Paper Citation Record · LEDGER

ViSTa Dataset: Do vision-language models understand sequential tasks?

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2411.13211.

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

pith.paper-citation-record.v1
2411.13211 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:45:47.406893Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

45 of 45 outbound references displayed

verified exact0
verified fuzzy13
unresolved32
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fcc889a0-0f25-4e85-9f8f-3a7b480f2842 · outbound

This paper cites Mas- tering the game of go with deep neural networks and tree search.

ViSTa Dataset: Do vision-language models understand sequential tasks? Mas- tering the game of go with deep neural networks and tree search

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T16:45:47.161355Z

Source-reported events for the cited work

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Observation 534fdd08-0d02-4d4d-bc0a-d5ea9d2d8de7 · outbound

This paper cites Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes.

ViSTa Dataset: Do vision-language models understand sequential tasks? Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 2

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Observation e7042915-8ccb-4735-8104-11b6b7592550 · outbound

This paper cites Specification gaming: the flip side of ai ingenuity.

ViSTa Dataset: Do vision-language models understand sequential tasks? Specification gaming: the flip side of ai ingenuity

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.155246Z

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Observation e93a6f76-1f30-4f21-868b-f11ca058f76c · outbound

This paper cites Defining and characterizing reward gaming.

ViSTa Dataset: Do vision-language models understand sequential tasks? Defining and characterizing reward gaming

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.138168Z

Source-reported events for the cited work

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Observation ae624301-8431-4ccc-9a5a-72ae93f62973 · outbound

This paper cites On the importance of hyperparameter optimization for model-based reinforcement learning.

ViSTa Dataset: Do vision-language models understand sequential tasks? On the importance of hyperparameter optimization for model-based reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.120939Z

Source-reported events for the cited work

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Observation ee53c557-2a5a-4e63-a00a-7aada7a3959c · outbound

This paper cites Variational inverse control with events: A general framework for data-driven reward definition.

ViSTa Dataset: Do vision-language models understand sequential tasks? Variational inverse control with events: A general framework for data-driven reward definition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.101686Z

Source-reported events for the cited work

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Observation b13ec86d-3df7-422f-bb34-42b565521e38 · outbound

This paper cites Deep reinforcement learning from human preferences.

ViSTa Dataset: Do vision-language models understand sequential tasks? Deep reinforcement learning from human preferences

Reference 7

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Observation 1c616136-d84a-46b6-ad8c-e02bdf1af33c · outbound

This paper cites Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning.

ViSTa Dataset: Do vision-language models understand sequential tasks? Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

Reference 8

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Observation ae1fa5bf-97f7-4b05-8bdb-1c3086adaa46 · outbound

This paper cites RoboCLIP: One Demonstration is Enough to Learn Robot Policies.

ViSTa Dataset: Do vision-language models understand sequential tasks? RoboCLIP: One Demonstration is Enough to Learn Robot Policies

Reference 9

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Source-reported events for the cited work

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Observation aa9aa0c6-3229-493d-a173-15357e8c13cb · outbound

This paper cites Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors.

ViSTa Dataset: Do vision-language models understand sequential tasks? Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T16:45:47.222909Z

Source-reported events for the cited work

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Observation a0169577-c9f9-4b4f-aace-dcf08d21b2bf · outbound

This paper cites Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos.

ViSTa Dataset: Do vision-language models understand sequential tasks? Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T16:45:47.228810Z

Source-reported events for the cited work

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Observation 6718bc60-6efa-4b08-99ee-8934da57ee5c · outbound

This paper cites The unsur- prising effectiveness of pre-trained vision models for control.

ViSTa Dataset: Do vision-language models understand sequential tasks? The unsur- prising effectiveness of pre-trained vision models for control

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.070961Z

Source-reported events for the cited work

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Observation e8819983-5ecb-496f-bbce-4df5d85468f5 · outbound

This paper cites Reward Design with Language Models.

ViSTa Dataset: Do vision-language models understand sequential tasks? Reward Design with Language Models

Reference 13

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Observation ed1eaabf-97a8-4d83-ba2c-94c173cec3a2 · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

ViSTa Dataset: Do vision-language models understand sequential tasks? RewardBench: Evaluating Reward Models for Language Modeling

Reference 14

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Observation 2d89699d-a082-48fc-9303-2e05cfd4779b · outbound

This paper cites Reward learning from narrated demonstrations.

ViSTa Dataset: Do vision-language models understand sequential tasks? Reward learning from narrated demonstrations

Reference 15

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Observation 710e80fd-f955-423f-8ebe-7018aaa971bc · outbound

This paper cites Zero-shot reward specification via grounded natural language.

ViSTa Dataset: Do vision-language models understand sequential tasks? Zero-shot reward specification via grounded natural language

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.035370Z

Source-reported events for the cited work

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Observation cc57d9c5-0e98-4c8f-95e1-cf4e4564c3f0 · outbound

This paper cites RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback.

ViSTa Dataset: Do vision-language models understand sequential tasks? RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

Reference 17

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Observation e93e662b-5681-4cfe-b64a-ed702c29e75b · outbound

This paper cites PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain.

ViSTa Dataset: Do vision-language models understand sequential tasks? PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain

Reference 18

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Observation bd236a1c-d166-4044-b0fc-81fa4e9e125e · outbound

This paper cites Paxion: Patching Action Knowledge in Video-Language Foundation Models,.

ViSTa Dataset: Do vision-language models understand sequential tasks? Paxion: Patching Action Knowledge in Video-Language Foundation Models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.018311Z

Source-reported events for the cited work

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Observation 12d3fae1-9b50-49d4-b082-a0c025bed755 · outbound

This paper cites BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation.

ViSTa Dataset: Do vision-language models understand sequential tasks? BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation

Reference 20

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Observation 5874afd9-ede9-4663-bd7a-ac3b4119de37 · outbound

This paper cites VirtualHome: Simulating Household Activities via Programs.

ViSTa Dataset: Do vision-language models understand sequential tasks? VirtualHome: Simulating Household Activities via Programs

Reference 21

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Observation 71db451c-40d2-45f9-8b25-7e66452edb7b · outbound

This paper cites Habitat: A Platform for Embodied AI Research, 2019.

ViSTa Dataset: Do vision-language models understand sequential tasks? Habitat: A Platform for Embodied AI Research, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:48.002322Z

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Observation bb1bd04c-aecc-4cc9-89e0-8037a97f1019 · outbound

This paper cites Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots.

ViSTa Dataset: Do vision-language models understand sequential tasks? Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots

Reference 23

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Observation 2279356c-1ac0-415b-9d63-798035e8fa5d · outbound

This paper cites ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks.

ViSTa Dataset: Do vision-language models understand sequential tasks? ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Reference 24

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Observation f0770c95-1ffe-4f66-bf58-fe6bfe25b17b · outbound

This paper cites A Short Note on the Kinetics-700 Human Action Dataset.

ViSTa Dataset: Do vision-language models understand sequential tasks? A Short Note on the Kinetics-700 Human Action Dataset

Reference 25

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Observation c5d0ccb6-c9d5-4759-9588-a02d4e5e6e0b · outbound

This paper cites BEDD: the minerl BASALT evaluation and demonstrations dataset for training and benchmarking agents that solve fuzzy tasks.

ViSTa Dataset: Do vision-language models understand sequential tasks? BEDD: the minerl BASALT evaluation and demonstrations dataset for training and benchmarking agents that solve fuzzy tasks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:47.986056Z

Source-reported events for the cited work

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Observation 7826661d-af97-436e-821c-bab9a859cffa · outbound

This paper cites Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks.

ViSTa Dataset: Do vision-language models understand sequential tasks? Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks

Reference 27

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Observation d2683055-6a7f-4111-9dd9-1f7db5471581 · outbound

This paper cites Learning transferable visual models from natural language supervision.

ViSTa Dataset: Do vision-language models understand sequential tasks? Learning transferable visual models from natural language supervision

Reference 28

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Observation e028cb0c-5dc6-43e0-a6a0-b2a019949f23 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

ViSTa Dataset: Do vision-language models understand sequential tasks? Reproducible scaling laws for contrastive language-image learning

Reference 29

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Observation 7cb16373-0c7a-453d-b7b7-d9eb3609c871 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

ViSTa Dataset: Do vision-language models understand sequential tasks? LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 30

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This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

ViSTa Dataset: Do vision-language models understand sequential tasks? InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

Reference 31

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This paper cites GPT-4o System Card.

ViSTa Dataset: Do vision-language models understand sequential tasks? GPT-4o System Card

Reference 32

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

Reference 34

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Observation 0d463477-81d5-4b03-80d1-0e4d632067d4 · outbound

This paper cites likely does not describe the video.

ViSTa Dataset: Do vision-language models understand sequential tasks? likely does not describe the video

Reference 35

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This paper cites Player holds an oak fence block in their hands.

ViSTa Dataset: Do vision-language models understand sequential tasks? Player holds an oak fence block in their hands

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:47.924572Z

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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ViSTa Dataset: Do vision-language models understand sequential tasks? Unresolved cited work

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Observation 89dd84ec-b538-41e0-aba9-af172bd47aad · outbound

This paper cites Figure 10: The prompt used to obtain frame-by-frame descriptions for Minecraft videos.

ViSTa Dataset: Do vision-language models understand sequential tasks? Figure 10: The prompt used to obtain frame-by-frame descriptions for Minecraft videos

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:45:47.776841Z

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ViSTa Dataset: Do vision-language models understand sequential tasks? Paxion: Patching Action Knowledge in Video-Language Foundation Models

Reference 2023

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Pith citing papers

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