Mango raises web agent success rates to 63.6% on WebVoyager and 52.5% on WebWalkerQA by bandit-based starting-point selection and memory, beating baselines by 7.3% and 26.8%.
The Twelfth International Conference on Learning Representations , year=
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AWM induces reusable workflows from agent experiences and provides them selectively to improve success rates by 24.6% on Mind2Web and 51.1% on WebArena while reducing steps taken.
LiSA improves AI guardrails lifelong by inducing conservative policies from sparse noisy failure reports via structured memory, conflict-aware rules, and posterior lower-bound gating.
citing papers explorer
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Mango: Multi-Agent Web Navigation via Global-View Optimization
Mango raises web agent success rates to 63.6% on WebVoyager and 52.5% on WebWalkerQA by bandit-based starting-point selection and memory, beating baselines by 7.3% and 26.8%.
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Agent Workflow Memory
AWM induces reusable workflows from agent experiences and provides them selectively to improve success rates by 24.6% on Mind2Web and 51.1% on WebArena while reducing steps taken.
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LiSA: Lifelong Safety Adaptation via Conservative Policy Induction
LiSA improves AI guardrails lifelong by inducing conservative policies from sparse noisy failure reports via structured memory, conflict-aware rules, and posterior lower-bound gating.