuning, we started from the trained Qwen model released in the TRL library6.

Understanding the Logic of Direct Preference Alignment through Logic To avoid repeating the process of instruction tuning, we started from the trained Qwen model released in the TRL library6

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.115175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5e9f422a-dc72-4b1f-a4c9-e8100f6224a7 · outbound

This paper cites This suggests that different types of preference data rely on a different semantics of preference, which requires a tuning approach that’s tailored to those differences.

Understanding the Logic of Direct Preference Alignment through Logic This suggests that different types of preference data rely on a different semantics of preference, which requires a tuning approach that’s tailored to those differences

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:24:22.060177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ea43b70-4807-4d59-b405-1be9ba8fffb7 · outbound

This paper cites an unresolved cited work.

Understanding the Logic of Direct Preference Alignment through Logic Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:24:22.035349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7173bdc4-e1e1-48a7-85ae-4dbfe67fac49 · outbound

This paper cites Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.

Understanding the Logic of Direct Preference Alignment through Logic Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Reference 1975

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.075426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 042ff44e-30a7-4c63-b41f-c437002ba784 · outbound

This paper cites Generalized Preference Optimization: A Unified Approach to Offline Alignment.

Understanding the Logic of Direct Preference Alignment through Logic Generalized Preference Optimization: A Unified Approach to Offline Alignment

Reference 1977

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.023974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7aefa3f-ddfd-4ddf-9c9e-581dfea35e95 · outbound

This paper cites Language Model Cascades.

Understanding the Logic of Direct Preference Alignment through Logic Language Model Cascades

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.922116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b9b2a69-ef90-4f56-89ff-cc6b22f408c4 · outbound

This paper cites Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks.

Understanding the Logic of Direct Preference Alignment through Logic Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.012067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5008a5d1-b1f4-4458-9452-ba3077d2eac9 · outbound

This paper cites Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge.

Understanding the Logic of Direct Preference Alignment through Logic Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:24:21.671331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a47bfd00-65e2-47a2-840c-31ccc875dd04 · outbound

This paper cites Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback.

Understanding the Logic of Direct Preference Alignment through Logic Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.987102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ac67d50-851b-4d1c-bfda-2c50bd113595 · outbound

This paper cites Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey.

Understanding the Logic of Direct Preference Alignment through Logic Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:21.046827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6df2947-5ffe-4330-ab02-943c86d44356 · outbound

This paper cites A General Theoretical Paradigm to Understand Learning from Human Preferences.

Understanding the Logic of Direct Preference Alignment through Logic A General Theoretical Paradigm to Understand Learning from Human Preferences

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.892964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 196dde86-df55-4ed1-a5cf-279d0f442537 · outbound

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

Understanding the Logic of Direct Preference Alignment through Logic Direct Language Model Alignment from Online AI Feedback

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.937725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4ac2847-9f4d-432d-84e5-379e57c986ee · outbound

This paper cites Logic Tensor Networks for Semantic Image Interpretation.

Understanding the Logic of Direct Preference Alignment through Logic Logic Tensor Networks for Semantic Image Interpretation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.929299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f56f1ffc-df57-47c5-93e2-aabd9cd11047 · outbound

This paper cites Qwen Technical Report.

Understanding the Logic of Direct Preference Alignment through Logic Qwen Technical Report

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.900125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5efe21c0-8cd9-42a8-9303-558f084c5ea0 · outbound

This paper cites Logically Consistent Language Models via Neuro-Symbolic Integration.

Understanding the Logic of Direct Preference Alignment through Logic Logically Consistent Language Models via Neuro-Symbolic Integration

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T05:24:20.914415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 1256f83f-8b8c-48b4-952c-e40239160c53 · inbound

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering cites this paper.

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering Understanding the Logic of Direct Preference Alignment through Logic

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T17:40:08.618174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.