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Research and Engineering Notes
Write down the decisions that shape dependable systems.
Notes on AI reliability, retrieval evaluation, learning signals, research method, and exploratory scientific learning — written to expose reasoning, not pad the portfolio.
ORCID iD · reports, preprints, and DOI-linked outputs ↗DOI-Linked Technical OutputsEarly research outputs deposited with persistent DOI identifiers. These are non-peer-reviewed technical preprints and reports.04 outputs
01Technical preprint · non-peer-reviewedFieldTrace-OCR: A Reliability-Oriented Evaluation Protocol for OCR-Based Invoice Parsing PipelinesReliability-oriented OCR invoice-parsing evaluation protocol with traceability and failure analysis.10.5281/zenodo.20669709↗02Technical report · non-peer-reviewedA Lightweight Multilingual Semantic Search Pipeline for Document Discovery and Question RoutingA modular multilingual document-discovery and routing pipeline spanning retrieval, ranking, and evaluation choices.10.5281/zenodo.20652874↗03Technical report · non-peer-reviewedTemporal Context and Momentum-Aware Reward Shaping in Reinforcement Learning: An Auditable Exploratory Study of AirRaid and MountainCarAn exploratory reinforcement-learning report focused on temporal context, reward shaping, and auditable benchmark interpretation.10.5281/zenodo.20669675↗04Technical preprint · non-peer-reviewedA Retrieval-Augmented Financial Advisory Prototype with Evidence-Gated Response GenerationA RAG prototype with retrieval, response gating, abstention boundaries, and non-product positioning.10.5281/zenodo.20652567↗
9 min read
Evaluating AI reliability beyond headline accuracy
Why production AI evaluation must cover the complete decision pipeline: ingestion, extraction, transformation, validation, review, and operational constraints.
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From keyword matching to semantic retrieval
Why TF-IDF remains useful, where dense embeddings help, and how retrieval quality should be evaluated before generation is added.
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What sparse rewards teach us about system design
Reward shaping is not a shortcut. It is an interface-design problem between an objective and a learning algorithm.
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