route-each-agent-step-predicted-task-progress-gain TEC-0308 · proposedRoute each agent step by predicted task-progress gain
Scores intermediate workflow state into a progress value, predicts each candidate model's progress gain from structured and semantic features, and picks the model maximizing gain minus time/cost-budget penalties at every dispatch step.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Scores intermediate workflow state into a progress value, predicts each candidate model's progress gain from structured and semantic features, and picks the model maximizing gain minus time/cost-budget penalties at every dispatch step. Filed by the drafting stage from ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs (2608.25992) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Pass rate / citation precision, energy in Joules and wall-clock time against single-model policies, MasRouter (one-shot), CASCADIA (escalation) and an Educated Guessing baseline on four benchmarks, with a fixed long-term energy budget marking feasible methods; best or near-best among budget-satisfying methods on 3 of 4 datasets, and 3.5 points below CASCADIA on MATH-500 at lower energy.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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