papers.luk.mediaAgentGarten couples physics engines to a shared neural renderer through untextured 3D geometry maps. This separation lets AI agents interact with photorealistic virtual worlds in real time while keeping physical rules and object coordinates explicitly controllable in code.
RealCompanion introduces a longitudinal benchmark derived from 27,218 real-world human-companion messages across up to 120 days, enabling systematic evaluation of an AI companion's selective memory retrieval, profile fact extraction, trait inferencing, and restraint when history is unnecessary.
RIDE pushes a student model beyond its reinforcement-learning teacher by training student hidden representations to overshoot teacher representations along the layerwise residual vector between the teacher and its pre-learning base model, bypassing noisy output-token sampling.
By measuring student learning progress through divergence from initialization, this paper fits joint power laws that predict a student language model's peak test accuracy and learning speed from model scale and teacher accuracy before distillation begins.
Raven packages language models with domain tools into reusable worker units, using a central host agent to break user requests into workflow graphs, validate file-based handoffs before execution, and run subtasks in parallel.
CrossFit partitions source documents into separate groups called folds and scores questions using a solver trained on complementary folds, reducing same-source error feedback to lower false agreement and improve search accuracy.