SemiFA is a four-agent LangGraph pipeline that combines DINOv2 and LLaVA image analysis with SECS/GEM telemetry and vector retrieval to produce complete FA reports in 48 seconds.
Adam: A method for stochastic optimization,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Frame-aligned fusion of Canary and WavLM encoders, with WavLM temporally prepared via learnable strided convolution, outperforms other fusion strategies and reaches Eval RMSE 24.96 and Corr 0.796 on non-intrusive intelligibility prediction.
citing papers explorer
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SemiFA: An Agentic Multi-Modal Framework for Autonomous Semiconductor Failure Analysis Report Generation
SemiFA is a four-agent LangGraph pipeline that combines DINOv2 and LLaVA image analysis with SECS/GEM telemetry and vector retrieval to produce complete FA reports in 48 seconds.
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Frame-Aligned Fusion of Canary and WavLM for Non-Intrusive Intelligibility Prediction of Hearing-Aid-Processed Speech
Frame-aligned fusion of Canary and WavLM encoders, with WavLM temporally prepared via learnable strided convolution, outperforms other fusion strategies and reaches Eval RMSE 24.96 and Corr 0.796 on non-intrusive intelligibility prediction.