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Paper Citation Record · LEDGER

RoboTTT: Context Scaling for Robot Policies

As of 9 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 0 inbound Pith citation observations for arXiv:2607.15275.

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

pith.paper-citation-record.v1
2607.15275 v1

Coverage vector

measured 86 of 86 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:43:25.912505Z

measured 86 of 86 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

86 of 86 outbound references displayed

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External citation measurements

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Outbound references

Observation 671f71f6-7a6d-4ad5-8fe8-cd32eb40b0d2 · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

RoboTTT: Context Scaling for Robot Policies Flamingo: a Visual Language Model for Few-Shot Learning

Reference 1

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source=pdf_text observed=2026-08-01T23:43:15.852168Z digest=sha256:ce344aadb8f88cb823e8a9c83bcd504258fc3db141b17944e33a65746db4b15a

Observation 87d0fd67-00cc-4ea4-b327-7e81e02c9d8a · outbound

This paper cites Evolve-vla: Test-time training from environment feedback for vision-language-action models.arXiv preprint arXiv: 2512.14666, 2025.

RoboTTT: Context Scaling for Robot Policies Evolve-vla: Test-time training from environment feedback for vision-language-action models.arXiv preprint arXiv: 2512.14666, 2025

Reference 2

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source=pdf_text observed=2026-08-01T23:43:15.916115Z digest=sha256:f87bf9e05b4528af6cf89ed4d380ffef1e09747bd035097b273ddcd792519286

Observation 87295c2d-eee8-41da-afb4-1ff0acecf587 · outbound

This paper cites Titans: Learning to Memorize at Test Time.

RoboTTT: Context Scaling for Robot Policies Titans: Learning to Memorize at Test Time

Reference 3

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source=pdf_text observed=2026-08-01T23:43:16.071652Z digest=sha256:5b85f9a155e2ac26c036a764c6f4535693fdf1ddc737916971e9560ea94e03bf

Observation cae708cc-cd1d-47a8-ad70-5bfbc935b8bf · outbound

This paper cites It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization.

RoboTTT: Context Scaling for Robot Policies It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization

Reference 4

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source=pdf_text observed=2026-08-01T23:43:16.177997Z digest=sha256:b8b56685ee76b64897b71c7178bd619ebdcfc33b10fd2b0bcdce210a1fb0e02e

Observation ec4c9175-5ba2-4eaa-bd6b-1b5e11f3166d · outbound

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

RoboTTT: Context Scaling for Robot Policies $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 5

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Observation 1dd3e937-17e2-4767-9b0a-44174ff6f27f · outbound

This paper cites Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, A.

RoboTTT: Context Scaling for Robot Policies Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, A

Reference 6

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source=pdf_text observed=2026-08-01T23:43:16.399292Z digest=sha256:b3d826b725cbeb511759e3c75723db54da33c732119f2ffe3a9c25177077789f

Observation 43b50ebd-8ae5-4338-b6e0-70e44bd1e67f · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

RoboTTT: Context Scaling for Robot Policies RT-1: Robotics Transformer for Real-World Control at Scale

Reference 7

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Observation 5170cf2e-5661-47c1-8aa0-baa5e3cdd8ab · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

RoboTTT: Context Scaling for Robot Policies RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 8

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source=pdf_text observed=2026-08-01T23:43:16.588442Z digest=sha256:9a8a3ca565217eb5b9a2afeda1d85f2f0288f6eb3c7baf6a7b4d64dbe59e4abe

Observation 1ce2541c-c102-420a-9336-fdad6ebe6408 · outbound

This paper cites Language Models are Few-Shot Learners.

RoboTTT: Context Scaling for Robot Policies Language Models are Few-Shot Learners

Reference 9

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source=pdf_text observed=2026-08-01T23:43:16.661783Z digest=sha256:9478dbc1a2df9c366425a90b7408d8216c6d68223fe70ed734fa8dc4b54d1373

Observation ba99f4cc-faef-4fc4-a770-985fb8768e4c · outbound

This paper cites Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion.

RoboTTT: Context Scaling for Robot Policies Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Reference 10

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Observation ffd473b7-1a65-4935-8b6f-613148047e70 · outbound

This paper cites TTT3R: 3D Reconstruction as Test-Time Training.

RoboTTT: Context Scaling for Robot Policies TTT3R: 3D Reconstruction as Test-Time Training

Reference 11

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Observation f24fac04-fb31-4f2e-b5e9-3f8aa9c44a6d · outbound

This paper cites RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies.

RoboTTT: Context Scaling for Robot Policies RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies

Reference 12

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Observation 86ba3f13-1d1f-4d36-8783-7c0627f4972e · outbound

This paper cites One-minute video generation with test-time training.

RoboTTT: Context Scaling for Robot Policies One-minute video generation with test-time training

Reference 13

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source=pdf_text observed=2026-08-01T23:43:17.227957Z digest=sha256:00503edb47c4bfbd9dffafaf4920bc9b4bcf7cd6e12983a6e5b1a56d8b3d86db

Observation 6a7de362-72df-47b0-b697-2286cf9eb70d · outbound

This paper cites Vision transformers need registers.

RoboTTT: Context Scaling for Robot Policies Vision transformers need registers

Reference 14

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Observation 75aabaec-bae6-446b-9c3f-6b52c9fa259b · outbound

This paper cites Causal confusion in imitation learning.

RoboTTT: Context Scaling for Robot Policies Causal confusion in imitation learning

Reference 15

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source=pdf_text observed=2026-08-01T23:43:17.500771Z digest=sha256:80cb4a417e403914c1747a14a485cc8c9ca6fafc3a09357c4b2a43948a573819

Observation 64a89432-6cef-4f8d-8c0f-a90073924f07 · outbound

This paper cites Longrope: Extending LLM context window beyond 2 million tokens.

RoboTTT: Context Scaling for Robot Policies Longrope: Extending LLM context window beyond 2 million tokens

Reference 16

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Observation 0e767ef8-3bf3-4dfa-ad51-c4b56f495fe0 · outbound

This paper cites Knowledge insulating vision-language-action models: Train fast, run fast, generalize better.Advances in Neural Information Processing Systems, 38:102867–102888,.

RoboTTT: Context Scaling for Robot Policies Knowledge insulating vision-language-action models: Train fast, run fast, generalize better.Advances in Neural Information Processing Systems, 38:102867–102888,

Reference 17

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source=pdf_text observed=2026-08-01T23:43:17.621297Z digest=sha256:74f5e758f8f5c5f15d6dfd9fc764c872999b289b717a2a2206942c6e879ee23c

Observation 45908b95-8882-4a25-8430-4e8aa0feedaf · outbound

This paper cites One-shot imitation learning.Advances in neural information processing systems, 30, 2017.

RoboTTT: Context Scaling for Robot Policies One-shot imitation learning.Advances in neural information processing systems, 30, 2017

Reference 18

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Observation 3ce97011-8962-433a-b591-c49bd1ccf417 · outbound

This paper cites MolmoAct2: Action Reasoning Models for Real-world Deployment.

RoboTTT: Context Scaling for Robot Policies MolmoAct2: Action Reasoning Models for Real-world Deployment

Reference 19

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Observation df012763-9c9e-4d9e-b527-d19ad5d2d84b · outbound

This paper cites In-place test-time training.

RoboTTT: Context Scaling for Robot Policies In-place test-time training

Reference 20

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Observation 21ae6f51-285f-4fbf-b66d-ad128b44c05c · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

RoboTTT: Context Scaling for Robot Policies Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 21

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source=pdf_text observed=2026-08-01T23:43:18.143170Z digest=sha256:7869448e8d5522aea115eca80e7f6e09ebc32ba7732d3dfaa93777423b6cb310

Observation 7c56c801-f669-475d-bbf6-15a1ece47b77 · outbound

This paper cites In-Context Imitation Learning via Next-Token Prediction.

RoboTTT: Context Scaling for Robot Policies In-Context Imitation Learning via Next-Token Prediction

Reference 22

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source=pdf_text observed=2026-08-01T23:43:18.315751Z digest=sha256:4b9732d59cbc8bf2ae785cc8ebf7f1a350339d0e2b12ca14621d2766ae16f41b

Observation ab592c32-4aee-464e-adb5-8488050eb4c8 · outbound

This paper cites Gated Memory Policy: In-Context Memorization and Adaptation.

RoboTTT: Context Scaling for Robot Policies Gated Memory Policy: In-Context Memorization and Adaptation

Reference 23

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Observation bbdbafdb-7351-4a15-b427-df3b4c3af5c5 · outbound

This paper cites ViT$^3$: Unlocking Test-Time Training in Vision.

RoboTTT: Context Scaling for Robot Policies ViT$^3$: Unlocking Test-Time Training in Vision

Reference 24

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Observation 32f5db61-5831-4dd5-b454-5197ca70c90f · outbound

This paper cites Gaussian Error Linear Units (GELUs).

RoboTTT: Context Scaling for Robot Policies Gaussian Error Linear Units (GELUs)

Reference 25

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source=pdf_text observed=2026-08-01T23:43:18.605850Z digest=sha256:c43ea45baa649001b667d89dd12834bf8f06e8ff93edf8d759a8c74b71523fee

Observation 246a5cf8-fc7f-4118-8de1-dc5f914c3587 · outbound

This paper cites Long short-term memory.Neural Comput., 9(8):1735–1780, November 1997.

RoboTTT: Context Scaling for Robot Policies Long short-term memory.Neural Comput., 9(8):1735–1780, November 1997

Reference 26

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Observation cb44f0c5-655b-44cb-bc94-7ab8f1a0fd00 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

RoboTTT: Context Scaling for Robot Policies RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 27

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Observation f17fabd4-e606-4f7e-a0f6-2d0dab8c9170 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

RoboTTT: Context Scaling for Robot Policies MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 28

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Observation 2f34aee2-6ed3-479c-8b72-6d7c3d813a3a · outbound

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

RoboTTT: Context Scaling for Robot Policies $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 29

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Observation 8dc905b8-b698-4345-b55c-684e686e9301 · outbound

This paper cites Perceiver: General Perception with Iterative Attention.

RoboTTT: Context Scaling for Robot Policies Perceiver: General Perception with Iterative Attention

Reference 30

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source=pdf_text observed=2026-08-01T23:43:19.069877Z digest=sha256:e3b05c44b63b458a997bfb7d2a2a99fe935a1f7dca1ef73a953bfd0d5e0c6e6f

Observation f797f881-d5c3-487b-a9b2-cb05b3d30b68 · outbound

This paper cites Contextvla: Vision-language-action model with amortized multi-frame context.arXiv preprint arXiv: 2510.04246,.

RoboTTT: Context Scaling for Robot Policies Contextvla: Vision-language-action model with amortized multi-frame context.arXiv preprint arXiv: 2510.04246,

Reference 31

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Observation 96156111-14c3-4546-9035-d4717af3cdce · outbound

This paper cites VIMA: General Robot Manipulation with Multimodal Prompts.

RoboTTT: Context Scaling for Robot Policies VIMA: General Robot Manipulation with Multimodal Prompts

Reference 32

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source=pdf_text observed=2026-08-01T23:43:19.452926Z digest=sha256:294a1b4564c829e6b57d782fe585d3f669766f971270177816e507801a625e07

Observation 4c22e418-8a6e-4445-bbff-1ffa2a8be276 · outbound

This paper cites Muon: An optimizer for hidden layers in neural networks, 2024.

RoboTTT: Context Scaling for Robot Policies Muon: An optimizer for hidden layers in neural networks, 2024

Reference 33

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Observation c3431f6d-aad9-47d3-a82e-05a67bb29b1f · outbound

This paper cites Scaling Laws for Neural Language Models.

RoboTTT: Context Scaling for Robot Policies Scaling Laws for Neural Language Models

Reference 34

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source=pdf_text observed=2026-08-01T23:43:19.641902Z digest=sha256:f2ae4f94efaee768359dbce0e738eb8577ff55820664cd920db4cd0fc449f063

Observation 1063c27c-5bae-46b2-aa5a-f8dc9d8d6bf5 · outbound

This paper cites Lattice: Learning to efficiently compress the memory.arXiv preprint arXiv: 2504.05646, 2025.

RoboTTT: Context Scaling for Robot Policies Lattice: Learning to efficiently compress the memory.arXiv preprint arXiv: 2504.05646, 2025

Reference 35

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Observation 401bba7f-0bd1-4c22-971f-075677c5e615 · outbound

This paper cites OpenVLA: An open-source vision-language- action model.

RoboTTT: Context Scaling for Robot Policies OpenVLA: An open-source vision-language- action model

Reference 36

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Observation 94557673-8d31-41ed-ad4e-7e5f89972af1 · outbound

This paper cites Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning.

RoboTTT: Context Scaling for Robot Policies Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

Reference 37

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source=pdf_text observed=2026-08-01T23:43:19.940511Z digest=sha256:25fa62cebc505755075176031cbf38f5691249417827ff34b99bfca53179963d

Observation 1c405f48-a7c0-4971-926a-952c256c786e · outbound

This paper cites RMA: Rapid Motor Adaptation for Legged Robots.

RoboTTT: Context Scaling for Robot Policies RMA: Rapid Motor Adaptation for Legged Robots

Reference 38

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Observation ac733d51-5719-42ca-865a-df219808172f · outbound

This paper cites In-context reinforcement learning with algorithm distillation.

RoboTTT: Context Scaling for Robot Policies In-context reinforcement learning with algorithm distillation

Reference 39

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source=pdf_text observed=2026-08-01T23:43:20.145535Z digest=sha256:7484cc4b9d2da19edca0f49da91c33f6a7d06a2e26feaa1ca49d69a018f14fbb

Observation ab1bf8e4-3a6a-463b-9724-9859ef227146 · outbound

This paper cites Causal World Modeling for Robot Control.

RoboTTT: Context Scaling for Robot Policies Causal World Modeling for Robot Control

Reference 40

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source=pdf_text observed=2026-08-01T23:43:20.476805Z digest=sha256:ac9003e3a9a3040af7f14f61ee9af39b885391925f81815262ee197886582098

Observation f16f371a-f3fe-45b3-aa73-c8c688eee140 · outbound

This paper cites Unified Video Action Model.

RoboTTT: Context Scaling for Robot Policies Unified Video Action Model

Reference 41

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source=pdf_text observed=2026-08-01T23:43:20.625021Z digest=sha256:52e2a5d5d922582907e126083c36e3daeec36aacf990acc547de3cce9d8fa5c1

Observation 093fd230-a580-4b3e-81ae-e6d38fd91f8d · outbound

This paper cites Tnt: Improving chunkwise training for test-time memorization.arXiv preprint arXiv:2511.07343, 2025.

RoboTTT: Context Scaling for Robot Policies Tnt: Improving chunkwise training for test-time memorization.arXiv preprint arXiv:2511.07343, 2025

Reference 42

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source=pdf_text observed=2026-08-01T23:43:20.753194Z digest=sha256:3efc42c7b11e632c143025fc827771d71e80a3e55d07d5321e71c3a31450f26a

Observation 12caec7c-83d3-4426-b2aa-63680cdd518a · outbound

This paper cites Parallelizing non-linear sequential models over the sequence length.

RoboTTT: Context Scaling for Robot Policies Parallelizing non-linear sequential models over the sequence length

Reference 43

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source=pdf_text observed=2026-08-01T23:43:20.853254Z digest=sha256:ebcde60e060afcb74623f52487e3c6b53f069655ca9453d453f8f40d13280ef9

Observation 7c7c90f2-7598-44a3-b5a1-9e85496a7252 · outbound

This paper cites Onetwovla: A unified vision-language-action model with adaptive reasoning.arXiv preprint arXiv:2505.11917, 2025.

RoboTTT: Context Scaling for Robot Policies Onetwovla: A unified vision-language-action model with adaptive reasoning.arXiv preprint arXiv:2505.11917, 2025

Reference 44

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source=pdf_text observed=2026-08-01T23:43:20.961154Z digest=sha256:a0873d0b7bf97d3a484391397219cdd09ac25c5a8458d9a02a77344095a4cb2a

Observation 82486657-606f-4bfe-ba84-a0eeefb1a8fa · outbound

This paper cites On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning.

RoboTTT: Context Scaling for Robot Policies On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning

Reference 45

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source=pdf_text observed=2026-08-01T23:43:21.089034Z digest=sha256:da8a59f7a760cf260793cfc3c61b671aba73b8b65d2dab25bf23c6521156d1f0

Observation 52691eba-b89c-48b3-84db-2ecbc6d25aa4 · outbound

This paper cites Lost in the middle: How language models use long contexts.Transactions of the association for computational linguistics, 12:157–173, 2024.

RoboTTT: Context Scaling for Robot Policies Lost in the middle: How language models use long contexts.Transactions of the association for computational linguistics, 12:157–173, 2024

Reference 46

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source=pdf_text observed=2026-08-01T23:43:21.243324Z digest=sha256:70dd604bdf2433a97f51f79507e5690025e8a6eb222728b4f8171180d2aa706c

Observation b5d65760-c1b0-4697-b8f9-96d99ec09698 · outbound

This paper cites RDT-1B:a diffusionfoundationmodelforbimanual manipulation.

RoboTTT: Context Scaling for Robot Policies RDT-1B:a diffusionfoundationmodelforbimanual manipulation

Reference 47

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source=pdf_text observed=2026-08-01T23:43:21.388466Z digest=sha256:897109b785330ac5429eb22a07d7393bbb05e9a87678f5fe26c3a3f75da790ea

Observation 540f2c13-b83f-4193-b1f6-702aacb3ce6f · outbound

This paper cites Loshchilov and F.

RoboTTT: Context Scaling for Robot Policies Loshchilov and F

Reference 48

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source=pdf_text observed=2026-08-01T23:43:21.500150Z digest=sha256:cc3cb355271a80d87e8c7501067848aab93184fc57c0b908ae16cc59830a1a41

Observation 8a09a967-5b70-4d1c-bbb3-dad3b3669094 · outbound

This paper cites Savarese, Yuke Zhu, and Roberto Mart’in-Mart’in.

RoboTTT: Context Scaling for Robot Policies Savarese, Yuke Zhu, and Roberto Mart’in-Mart’in

Reference 49

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source=pdf_text observed=2026-08-01T23:43:21.701526Z digest=sha256:f9036c6ec49db50dd2d12ded55789d4853bc57939e8dc9dd475ae140bb4b8611

Observation d4504d80-b22a-4cbf-b028-bc209c9a4f8d · outbound

This paper cites Bpp: Long-context robot imitation learning by focusing on key history frames.arXiv preprint arXiv: 2602.15010, 2026.

RoboTTT: Context Scaling for Robot Policies Bpp: Long-context robot imitation learning by focusing on key history frames.arXiv preprint arXiv: 2602.15010, 2026

Reference 50

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source=pdf_text observed=2026-08-01T23:43:21.860647Z digest=sha256:020b06789c76d146f72a77addfc45e708f0ca0eb55e72d00c460ca122fec3cbf

Observation ccfa2e99-5d96-4274-bc32-8cd3b2d3c3ab · outbound

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

RoboTTT: Context Scaling for Robot Policies GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 51

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source=pdf_text observed=2026-08-01T23:43:22.041546Z digest=sha256:b559c9362c98717ed14f8cda0a81caec2f6d4a72ab10c09ef6cc9ff087603396

Observation 5801f085-fe96-4c43-847b-ef08b6073d5f · outbound

This paper cites Scalable Diffusion Models with Transformers.

RoboTTT: Context Scaling for Robot Policies Scalable Diffusion Models with Transformers

Reference 52

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source=pdf_text observed=2026-08-01T23:43:22.166559Z digest=sha256:2660bfec528178486fafa76613646b0cd7e026c67329b98968e1444b55ff8f8a

Observation d8959a2c-822e-4bc9-8aff-15794a67d0e3 · outbound

This paper cites Yarn: Efficient context window extension of large language models.

RoboTTT: Context Scaling for Robot Policies Yarn: Efficient context window extension of large language models

Reference 53

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source=pdf_text observed=2026-08-01T23:43:22.282663Z digest=sha256:1c229afa4f9e996631dc9f5142ca9facfb94c049bf98873946aa1e7375083ff8

Observation a70971ab-1cc4-4288-9f12-9d103f668b30 · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

RoboTTT: Context Scaling for Robot Policies FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 54

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source=pdf_text observed=2026-08-01T23:43:22.434898Z digest=sha256:bd4faebf56fb987b80aa9c2691b931768829f18d74f9b0ec810cbbe8d0bc1fd3

Observation 0d5f071e-d6cb-4c66-8427-19b06453fef4 · outbound

This paper cites In-hand object rotation via rapid motor adaptation.

RoboTTT: Context Scaling for Robot Policies In-hand object rotation via rapid motor adaptation

Reference 55

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source=pdf_text observed=2026-08-01T23:43:22.579096Z digest=sha256:ddcacb1affe202adc62b66702836d178ac0d4ee2def6dec8272fe106b2a42b47

Observation 891e13c8-ca28-45ac-b20e-a2c8ed408713 · outbound

This paper cites an unresolved cited work.

RoboTTT: Context Scaling for Robot Policies Unresolved cited work

Reference 56

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source=pdf_text observed=2026-08-01T23:43:22.735442Z digest=sha256:1ae81b308eed6f4fa17f762eec9bb46a75a87d5eb5b4c7ef21c3fc5a9e68802f

Observation b1020e68-589a-410f-89ae-082605916bfe · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning.

RoboTTT: Context Scaling for Robot Policies A reduction of imitation learning and structured prediction to no-regret online learning

Reference 57

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source=pdf_text observed=2026-08-01T23:43:22.864898Z digest=sha256:616bc3b10fe8b5e8a85e3cf3c0abb3efac78bd0bb5053e45a693b7d6fe9bce62

Observation fae23fa3-a051-474e-b2df-864b44533411 · outbound

This paper cites Linear transformers are secretly fast weight programmers.

RoboTTT: Context Scaling for Robot Policies Linear transformers are secretly fast weight programmers

Reference 58

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source=pdf_text observed=2026-08-01T23:43:22.987980Z digest=sha256:d6fd41b0a0f5129c6d8bdfc79dea9290d3d0ae9658d78012b29b93b8bc41068c

Observation 64578de8-8e8d-49a0-b2e5-6c9c8ec74eaf · outbound

This paper cites Eagle: Exploring the design space for multimodal llms with mixture of encoders.

RoboTTT: Context Scaling for Robot Policies Eagle: Exploring the design space for multimodal llms with mixture of encoders

Reference 59

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source=pdf_text observed=2026-08-01T23:43:23.154909Z digest=sha256:1e1be84fb7013b2aa216985fa537652bb795a1a4a5cbaf7271c04b20fe94ed78

Observation 38c3137a-f89f-4ebc-972b-08378acbe12b · outbound

This paper cites SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics.

RoboTTT: Context Scaling for Robot Policies SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Reference 60

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source=pdf_text observed=2026-08-01T23:43:23.313663Z digest=sha256:34841415d7f134ca75d6496d77cd1c34503b07366ed79b318f14405b7ace7217

Observation a0d925c1-36b6-47a0-b14e-dda8d3251f56 · outbound

This paper cites Memer: Scaling up memory for robot control via experience retrieval.arXiv preprint arXiv: 2510.20328, 2025.

RoboTTT: Context Scaling for Robot Policies Memer: Scaling up memory for robot control via experience retrieval.arXiv preprint arXiv: 2510.20328, 2025

Reference 61

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source=pdf_text observed=2026-08-01T23:43:23.447664Z digest=sha256:ff252a97b024aaa470c14acb4271c0df8b9654eff6b2fa3c84cf0fbf7f6b346f

Observation 9a9b1450-4036-4991-b2bf-e4ff38ff5fb2 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

RoboTTT: Context Scaling for Robot Policies RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 62

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source=pdf_text observed=2026-08-01T23:43:23.575896Z digest=sha256:9c106e895a65ba87e0ac234be8ec2380ceaecf7487436ff1d1933c762677d529

Observation 413bb12a-8ad4-4bdc-ac38-0680d192f275 · outbound

This paper cites Efros, and Moritz Hardt.

RoboTTT: Context Scaling for Robot Policies Efros, and Moritz Hardt

Reference 63

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source=pdf_text observed=2026-08-01T23:43:23.667612Z digest=sha256:791ca40426578eacbe968a966e40c756964da64acd691806efd86e46c746e4bf

Observation 7b605e73-4aa8-472f-8145-f110b76246a0 · outbound

This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

RoboTTT: Context Scaling for Robot Policies Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 64

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source=pdf_text observed=2026-08-01T23:43:23.808805Z digest=sha256:25ff356ff4f03191e37b2e72f08cb78028609cc9385ada4e5d1483fb1d3cdbf1

Observation 0340e60d-b7e9-4757-852e-1e738abeaee5 · outbound

This paper cites End-to-end test-time training for long context.arXiv preprint arXiv: 2512.23675, 2025.

RoboTTT: Context Scaling for Robot Policies End-to-end test-time training for long context.arXiv preprint arXiv: 2512.23675, 2025

Reference 65

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source=pdf_text observed=2026-08-01T23:43:23.953687Z digest=sha256:9e63efbe439e7a8d9f6693ea38d2dfa6c12a229de560ace515f3d4fa9cce58d0

Observation 46e329bf-da5f-4283-bab2-3740d621764c · outbound

This paper cites Human-Timescale Adaptation in an Open-Ended Task Space.

RoboTTT: Context Scaling for Robot Policies Human-Timescale Adaptation in an Open-Ended Task Space

Reference 66

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source=pdf_text observed=2026-08-01T23:43:24.092787Z digest=sha256:16b17022e86907e3799bda58b57f6e805275419c4777b4c7a2171d8b104ed777

Observation 8364f1c7-de09-47db-ae52-87245e16f758 · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

RoboTTT: Context Scaling for Robot Policies Octo: An Open-Source Generalist Robot Policy

Reference 67

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source=pdf_text observed=2026-08-01T23:43:24.217680Z digest=sha256:a81b61b96617b11df1209db9aebeedffd593eb02c1523123e02d8d4a94714807

Observation f9bbfd80-e5c7-45fa-a24c-bb465117d700 · outbound

This paper cites Learning Long-Context Diffusion Policies via Past-Token Prediction.

RoboTTT: Context Scaling for Robot Policies Learning Long-Context Diffusion Policies via Past-Token Prediction

Reference 68

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source=pdf_text observed=2026-08-01T23:43:24.325693Z digest=sha256:6e59bc6202c9915c494e8a8230a7dc3312780fc749894d3f4b064605880c605b

Observation ead81c8c-1ab1-4049-abb4-9fd29beeb56c · outbound

This paper cites Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Equi, Quan Vuong, Jost Tobias Springen- berg, Sergey Levine, Chelsea Finn, and Danny Driess.

RoboTTT: Context Scaling for Robot Policies Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Equi, Quan Vuong, Jost Tobias Springen- berg, Sergey Levine, Chelsea Finn, and Danny Driess

Reference 69

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source=pdf_text observed=2026-08-01T23:43:24.424837Z digest=sha256:fc1a987d3db91c7da01543b913599e29d2e3fd827cf54cef2566d61b8735e533

Observation bd438cd3-dc70-4862-ba26-62d73d10ad53 · outbound

This paper cites Fighting Copycat Agents in Behavioral Cloning from Observation Histories.

RoboTTT: Context Scaling for Robot Policies Fighting Copycat Agents in Behavioral Cloning from Observation Histories

Reference 70

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source=pdf_text observed=2026-08-01T23:43:24.534250Z digest=sha256:b595ed6b762b4e8184d360500d3cca26a2cb4aae3c366e9cbd209079784f550b

Observation ba4c103c-6603-43ad-b217-23528caa1e82 · outbound

This paper cites Efficient streaming language models with attention sinks.

RoboTTT: Context Scaling for Robot Policies Efficient streaming language models with attention sinks

Reference 71

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source=pdf_text observed=2026-08-01T23:43:24.619347Z digest=sha256:6ecb7c1941e839b3b94a510e02ea12ab46084b6605b8d98b3e6eb38d06a4e85e

Observation 288b4dad-98b3-488b-b329-3d80118200b4 · outbound

This paper cites Magma: A Foundation Model for Multimodal AI Agents.

RoboTTT: Context Scaling for Robot Policies Magma: A Foundation Model for Multimodal AI Agents

Reference 72

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source=pdf_text observed=2026-08-01T23:43:24.735638Z digest=sha256:e8fe7cbd471b3613a5a25888b4d3525254ba64f9b725b65ae76c8cc0766e7bcf

Observation 13d7e682-ecc2-4b03-b4af-f7de2875f4b7 · outbound

This paper cites Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024.

RoboTTT: Context Scaling for Robot Policies Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024

Reference 73

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source=pdf_text observed=2026-08-01T23:43:24.824823Z digest=sha256:0ce357f75b198f7ac751796a32cdfdd9c7de4b2c899087fe7721f41217bfb34d

Observation ba062ce4-2292-4fa6-8e6d-b262b94d2869 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

RoboTTT: Context Scaling for Robot Policies Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 74

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source=pdf_text observed=2026-08-01T23:43:24.934146Z digest=sha256:dd3ed4c6f805a36fae60776da7b46de17a4835ca3717b78f0183ba21b25a3a87

Observation 1406adec-9535-4996-a609-bb4c1d099445 · outbound

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

RoboTTT: Context Scaling for Robot Policies World Action Models are Zero-shot Policies

Reference 75

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source=pdf_text observed=2026-08-01T23:43:25.044994Z digest=sha256:969a00ff0207d53c7c9a14d163078de6a2e270d7322808591633f2cd33616099

Observation b699bc61-a853-48c1-b0b8-29722b0c2715 · outbound

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

RoboTTT: Context Scaling for Robot Policies Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 76

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no resolver link, observed 2026-08-01T23:43:25.154593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.154593Z digest=sha256:be29e73b766f2e36df0118881e09dbc387e2464c6e9224cb96eacc1ce7aed166

Observation 46c6a371-52b0-468b-ad5e-a2f6f9c866f0 · outbound

This paper cites Learning to Discover at Test Time.

RoboTTT: Context Scaling for Robot Policies Learning to Discover at Test Time

Reference 77

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no resolver link, observed 2026-08-01T23:43:25.247468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.247468Z digest=sha256:0fc657ce4c165441a15b5373ec420acee98d0c9a079be45a5db766c32bd2e156

Observation e6ff4bbe-9653-450f-a74b-dba3b8ab30c3 · outbound

This paper cites Test-Time Training Done Right.

RoboTTT: Context Scaling for Robot Policies Test-Time Training Done Right

Reference 78

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no resolver link, observed 2026-08-01T23:43:25.364420Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T23:43:25.364420Z digest=sha256:a9da106bc6f8d99960e64b603a197298fae459879fd4dae45c39acefda52a845

Observation 163994e5-5d4d-4c82-a6b8-ecd5b181612d · outbound

This paper cites Fast-weight product key memory.arXiv preprint arXiv: 2601.00671, 2026.

RoboTTT: Context Scaling for Robot Policies Fast-weight product key memory.arXiv preprint arXiv: 2601.00671, 2026

Reference 79

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no resolver link, observed 2026-08-01T23:43:25.476872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.476872Z digest=sha256:f0a0abb270db2f2fea7f059eedea4d5b7a6812aee4f08386209d9ee4e791d868

Observation 48010602-8a08-4728-8576-64ddd5699ae3 · outbound

This paper cites TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies.

RoboTTT: Context Scaling for Robot Policies TraceVLA: Visual trace prompting enhances spatial-temporal awareness for generalist robotic policies

Reference 80

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no resolver link, observed 2026-08-01T23:43:25.589301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.589301Z digest=sha256:f547b82a192a02ad8ac4e09cd31bc7c230b66d8dd9f3bcc568322801667a4551

Observation 0b2b2155-4bf6-479a-bc5b-69fdfe1ab351 · outbound

This paper cites Egoscale: Scaling dexterous manipulation with diverse egocentric human data.arXiv preprint arXiv: 2602.16710, 2026.

RoboTTT: Context Scaling for Robot Policies Egoscale: Scaling dexterous manipulation with diverse egocentric human data.arXiv preprint arXiv: 2602.16710, 2026

Reference 81

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no resolver link, observed 2026-08-01T23:43:25.694733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.694733Z digest=sha256:96356f2824aa8ae6ec750aa4e414445a1fa6bca3b70f4306528bdfda8324fd32

Observation 17e5b6a8-7bb5-4e4f-9b51-960e3ae87ff1 · outbound

This paper cites Ttt-parkour: Rapid test-time training for perceptive robot parkour.arXiv preprint arXiv:2602.02331, 2026.

RoboTTT: Context Scaling for Robot Policies Ttt-parkour: Rapid test-time training for perceptive robot parkour.arXiv preprint arXiv:2602.02331, 2026

Reference 82

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no resolver link, observed 2026-08-01T23:43:25.797883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.797883Z digest=sha256:174f147e45e40bca3c48ca3fd2de6634217f89c618334c7d08629f55895b2142

Observation b17d375b-cfa8-4fce-b228-2402c456cf02 · outbound

This paper cites Pup Go Car.

RoboTTT: Context Scaling for Robot Policies Pup Go Car

Reference 83

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no resolver link, observed 2026-08-01T23:43:25.912505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:25.912505Z digest=sha256:510285cb9f7f7191b0e0d372a8cb366e65f53c20ed71688e4bddd93d28eb2d58

Observation a48f8ce1-3742-4e94-8690-a0157bcb9aec · outbound

This paper cites URLhttps://openreview.net/forum?id=hy0a5MMPUv.

RoboTTT: Context Scaling for Robot Policies URLhttps://openreview.net/forum?id=hy0a5MMPUv

Reference 2023

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no resolver link, observed 2026-08-01T23:43:20.312710Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T23:43:20.312710Z digest=sha256:13e15bf4724162f290e5ccc40457a79ef0e1625990fb93aa1105bfecce7cd33b

Observation 7f80de5a-26ac-47d6-903b-74fe1cdaf01a · outbound

This paper cites URLhttps://openreview.net/forum?id=2dnO3LLiJ1.

RoboTTT: Context Scaling for Robot Policies URLhttps://openreview.net/forum?id=2dnO3LLiJ1

Reference 2024

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unresolved
no resolver link, observed 2026-08-01T23:43:17.415629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T23:43:17.415629Z digest=sha256:eeab76f1d78a08061767eda0483cc0ec4b539369202736c4f7f5fe59ea291d38

Observation c190bd0d-7235-4835-8d49-daeca4bb0222 · outbound

This paper cites an unresolved cited work.

RoboTTT: Context Scaling for Robot Policies Unresolved cited work

Reference 2025

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no resolver link, observed 2026-08-01T23:43:19.275272Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T23:43:19.275272Z digest=sha256:f9757eec141db38767f1f0c617cb5422d5e481792183c8c15f634163cadf4f6d

Pith citing papers

No inbound Pith citation observations are available.