HAM-VLN
HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation
An Liu, Bingxi Liu, Hongyu Ding, Yixuan Jiang, Yaran Chen, Fulin Tang, et al. · 2026-07-31
- Management
- T2 Dynamic Memory
- Memory changes during deployment through a stable management policy.
- Representation
- Hybrid
- Distinct operative representation paths are explicitly linked for downstream retrieval, reasoning, or control.
How experience becomes memory
Each path is one memory component, traced from what is stored to how it is found again.
recent waypoint observations
- Represented asRetained perceptual source evidenceSource- and modality-specific
- Stored inHierarchical multi-tier store
- Retrieved byMetadata / spatiotemporal retrieval
- Source evidenceSource directly available
Recent depth-grounded waypoints remain verbatim within a bounded window.Full paper: Memory · PDF p. 3 · Figure 1
persistent world graph and reflection notes
- Represented asSpatial, temporal & cross-modal structured memoryCross-represented explicit
- Stored inGraph / relational store
- Retrieved byStructure traversal, Similarity search
- Source evidenceSource linked & recoverable
The agent records room, object, progress, and failure information in a depth-grounded graph.Full paper: Method · PDF p. 2 · Figure 1
How memory changes (T2 operations)
Update · Rule-based update
Each action decision also writes semantic and reflective information into the world graph. For HAM-VLN, this specifically supports fixed-rule insertion or replacement of recent waypoint observations.
Forgetting · Discrete eviction
Older verbatim waypoints leave the bounded recent window. For HAM-VLN, this specifically supports explicit deletion or eviction of stored items of recent waypoint observations.
Reorganization · Relinking & reindexing
Older history is re-accessed by relevance, recency, salience, and one-hop graph expansion. For HAM-VLN, this specifically supports relinking or reindexing of stored items of recent waypoint observations.