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Source note3 min read

DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation

Source

arXiv

arxiv.org/abs/2607.01043v1

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Source

Plain-Language Brief

The researchers present DART-VLN, a test-time control framework for vision-language navigation agents that aims to improve their performance by addressing two common issues: stale historical evidence at memory readout and inefficient local backtracking during action selection. The framework uses Test-Time Memory Decay and Anti-Loop Regularization, which do not require new learnable parameters or changes to the learned backbone.

What the Document Says

  • The text discusses a test-time control framework called DART-VLN for memory-based discrete vision-language navigation (VLN) agents.
  • DART-VLN aims to address two common failure modes: stale historical evidence at memory readout and inefficient local backtracking during action selection.
  • Test-Time Memory Decay is a read-side memory reweighting rule that suppresses stale and redundant evidence without altering stored content.
  • Anti-Loop Regularization is a lightweight next-hop penalty that discourages immediate reversals during action selection.
  • DART-VLN introduces no new learnable parameters and leaves the learned backbone unchanged.
  • Experiments on R2R and REVERIE show improved navigation performance with shorter trajectories, lower runtime, and better quality-efficiency trade-offs when using DART-VLN compared to decay-only.
  • Behavioral analysis confirms that anti-loop regularization reduces local backtracking and improves path efficiency under frozen backbones.

Key Concepts Mentioned

  • memory-based discrete vision-language navigation
  • test-time control
  • Test-Time Memory Decay
  • Anti-Loop Regularization
  • stale historical evidence
  • inefficient local backtracking

Claims Made by the Document

  • DART-VLN improves the performance of memory-based discrete VLN agents.
  • Test-Time Memory Decay and Anti-Loop Regularization do not require new learnable parameters or changes to the learned backbone.

Evidence Used

  • Experiments on R2R and REVERIE show improved navigation performance with shorter trajectories, lower runtime, and better quality-efficiency trade-offs when using DART-VLN compared to decay-only.

Methods / Approach

  • Test-Time Memory Decay
  • Anti-Loop Regularization

Limitations Stated by the Authors

The authors do not mention any limitations of their proposed method in the provided text.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent is a memory-based discrete vision-language navigation (VLN) agent, and its performance may be limited by the quality and amount of data used for training.

Open Questions

  • What specific improvements does anti-loop regularization bring to path efficiency under frozen backbones?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text suggests that anti-loop regularization reduces local backtracking and improves path efficiency, but it does not provide detailed information on how this is achieved.

Raw Abstract / Excerpt

Memory-based discrete vision-language navigation (VLN) agents must act under partial observability, yet even strong frozen backbones remain vulnerable at test time. Two common failure modes are stale historical evidence at memory readout and inefficient local backtracking during action selection. We present DART-VLN, a training-free test-time control framework for discrete VLN. DART-VLN combines Test-Time Memory Decay, a read-side memory reweighting rule that suppresses stale and redundant evidence without rewriting stored content, with Anti-Loop Regularization, a lightweight next-hop penalty that discourages immediate reversals during action selection. The framework introduces no new learnable parameters and leaves the learned backbone unchanged. Experiments on R2R and REVERIE show a consistent pattern: decay-only provides stable read-side gains, while decay+anti-loop achieves the best overall quality-efficiency trade-off, yielding shorter trajectories, lower runtime, and improved navigation performance in key settings. Behavioral analysis further confirms that anti-loop regularization reduces local backtracking and improves path efficiency under frozen backbones. Overall, the results show that modest test-time control can make memory-based discrete VLN more reliable and efficient without retraining.