STAR presents a failure-aware routing framework using a state-conditioned transition policy and an agent routing matrix combining expert routes with learned recoveries from execution traces to improve multi-agent spatiotemporal reasoning.
Benchmarking spatiotemporal reasoning in llms and reasoning models: Capabilities and challenges
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FM-CAC uses battery buffering and time-series foundation models for zero-shot carbon forecasting in a dynamic programming optimizer to reduce edge AI carbon emissions by up to 65.6% with near-maximum accuracy.
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STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning
STAR presents a failure-aware routing framework using a state-conditioned transition policy and an agent routing matrix combining expert routes with learned recoveries from execution traces to improve multi-agent spatiotemporal reasoning.
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FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models
FM-CAC uses battery buffering and time-series foundation models for zero-shot carbon forecasting in a dynamic programming optimizer to reduce edge AI carbon emissions by up to 65.6% with near-maximum accuracy.