DeRAN converts black-box DRL policies into interpretable symbolic representations for O-RAN automation, retaining 78-87% of original performance while adding built-in transparency.
Sia: Symbolic interpretability for anticipatory deep rein- forcement learning in network control
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Chimera combines kernelized attention approximations with symbolic fusion mechanisms to enable high-fidelity neuro-symbolic inference inside commodity programmable switches.
citing papers explorer
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Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
DeRAN converts black-box DRL policies into interpretable symbolic representations for O-RAN automation, retaining 78-87% of original performance while adding built-in transparency.
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Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence
Chimera combines kernelized attention approximations with symbolic fusion mechanisms to enable high-fidelity neuro-symbolic inference inside commodity programmable switches.