Paper Citation Record · LEDGER

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2608.09771.

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

pith.paper-citation-record.v1
2608.09771 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:08:46.212987Z

measured 27 of 27 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.

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

27 of 27 outbound references displayed

verified exact0
verified fuzzy2
unresolved25
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83cb2c09-8967-4031-8d35-2babda4c855d · outbound

This paper cites WorldVLA: Towards Autoregressive Action World Model.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation WorldVLA: Towards Autoregressive Action World Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.747732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f3ab69f-682b-4a43-80d0-c2fd6ceefeeb · outbound

This paper cites LaW AM: Latent world action models for efficient dynamics- aware robot policies.arXiv preprint arXiv:2606.15768,.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation LaW AM: Latent world action models for efficient dynamics- aware robot policies.arXiv preprint arXiv:2606.15768,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.833247Z

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Observation 5c1dfd9f-c37e-43d5-b186-f708a588fda5 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation PaLM-E: An Embodied Multimodal Language Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.872154Z

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Observation ca298d05-24c0-40b8-a0c2-9d854b69b1e6 · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.922224Z

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Observation 765db3dd-c201-41cb-8efb-7c4aeb080a39 · outbound

This paper cites NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.931972Z

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Observation c8615701-0ad2-49e9-8dab-290db302e8cb · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation OpenVLA: An Open-Source Vision-Language-Action Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.941219Z

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Observation 57dcca88-e4dd-4d53-96fb-a67d814b6134 · outbound

This paper cites Yann LeCun.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Yann LeCun

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:08:46.772331Z

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 1342aa1b-2879-4608-aa91-bbafe3e25cc2 · outbound

This paper cites Version 0.9.2.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Version 0.9.2

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:08:46.573656Z

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 e0cd1a2d-af60-4903-9568-c07e39e9b554 · outbound

This paper cites V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.959926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71391fcd-e6c3-4f5c-9931-fa5a09f56ace · outbound

This paper cites Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.964568Z

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Observation 8688246d-06b4-4f5a-ad86-e9bd14a83cb1 · outbound

This paper cites GR00T N1: An Open Foundation Model for Generalist Humanoid Robots.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.969263Z

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Observation dcad48c1-4510-48aa-aaae-bafad8447487 · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.974690Z

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Observation aba3db34-5f17-47ba-829b-86c7ebc7ecf2 · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.979381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dc75381-318f-4ed7-9052-9e94ce4e4aa6 · outbound

This paper cites VLA-JEPA: Enhancing vision-language-action model with latent world model.arXiv preprint arXiv:2602.10098,.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation VLA-JEPA: Enhancing vision-language-action model with latent world model.arXiv preprint arXiv:2602.10098,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.010332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56422c58-b632-45e8-8594-8037de63614d · outbound

This paper cites One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.049581Z

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Unavailable: canonical work link unavailable.

Observation fc1388da-6e49-4c77-9948-bb653c50241c · outbound

This paper cites RepWAM: World Action Modeling with Representation Visual-Action Tokenizers.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation RepWAM: World Action Modeling with Representation Visual-Action Tokenizers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.106485Z

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Observation 7ab6af63-4bfa-46c9-81a4-e328aab4b4d8 · outbound

This paper cites World Action Models are Zero-shot Policies.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation World Action Models are Zero-shot Policies

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.164680Z

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Observation b5b6ed8a-023d-4443-84a6-46cc0d14d0ed · outbound

This paper cites Fast-WAM: Do World Action Models Need Test-time Future Imagination?.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.206232Z

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Observation 2e1119bf-dc01-4872-a5e8-a36b1cc4a28c · outbound

This paper cites Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.209237Z

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Observation 9452ac29-a653-4698-a1ca-0e630528246d · outbound

This paper cites Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:46.212987Z

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Unavailable: canonical work link unavailable.

Observation b21644e1-2a10-48e0-ae06-76d9f94b39be · outbound

This paper cites Learning Actionable Representations with Goal-Conditioned Policies.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Learning Actionable Representations with Goal-Conditioned Policies

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.926331Z

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Unavailable: canonical work link unavailable.

Observation 9e742514-6b93-4c6a-a89a-6181e74ea0bb · outbound

This paper cites FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.984225Z

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Observation aa0eef69-d08e-47f6-9380-c7b23718aef7 · outbound

This paper cites Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.647572Z

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Observation 7171984b-7e5d-4db6-9029-3f5aa3d45168 · outbound

This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.630477Z

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Observation fc697d1c-8ced-43c9-9d40-fa180351349b · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.641449Z

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Observation 30b5ebf9-3ef9-442e-8016-e3f4984afd1e · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.636699Z

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Observation ecff5be2-a9fe-43a5-b00d-13475af2ee70 · outbound

This paper cites LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-11T11:08:45.955594Z

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Pith citing papers

No inbound Pith citation observations are available.