LongNav-R1 Scaling Under Instruction Ambiguity in ValHouse3D Benchmark

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does LongNav-R1's performance scale with increasing instruction ambiguity complexity on the ValHouse3D benchmark compared to single-turn VLA policies in terms of trajectory deviation and success. In the vision and language navigation task (Anderson et al. 2018), the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does LongNav-R1's performance scale with increasing instruction ambiguity complexity on the ValHouse3D benchmark compared to single-turn VLA policies in terms of trajectory deviation and success rate?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance