TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
2026.POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization
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COEVO unifies correctness and multi-objective PPA optimization in a single evolutionary loop for LLM RTL generation, reporting 97.5% and 94.5% Pass@1 on VerilogEval/RTLLM benchmarks plus best PPA on 43 of 49 designs.
Dr. RTL's multi-agent framework with group-relative skill learning achieves 21% WNS and 17% TNS timing improvements plus 6% area reduction on 20 real-world RTL designs over commercial synthesis tools.
citing papers explorer
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TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation
COEVO unifies correctness and multi-objective PPA optimization in a single evolutionary loop for LLM RTL generation, reporting 97.5% and 94.5% Pass@1 on VerilogEval/RTLLM benchmarks plus best PPA on 43 of 49 designs.
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Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement
Dr. RTL's multi-agent framework with group-relative skill learning achieves 21% WNS and 17% TNS timing improvements plus 6% area reduction on 20 real-world RTL designs over commercial synthesis tools.