Paper Citation Record · LEDGER

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2502.02628.

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

pith.paper-citation-record.v1
2502.02628 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:11:21.981193Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy1
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d8dc0c14-1e56-425f-9c13-92f81bc530bc · outbound

This paper cites Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.939061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01e10bc9-eb54-4dab-ac59-af1746945dcc · outbound

This paper cites RLTF: Reinforcement Learning from Unit Test Feedback.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration RLTF: Reinforcement Learning from Unit Test Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.953185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c88e1cd-3f51-4a18-a712-9e3aeba6a0c8 · outbound

This paper cites Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.967541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2982919-b534-4fac-a43d-5dafaa005de9 · outbound

This paper cites Panacea: Pareto Alignment via Preference Adaptation for LLMs.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Panacea: Pareto Alignment via Preference Adaptation for LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.971938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2adec81f-4050-46c2-a2f3-a5e07375ef07 · outbound

This paper cites Beyond one-preference-fits-all alignment: Multi-objective direct preference optimization.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Beyond one-preference-fits-all alignment: Multi-objective direct preference optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:11:22.348870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5fc9c0df-dbbd-4974-b34b-d4c881f0fa24 · outbound

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

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Fine-Tuning Language Models from Human Preferences

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.981193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2da3be00-2b93-4b6a-80b9-b9381a31a13f · outbound

This paper cites Contrastive Preference Learning: Learning from Human Feedback without RL.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Contrastive Preference Learning: Learning from Human Feedback without RL

Reference 1971

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.943767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c78cfb3-14d7-4644-9215-f2fb06d9bb9d · outbound

This paper cites Deep Generative Model for Mechanical System Configuration Design.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Deep Generative Model for Mechanical System Configuration Design

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.929681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e37066ae-2d7d-40b8-8b8e-00bec3edcc6f · outbound

This paper cites Multi-objective Reinforcement learning from AI Feedback.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Multi-objective Reinforcement learning from AI Feedback

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.962879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c7e8b3-628c-45ad-8771-e82d559dc8ef · outbound

This paper cites Proximal Policy Optimization Algorithms.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Proximal Policy Optimization Algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.958433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 950179fc-631a-4357-ab64-4d0c598634aa · outbound

This paper cites Rlsf: Reinforce- ment learning via symbolic feedback.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration Rlsf: Reinforce- ment learning via symbolic feedback

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.948733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6406cbd-e3ed-489f-8a1a-7b072c492817 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration KTO: Model Alignment as Prospect Theoretic Optimization

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T12:11:21.934746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.