Multi-agent systems built on large language models (LLMs) require many coordination choices that are difficult to fix a priori: which skill protocol to invoke, which agent role should perform a subtask, which model to bind to each role, how roles should interact, when to use retrieval or verification, and when to omit a step entirely. These choices interact with task regime and operational constraints, so static pipelines and one-off model comparisons provide only a limited view of the design space. This paper introduces AgensFlow, an open-source framework that treats multi-agent coordination as an online policy-learning problem under partial observability. The framework makes coordination decisions observable and learnable from repeated trajectories, rather than treating skill, role, model, topology, and evaluation choices as fixed pipeline design. AgensFlow is evaluated on two corpora: distributed-systems incident tasks and security-advisory tasks. The evaluation shows three main results: learned routing reaches a higher-quality operating point than a fixed pipeline baseline on coordination-heavy classes; skip:X isolates topology compression as a meaningful part of the substrate; and warm-started policy graphs can reduce exploration cost while preserving plateau quality. Overall, the results support that learned, auditable routing can improve coordination-heavy multi-agent workflows over static wiring.

Neural Information Processing Systems http://nips.cc/

A straightforward example is as follows.

This is a clear example of exploration-then-exploitation behaviour with exactly one phase change in the process.

This paper considers the safe exploration problem in both (Bayesian, Gaussian Process) optimization and reinforcement learning settings. In this work, as with some previous works, which states are safe is treated as unknown, but it is assumed that safety is determined by a sufficiently smooth constraint function, so that evaluating (exploring) a point may be adequate to ensure that nearby points are also safe on account of smoothness. Perhaps the most significant aspect of this work is the way the problem is formulated. Some previous works allowed unsafe exploration, provided that a near-optimal safe point could be identified; other works treated safe exploration as the sole objective, with finding the optimal point within the safe region as an afterthought. The former model is inappropriate for many reinforcement learning applications in which the learning may happen on-line in a live robotic platform and safety must be ensured during execution; the latter model is simply inefficient, which is in a sense the focus of the evaluation in this work.