Cognitive Foundation Agents for Generalizable Vision-and-Language Navigation
Bibliographic record
Abstract
Vision-and-Language Navigation (VLN) is a key task in embodied AI, yet most agents remain reactive, task specific, and cognitively limited. As these systems extend to real-world areas like assistive guidance, disaster response, and multi agent teaming, the lack of ability to reason, reflect, and adapt presents critical flaws. This paper introduces Cognitive Foundation Agent (CFA), a conceptual model that reconceives VLN as a problem of spatial cognition and collaborative intelligence. CFA integrates perception, language, memory, and planning in a cognitive process that supports real time adaptation in complex environments. The model comprises five asynchronous modules: multimodal perception, meta-cognition, self-evolving world model, spatiotemporal planning, and multi agent collaboration, linked by a real-time Cognitive Feedback Loop (CFL) that enables agents to perceive, coordinate, reason, and adapt across tasks and environments. To drive progress in this space, this paper outlines the need for CFA-Bench, a dedicated evaluation suite for cognitively grounded navigation. CFA represents a shift toward embodied agents that move, reason, and collaborate with human-aligned spatial intelligence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".