SquidAgent: Parallelize Wisely, Coordinate Efficiently
LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency.
Key points
- In principle, parallelizing work across multiple agents should yield near-linear speedups.
- We thus derive a principled decision criterion: a layer should be parallelized only when its critical-path cost, plus re-exploration and alignment overheads, is lower than the corresponding serial cost.
- Building on this token-based criterion, we propose SquidAgent.
- Empirically, SquidAgent achieves a 2.2$\times$ mean throughput improvement and a 2.6$\times$ mean wall-time speedup over Claude Code, and a 2.0$\times$ throughput improvement over the strongest multi-agent baseline.
Sources (1)
- [1]SquidAgent: Parallelize Wisely, Coordinate EfficientlyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 04:35 PM
LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency.
In principle, parallelizing work across multiple agents should yield near-linear speedups.
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