---
pattern_id: pattern_localization_from_embodied_dialog_led
applicable_symptoms: [localization_from_embodied_dialog_led]
domain: Memory_Reasoning
---

# Automated localization from dialog history achieves only 32.7% success within 3m on unseen buildings, far below human performance of 70.4%.

**Domain**: `Memory_Reasoning`

## Fix

Use a neural architecture that integrates dialog history, first-person visual features, and top-down map-based spatial reasoning to predict observer location within 3m.

## Anti-pattern

Relying solely on dialog history without visual or map cues leads to poor generalization to unseen buildings.

## Cross-domain analogies

- **Perception_Vision** → Hierarchical decomposition with proximal and distal sub-networks for near and far spatial context.
  - related fix: Hierarchical network with proximal sub-network for near-field features, distal sub-network for far-field context, recurrent temporal modeling, and attention-based fusion to output continuous risk scores.
- **Planning_Decision** → Use Advisor/Arborist replanning to dynamically restructure dialog-based localization hypotheses upon failure.
  - related fix: Adaptive replanning via Advisor module (assess alternatives) and Arborist module (restructure plan) using LLM reasoning.
- **Learning_Training** → Unified multi-task co-training with shared cross-modal embeddings improves generalization from limited dialog history.
  - related fix: Unified multi-task model co-trained on all VLNVerse benchmark tasks (goal-oriented navigation, language-guided exploration, instruction following) using shared transformer-based cross-modal attention and common visual-linguistic embedding space.

## Patch

```diff
--- localization_from_embodied_dialog_led.before.py
+++ localization_from_embodied_dialog_led.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Automated localization from dialog history achieves only 32.7% success within 3m on unseen buildings, far below human performance of 70.4%.

+# Fix    : Use a neural architecture that integrates dialog history, first-person visual features, and top-down map-based spatial reasoning to predict observer location within 3m.

+# Avoid  : Relying solely on dialog history without visual or map cues leads to poor generalization to unseen buildings.

```
