ROMA improves multimodal reasoning robustness to visual corruption via dual-pass RL optimization that avoids reward poisoning while preserving clean accuracy.
MedVIGIL evaluates medical vision-language models under broken visual evidence via clinician-supervised probes, revealing a 14.1-point gap between top models and radiologist reliability.
PLATO uses a pointer-network actor and GNN critic to handle open multi-agent reinforcement learning with unbounded agent and task spaces, achieving strong zero-shot generalization.
StraTA introduces trajectory-level strategies into agentic reinforcement learning via hierarchical rollout training, improving long-horizon decision-making and reaching 93.1% on ALFWorld.
LCDD constructs sparse, causally necessary subnetworks for SFT behaviors, and SFT-Eraser reverses them via activation-matched soft prompts without weight changes.
GazeWorld models radiologist eye-tracking as fixation trajectories through images to pretrain medical representations that achieve state-of-the-art diagnostic and gaze prediction accuracy without requiring real gaze data at inference.
Palette enables modular, efficient relaxation of LLM refusal behaviors for authorized domains via lightweight adaptation and parameter merging while preserving general safety.