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Published on Oct 13 · Submitted by Keru Chen on Oct 15 Abstract Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing information, and incorporating teammates' contributions into decisions. Training these behaviors is challenging because final task outcomes provide limited feedback on the value of individual messages.
1 Introduction Mitigating global climate change by reducing carbon emissions demands a rapid transition toward renewable energy sources such as wind and photovoltaic power [1]. While wind power offers a scalable and low-carbon energy solution, its inherent intermittency and volatility present significant challenges to power grid stability [2].
We present a systematic photonic filter design approach by deploying pole-zero optimization. The filter transfer function is derived from its specifications by formulating closed-form optimization objective functions and subsequently translating them into optical design parameters. Two distinct filter examples, namely Chebyshev and elliptic filters, are considered for the design and validation.
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