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This paper proposes SG-WAM, a self-guided framework for learning geometry-aware action-conditioned world models directly in policy-derived representation space. It achieves state-of-the-art success rates on LIBERO and LIBERO-Plus benchmarks, outperforming strong baselines in real-world evaluations.
Proposes Self-Guided Test-Time Training (S-TTT), where the model identifies relevant evidence spans in long contexts for adaptation, achieving up to 15% relative improvement on long-context reasoning benchmarks.
InSight presents a framework for autonomous skill acquisition in vision-language-action (VLA) models by enabling steerability at the primitive-action level and using a VLM-guided data flywheel to generate demonstrations, achieving manipulation tasks like block flipping and pouring without human demonstrations.
CS 6120 is a PhD-level online self-guided course on advanced compilers, covering intermediate representations, data flow, optimizations, and including paper readings and implementation tasks using LLVM and Bril.