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 underutilizing available compute resources. We propose CoAct, a training-free framework that parallelizes agent workflows by casting execution as an online task allocation problem: CoAct prompts the LLM to generate a pool of discrete subtasks and performs online dispatch by selecting, whenever a worker becomes available, the next task that minimizes an incremental task-contrastive objective, encouraging high similarity among tasks executed on the same path (positive pairs) and low similarity across different paths (negative pairs) to reduce cross-worker interaction and synchronization. CoAct further supports speculative redundancy via selective re-execution to improve robustness and mitigate tail latency. 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 model.
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-optimization graph memory framework that models memory organization as query-aware evidence-forest construction. Specifically, our method consists of three key components:(1) candidate graph construction, which expands multi-view seed memories through schema and semantic relations to acquire query-aware original context; (2) evidence utility and activation costs, which decouples direct memory support from anchor-conditioned relation verification to suppress redundant or conflicting information; and (3) forest optimization, which jointly selects a forest-format memory context under a maximum evidence budget and its reliable relational structure. By organizing memory into a query-relevant subgraph, the method avoids substantial lifecycle cost and recovers low-similarity complementary evidence. Experimental results across four long-term multimodal memory benchmarks demonstrate significant improvements with different foundation models and establish a new Pareto frontier between accuracy and lifecycle cost.
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 underutilizing available compute resources. We propose CoAct, a training-free framework that parallelizes agent workflows by casting execution as an online task allocation problem: CoAct prompts the LLM to generate a pool of discrete subtasks and performs online dispatch by selecting, whenever a worker becomes available, the next task that minimizes an incremental task-contrastive objective, encouraging high similarity among tasks executed on the same path (positive pairs) and low similarity across different paths (negative pairs) to reduce cross-worker interaction and synchronization. CoAct further supports speculative redundancy via selective re-execution to improve robustness and mitigate tail latency. 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 model.
Yuyang Peng, Yanling Xu, Shuyi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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