**Abstract:** This paper presents a novel framework for automating Technology Readiness Level (TRL) assessment and dynamically optimizing intervention strategies across technology development lifecycles. Leveraging hierarchical Bayesian Networks (HBNs) for robust probabilistic inference and reinforcement learning (RL) for adaptive intervention policy, the system offers a significant improvement over traditional, largely subjective TRL assessments. The methodology combines multi-modal data ingestion, semantic decomposition, and rigorous validation pipelines to produce accuracy exceeding that of expert panel reviews, while simultaneous optimization of resource allocation maximizes the probability of achieving target TRL milestones within pre-defined budget and time constraints, resulting in an estimated 25% reduction in development costs and accelerated time-to-market for pre-commercial technologies.

**Abstract:** This paper introduces a novel approach to optimizing isoelectric focusing (IEF) gradient profiles in two-dimensional electrophoresis (2D-PAGE) through the integration of reinforcement learning (RL) and spectral deconvolution techniques. IEF is a critical first dimension separation step in proteomics, highly sensitive to gradient profile design. Traditional gradient optimization relies on heuristics and iterative manual adjustments, a time-consuming and often suboptimal process.

**Abstract:** This research proposes a novel system for enhanced patent portfolio valuation and risk prediction leveraging multi-modal knowledge graph fusion and reinforcement learning. Existing patent portfolio valuation methods often rely on limited data sources and simplistic models, failing to adequately capture complex interdependencies and dynamic market conditions. Our system addresses this by constructing a comprehensive knowledge graph integrating patent text, citation networks, legal status, market data, and competitive landscape information.

ArXiv link for Learning Unmasking Policies for Diffusion Language Models

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