Researchers have developed PACE (Planning with Adaptive Cognitive Effort), a new framework designed to make reasoning-enhanced large language models more practical for embodied systems by addressing prohibitive inference delays. PACE introduces an Interleaved Think-Act architecture that allows reasoning and action execution to occur concurrently, alongside a Dynamic Budget Allocator that adjusts reasoning token budgets based on available execution time. Tested on the Robotouille benchmark with the Qwen3-8B-AWQ model, PACE demonstrated a 67% improvement in success rate over the ReAct+Think baseline and achieved a 6.9x acceleration in thinking time, effectively hiding 66.8% of thinking time within execution windows. AI
IMPACT Enables reasoning models to operate in latency-sensitive embodied domains, potentially accelerating robotics and real-world AI applications.
RANK_REASON The cluster contains an academic paper detailing a new framework and methodology for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Dynamic Budget Allocator
- Interleaved Think-Act
- PACE
- Planning with Adaptive Cognitive Effort
- Qwen3-8B-AWQ
- ReAct+Think
- Robotouille
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