Paper Citation Record · LEDGER

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation

As of 22 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2501.14856.

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

pith.paper-citation-record.v1
2501.14856 v2

Coverage vector

measured 22 of 22 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T15:02:58.498032Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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verified fuzzy7
unresolved12
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External citation measurements

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Outbound references

Observation a94f2edf-f447-4fd6-8c82-6ac064b3bd89 · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Towards Principled Methods for Training Generative Adversarial Networks

Reference 1

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Observation 6ca16a69-ef83-444c-9fd3-c0a1a35b1a43 · outbound

This paper cites Annealed importance sampling.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Annealed importance sampling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.938667Z

Source-reported events for the cited work

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Observation 0964c205-1aa6-4bfd-92fb-510447c21789 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 8

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Observation f74dc2a3-89d2-4e07-be08-e83b7eacd87e · outbound

This paper cites The average dynamic time warping pose error is then computed as the average DTW score of allτm across all expert trajectories ˆτj with ∥ˆxi − xi∥2 as the cost function.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation The average dynamic time warping pose error is then computed as the average DTW score of allτm across all expert trajectories ˆτj with ∥ˆxi − xi∥2 as the cost function

Reference 19

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Observation 69f823dc-2142-4934-a4f2-87f6a983f2fb · outbound

This paper cites The smoothness of the character’s trajectory is an interesting metric to determine the policy’s ability to perform periodic motions in a controlled manner.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation The smoothness of the character’s trajectory is an interesting metric to determine the policy’s ability to perform periodic motions in a controlled manner

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.834237Z

Source-reported events for the cited work

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Observation ba6b39a0-898c-4314-a1c9-b54797abf345 · outbound

This paper cites The agent aims to reach the goal position at the bottom right (the episode ends when the agent’s position is within some threshold of the goal).

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation The agent aims to reach the goal position at the bottom right (the episode ends when the agent’s position is within some threshold of the goal)

Reference 21

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Observation 53411cb2-ba15-42b3-a672-7f10d3bf9082 · outbound

This paper cites an unresolved cited work.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Unresolved cited work

Reference 22

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Observation c8b2044c-add5-4280-9230-51e1c25d89b1 · outbound

This paper cites The decoder has (1024, 512,.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation The decoder has (1024, 512,

Reference 1024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.876759Z

Source-reported events for the cited work

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Observation 1e7c7844-a4ed-4319-8982-5ecc58fe837f · outbound

This paper cites On Convergence and Stability of GANs.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation On Convergence and Stability of GANs

Reference 1983

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Observation a47aaee4-d247-495c-8d38-13fdd3cc5d8c · outbound

This paper cites When using the environment-supplied task reward, we set wtask = wenergy = 0.5.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation When using the environment-supplied task reward, we set wtask = wenergy = 0.5

Reference 1988

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.891580Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 48d39cc6-fdce-43ce-a7fa-7fcf72b0904c · outbound

This paper cites Diffusion Imitation from Observation.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Diffusion Imitation from Observation

Reference 1989

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:02:58.629076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a1f68be-625f-4626-be4d-933a32674882 · outbound

This paper cites Calm: Conditional adversarial latent models for directable virtual characters.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Calm: Conditional adversarial latent models for directable virtual characters

Reference 1999

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Observation b070fa24-76c0-4a1c-9925-83a20c2ed091 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 2008

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Observation e3c1530a-9682-4475-a186-74c176b8d3cc · outbound

This paper cites Further, we standardise samples before passing them to the network.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Further, we standardise samples before passing them to the network

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.862251Z

Source-reported events for the cited work

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Observation e6028bf2-01c3-4535-b1f7-35408054b2d3 · outbound

This paper cites Lemma A.1.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Lemma A.1

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:02:58.904387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12b1156e-e6df-49b4-a962-3640beaf4b3c · outbound

This paper cites Proximal Policy Optimization Algorithms.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Proximal Policy Optimization Algorithms

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T15:02:58.443048Z

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Observation e8d2855a-f123-43c0-97d7-945ebc9a3517 · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T15:02:58.429362Z

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Observation 2feb0a8d-07d2-4ab8-857e-0c39c9c9e917 · outbound

This paper cites Recent Advances in Imitation Learning from Observation.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Recent Advances in Imitation Learning from Observation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T15:02:58.459904Z

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Observation 914299c6-c907-4963-99bc-4542f8eaa6a5 · outbound

This paper cites How to Train Your Energy-Based Models.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation How to Train Your Energy-Based Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T15:02:58.447117Z

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Observation aff63885-e3b7-43b4-9312-884df26e62b5 · outbound

This paper cites George Cybenko.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation George Cybenko

Reference 2021

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Observation eeca6cae-d144-478e-b09e-369ad1e68b98 · outbound

This paper cites Generative Adversarial Imitation from Observation.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Generative Adversarial Imitation from Observation

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T15:02:58.455513Z

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Observation c46eef19-83dc-4f06-a990-c1c70467b530 · outbound

This paper cites an unresolved cited work.

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-10T15:02:58.917195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.