Experiments on tool-augmented agent workloads show that CoAct improves per-step execution efficiency and resource utilization while achieving competitive or superior task accuracy, demonstrating that contrastive online dispatch can expose substantial parallelism in LLM-agent workflows without retraining the underlying...
Yuyang Peng, Yanling Xu, Shu-Yi Wang et al.· Proceedings of the 32nd ACM...· 1 citation
Organizing long-term memory for multimodal agents remains challenging because existing methods either suffer from expensive question-agnostic offline summaries or naive embedding similarity matching that introduces incomplete and redundant context. To address these issues, we propose GraphMemix, a combinatorial-optimiz...
LLM agents solve complex tasks by executing multi-step workflows that interleave LLM inference with external tool calls, yet execution efficiency is often the dominant bottleneck in real deployments because LLM-generated workflows are typically chain-structured and inherently sequential, limiting parallelism and underu...
Yuyang Peng, Yanling Xu, Shuyi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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