Paper Citation Record · LEDGER

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters

As of 22 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.11029.

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

pith.paper-citation-record.v1
2607.11029 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T07:09:16.882981Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

22 of 22 outbound references displayed

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unresolved22
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b76f0d30-ff36-4b69-aafd-0680ef611ac9 · outbound

This paper cites DD-PPO: Learning near-perfect PointGoal navigators from 2.5 billion frames,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters DD-PPO: Learning near-perfect PointGoal navigators from 2.5 billion frames,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:14.633819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c72f8d8-2785-4219-9a4e-44b20f1d53bd · outbound

This paper cites GNM: A General Navigation Model to Drive Any Robot.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters GNM: A General Navigation Model to Drive Any Robot

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:14.794966Z

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Observation 6db71e94-3207-4520-889b-7cf54c88d980 · outbound

This paper cites ViNT: A Foundation Model for Visual Navigation.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters ViNT: A Foundation Model for Visual Navigation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:14.936674Z

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Observation 4d781569-9851-4c31-b0d1-cbca0d1117e1 · outbound

This paper cites NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.037999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deb636b0-00a4-4c3b-b147-33a4fccae9db · outbound

This paper cites LoGoPlanner: Localization Grounded Navigation Policy with Metric- aware Visual Geometry,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters LoGoPlanner: Localization Grounded Navigation Policy with Metric- aware Visual Geometry,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.166281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a30c20a-6412-415a-adbc-3198e374b0b2 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Diffusion policy: Visuomotor policy learning via action diffusion,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.305235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91821f84-6fd8-4230-b4ee-3e12b96ab95d · outbound

This paper cites NoMaD: Goal masked diffusion policies for navigation and exploration,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters NoMaD: Goal masked diffusion policies for navigation and exploration,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.407088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05911213-236a-4864-9ad7-6ddf3f74badb · outbound

This paper cites Prior does matter: Visual navigation via denoising diffusion bridge models,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Prior does matter: Visual navigation via denoising diffusion bridge models,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.670164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 098164ec-d869-4596-9482-569b80dc6989 · outbound

This paper cites StepNav: Structured trajectory priors for efficient and multimodal visual navigation,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters StepNav: Structured trajectory priors for efficient and multimodal visual navigation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.825178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2be46c59-d4f0-46c3-baad-31af99a26d66 · outbound

This paper cites Rectified Schr\"odinger Bridge Matching for Few-Step Visual Navigation.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Rectified Schr\"odinger Bridge Matching for Few-Step Visual Navigation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.924113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9953dc54-41b1-46a3-b585-0aff9b97dcd7 · outbound

This paper cites SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters SanD-Planner: Sample-Efficient Diffusion Planner in B-Spline Space for Robust Local Navigation,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.006780Z

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Unavailable: canonical work link unavailable.

Observation 1b9e485d-c38a-46f5-a5c8-074b278e3511 · outbound

This paper cites The dynamic window approach to collision avoidance,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters The dynamic window approach to collision avoidance,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.090595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b63b3f-5f85-46d2-9d23-6f29afbbd928 · outbound

This paper cites Adaptive and explainable deployment of navigation skills via hierarchical deep reinforcement learning,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Adaptive and explainable deployment of navigation skills via hierarchical deep reinforcement learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.199883Z

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Unavailable: canonical work link unavailable.

Observation 302ca926-aa07-4ecd-be48-80a6731f9218 · outbound

This paper cites Object goal navigation using goal-oriented semantic exploration,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Object goal navigation using goal-oriented semantic exploration,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.265425Z

Source-reported events for the cited work

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Observation 48d31064-5360-4037-9a23-f0978042bb62 · outbound

This paper cites Viplanner: Visual semantic imperative learning for local navigation,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Viplanner: Visual semantic imperative learning for local navigation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.391291Z

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Unavailable: canonical work link unavailable.

Observation 08062a6e-a350-4712-9f82-9b2c5e6e294c · outbound

This paper cites iPlanner: Imperative path planning,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters iPlanner: Imperative path planning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.524869Z

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Unavailable: canonical work link unavailable.

Observation ee5a701e-f032-4396-a7a0-f4b6fe188a4b · outbound

This paper cites Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and- Language Navigation,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and- Language Navigation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.579888Z

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Unavailable: canonical work link unavailable.

Observation 3e58ec5d-a9a5-40a5-a0b4-cfddf6b70090 · outbound

This paper cites Depth anything V2,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Depth anything V2,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.645755Z

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Unavailable: canonical work link unavailable.

Observation acbfed47-c5f4-4854-bd2c-32869d3c4963 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Denoising Diffusion Probabilistic Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.722889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d945274-4e14-442c-9d80-bd2f966f0e49 · outbound

This paper cites Matterport3D: Learning from RGB- D data in indoor environments,.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters Matterport3D: Learning from RGB- D data in indoor environments,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.799529Z

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Unavailable: canonical work link unavailable.

Observation 6c5f98e9-8f55-4c9b-a27d-0b3fed0cfad2 · outbound

This paper cites On Evaluation of Embodied Navigation Agents.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters On Evaluation of Embodied Navigation Agents

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:16.882981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ad07f7-8d0b-4cc3-af0a-b6c720a267ae · outbound

This paper cites NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration.

Learning to Navigate Efficiently with Only 0.58M Trainable Parameters NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T07:09:15.518958Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.