Paper Citation Record · LEDGER

GMem: A Modular Approach for Ultra-Efficient Generative Models

As of 12 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2412.08781.

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

pith.paper-citation-record.v1
2412.08781 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:42:11.741714Z

measured 39 of 39 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 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

39 of 39 outbound references displayed

verified exact0
verified fuzzy8
unresolved30
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68a9001f-a48b-4265-b137-c5594497f844 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

GMem: A Modular Approach for Ultra-Efficient Generative Models Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.586101Z

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Observation f94e3083-e903-4c58-ae47-bc0b9f15f388 · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

GMem: A Modular Approach for Ultra-Efficient Generative Models Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.600091Z

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Observation fbc05112-b163-492b-8c5f-7727d957c114 · outbound

This paper cites Deconstructing Denoising Diffusion Models for Self-Supervised Learning.

GMem: A Modular Approach for Ultra-Efficient Generative Models Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.604491Z

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Observation c0ed2279-842f-40ff-b610-fa80df2c7656 · outbound

This paper cites We then create nine interpolated snippets ˆsi by linearly interpolating between s1 and s2 with interpolation coefficients αi ranging from 0.1 to 0.9 in increments of 0.1.

GMem: A Modular Approach for Ultra-Efficient Generative Models We then create nine interpolated snippets ˆsi by linearly interpolating between s1 and s2 with interpolation coefficients αi ranging from 0.1 to 0.9 in increments of 0.1

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.104541Z

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Observation 351cca57-84a6-4f8e-ba5e-43ece73c92bb · outbound

This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

GMem: A Modular Approach for Ultra-Efficient Generative Models Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.622532Z

Source-reported events for the cited work

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Observation 3c7cae1c-7e4f-4211-b7a0-fdfaa7aab3ea · outbound

This paper cites Auto-Encoding Variational Bayes.

GMem: A Modular Approach for Ultra-Efficient Generative Models Auto-Encoding Variational Bayes

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.626567Z

Source-reported events for the cited work

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Observation 9817f8ec-460c-4afb-8a60-9a47f2792294 · outbound

This paper cites Memory-Driven Text-to-Image Generation.

GMem: A Modular Approach for Ultra-Efficient Generative Models Memory-Driven Text-to-Image Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.634344Z

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Observation 82f9e333-39ae-4757-a4b5-f307e8513fbd · outbound

This paper cites Return of Unconditional Generation: A Self-supervised Representation Generation Method.

GMem: A Modular Approach for Ultra-Efficient Generative Models Return of Unconditional Generation: A Self-supervised Representation Generation Method

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.638327Z

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Observation 424bc95d-a33f-4228-bd61-b32652ab58d7 · outbound

This paper cites DREAM: Efficient Dataset Distillation by Representative Matching.

GMem: A Modular Approach for Ultra-Efficient Generative Models DREAM: Efficient Dataset Distillation by Representative Matching

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.642367Z

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Observation 2dc4e993-47a4-46e4-adc2-b289a3760cbe · outbound

This paper cites Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models.

GMem: A Modular Approach for Ultra-Efficient Generative Models Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.646273Z

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Observation 9f34fcf2-1805-4abd-973f-b7e2911601c4 · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

GMem: A Modular Approach for Ultra-Efficient Generative Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.650291Z

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Observation 4bc0332f-e2cc-488e-bd5f-cb2fd167e638 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

GMem: A Modular Approach for Ultra-Efficient Generative Models DINOv2: Learning Robust Visual Features without Supervision

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.654253Z

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Observation 7ec2fcaf-25ff-40df-b9e2-69143dd9cafd · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

GMem: A Modular Approach for Ultra-Efficient Generative Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.658200Z

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Observation 15e957d9-6c6b-42c0-b9b2-2900fee412aa · outbound

This paper cites Stylegan-xl: Scaling stylegan to large diverse datasets.

GMem: A Modular Approach for Ultra-Efficient Generative Models Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.662781Z

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Observation e9299f34-8726-47a2-8f05-3af56e178862 · outbound

This paper cites KNN-Diffusion: Image Generation via Large-Scale Retrieval.

GMem: A Modular Approach for Ultra-Efficient Generative Models KNN-Diffusion: Image Generation via Large-Scale Retrieval

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.666796Z

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Observation 64d75610-23ee-4997-83b4-6b3c9a7fb433 · outbound

This paper cites Denoising Diffusion Implicit Models.

GMem: A Modular Approach for Ultra-Efficient Generative Models Denoising Diffusion Implicit Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.670836Z

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Observation 9560ea57-cd97-41eb-8019-2acc48c09a54 · outbound

This paper cites Diffusion-GAN: Training GANs with Diffusion.

GMem: A Modular Approach for Ultra-Efficient Generative Models Diffusion-GAN: Training GANs with Diffusion

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.679076Z

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Observation bdb12dd1-9b82-4b5c-91b2-94341851107c · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

GMem: A Modular Approach for Ultra-Efficient Generative Models Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.683251Z

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Observation 9fb6f089-c296-439b-be83-7d342bd63acb · outbound

This paper cites Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models.

GMem: A Modular Approach for Ultra-Efficient Generative Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.687126Z

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Observation d846b3f1-ff4a-4fa0-b20d-e97a3a731597 · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

GMem: A Modular Approach for Ultra-Efficient Generative Models Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.695214Z

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Observation ec87665a-d71f-4a0f-b440-29ee383166c8 · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

GMem: A Modular Approach for Ultra-Efficient Generative Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.699689Z

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Observation cd3e058f-444e-4aaf-9967-d5c6ddf37b30 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

GMem: A Modular Approach for Ultra-Efficient Generative Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.703397Z

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Observation a6409ebc-c5fb-4366-a42f-922a59017d17 · outbound

This paper cites Therefore, we adopted Zero mask for all major experiments.

GMem: A Modular Approach for Ultra-Efficient Generative Models Therefore, we adopted Zero mask for all major experiments

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.168198Z

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

Observation 82e37b67-6471-467a-a91d-b268f7ff0829 · outbound

This paper cites an unresolved cited work.

GMem: A Modular Approach for Ultra-Efficient Generative Models Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-11T17:42:12.145794Z

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

Observation e739e60a-ca9a-4333-8849-dc0fee14f16d · outbound

This paper cites In contrast, for pixel space generation, we directly use the raw pixel data as input.

GMem: A Modular Approach for Ultra-Efficient Generative Models In contrast, for pixel space generation, we directly use the raw pixel data as input

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.133385Z

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

Observation 4e8fde72-1f55-4c89-99f3-6e6c92c12553 · outbound

This paper cites Dinov2-B offers superior performance, making it an ideal choice for facilitating the efficient training for constructing the memory bank.

GMem: A Modular Approach for Ultra-Efficient Generative Models Dinov2-B offers superior performance, making it an ideal choice for facilitating the efficient training for constructing the memory bank

Reference 34

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

Observation 5711d9b3-5414-48b9-aa06-c3305c0dca79 · outbound

This paper cites In the DDPM framework introduced by Ho et al.

GMem: A Modular Approach for Ultra-Efficient Generative Models In the DDPM framework introduced by Ho et al

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.090578Z

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

Observation 77b79757-6e94-4f5f-b9e2-6160b9fed416 · outbound

This paper cites The process is formulated as: xt = αtx0 + σtε, with α0 = σ1 = 1, α 1 = σ0 = 0, where αt decreases and σt increases as functions of t.

GMem: A Modular Approach for Ultra-Efficient Generative Models The process is formulated as: xt = αtx0 + σtε, with α0 = σ1 = 1, α 1 = σ0 = 0, where αt decreases and σt increases as functions of t

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.077307Z

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

Observation 2c88bb8f-e50a-474a-bfae-db4b5861e4e7 · outbound

This paper cites Consequently, simple interpolants can be utilized by defining αt and σt as straightforward functions during training and inference.

GMem: A Modular Approach for Ultra-Efficient Generative Models Consequently, simple interpolants can be utilized by defining αt and σt as straightforward functions during training and inference

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.065070Z

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

Observation d2cc78b7-119c-4106-8fa9-0160accd9a00 · outbound

This paper cites ↓ means lower is better and all results reported are without classifier-free guidance.

GMem: A Modular Approach for Ultra-Efficient Generative Models ↓ means lower is better and all results reported are without classifier-free guidance

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.157035Z

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

Observation c85bd474-0405-4501-a7c5-31f031722956 · outbound

This paper cites Generation involves solving the corresponding reverse SDE, starting from random Gaussian noise xT ∼ N(0, I).

GMem: A Modular Approach for Ultra-Efficient Generative Models Generation involves solving the corresponding reverse SDE, starting from random Gaussian noise xT ∼ N(0, I)

Reference 1000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:42:12.052986Z

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

Observation 68f4d551-5166-4fae-82c8-418e8a54201a · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

GMem: A Modular Approach for Ultra-Efficient Generative Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2009

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Observation be42d545-5c95-4574-b5f2-c5b815853af4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

GMem: A Modular Approach for Ultra-Efficient Generative Models Adam: A Method for Stochastic Optimization

Reference 2013

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Observation 9511fb33-5477-4fab-811a-116851754da7 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

GMem: A Modular Approach for Ultra-Efficient Generative Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.675074Z

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Observation 09457cac-858b-451f-8cbd-94ceddc6de7f · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

GMem: A Modular Approach for Ultra-Efficient Generative Models Imagenet: A large-scale hierarchical image database

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.609124Z

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Observation decef20b-f63e-422d-9502-9e1ea287b62a · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

GMem: A Modular Approach for Ultra-Efficient Generative Models Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.595660Z

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Observation ccc6baac-8a20-460d-a6c1-da7af1f46a42 · outbound

This paper cites Semi-Parametric Neural Image Synthesis.

GMem: A Modular Approach for Ultra-Efficient Generative Models Semi-Parametric Neural Image Synthesis

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.591242Z

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Observation f62512a5-5460-461b-9c10-735f3b426457 · outbound

This paper cites Diffusion Models and Representation Learning: A Survey.

GMem: A Modular Approach for Ultra-Efficient Generative Models Diffusion Models and Representation Learning: A Survey

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.618595Z

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Observation 2bd14ada-9722-47bf-a8ab-1dd0402a024b · outbound

This paper cites FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification.

GMem: A Modular Approach for Ultra-Efficient Generative Models FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-11T17:42:11.691106Z

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Pith citing papers

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