WAM Research Interest Profile
This profile is read verbatim by the relevance-filter and scoring prompts. It defines what "World Action Models" means for this project and what we care about. Edit freely — no code changes needed.
What counts as a World Action Model (WAM)
A WAM is a learned model that maps perception + goals to actions in the physical or simulated world, often grounded in a learned world model (predictive dynamics). The umbrella includes:
- Generalist robot/agent policies and embodied foundation models
- Vision-Language-Action (VLA) models that output actions
- World models / action-conditioned video & dynamics models used for planning or control
- Models that unify perception, prediction, and action for embodied agents
Two relevance tracks
- core — the paper is a WAM (or directly proposes/evaluates one). Gets the full two-layer rubric and benchmark extraction.
- adjacent — VLA / world model / video-generation work that is not a WAM itself but carries a technique plausibly transferable to WAM. Gets an innovation note only (key idea + why it could transfer); no rubric scores.
- drop — unrelated to the above.
What we care most about (drives WAM-specific scoring)
Top-4 (weighted 2x):
- inference_speed — real-time or faster control; latency/throughput on stated hardware.
- generalist — generalizes across tasks, benchmarks, and especially embodiments.
- specialist — extreme accuracy/robustness on a specific task or task set.
- inference_cost — compute/$ to run; small models that punch above their weight.
Also tracked (weighted 1x): trustworthiness/safety, collaborative (multi-robot control), controlled/steerable generation of videos or actions, and other features (e.g. async inference, streaming, on-device).
Scoring guidance
- Score each metric 0–10 from evidence in the paper; use "N/A" when the paper does not address a metric — do not guess or penalize with a 0.
- Be skeptical of self-reported numbers. Note the dataset a model was trained/finetuned on; the same model name on a different dataset is a different system.
- Reward reproducibility (released code/data/weights) under
impact.