Automatic multi-agent workflow generation now targets execution time: LAWA cuts total makespan through graph edits aimed at critical-path bottlenecks while keeping competitive task performance across seven benchmarks.
Workflow selection for LLM agents is recast as a contextual bandit over a reusable pool, reaching an 81.37% average across five benchmarks while cutting MATH training-phase token overhead by at least 40.55%.
DAGO models each parent-workflow combination as a bandit arm represented by pretrained embeddings of its constituent workflows' code and prompts, uses a diagonal LinUCB policy to trade off predicted offspring quality against uncertainty-driven exploration, has an LLM generate a child through summary-guided fusion with the child's validation score as the reward, and across six benchmarks in mathematical reasoning, code generation, and question answering achieves the highest macro-average score among the evaluated baselines, improving over AFlow from 80.3 to 81.7 under matched validation-evaluation budgets while reducing aggregate search expenditure by 11.2%.
The work proposes InFlowOp, which prices every decision in one label-free cost that weighs how well an agent's competence meets a subtask's demands against how much that agent takes to run, using it before execution to bidirectionally set decomposition granularity and agent assignment and during execution to locate and locally correct faults, and introduces Braid, a benchmark whose tasks require multi-agent coordination, reporting gains up to +11.97% over single-agent baselines across eight domains and six backbones, with +9.64% from in-flow optimization.
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