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Extracts reusable workflows from past task trajectories to guide future web actions. Supports offline demonstrations and online experience, improving long-horizon navigation without manually written routines.

Separates web-agent planning from action execution and trains the planner on plans inferred from successful trajectories. Reports 57.58% success on WebArena-Lite and 81.36% on text-only WebVoyager.

Evaluates GPT-6-Astra as an embodied policy across 12 robotic benchmarks, examining in‑context learning, hybrid System‑2/System‑1 control, and real‑world deployment gaps; finds strong benchmark competence but practical limits from demonstration cost, latency, and failure recovery.

Learns executable language-conditioned local navigation by pretraining on instruction-conditioned Internet egocentric videos, aligning learned visual dynamics with simulated action labels, then jointly fine-tuning for navigation. Achieves high success rates on short-horizon benchmarks (86.5% OVON, 94.9% HabitatGS-LN) with real-robot validation and released code/data.