Critical Reading Exam
🎓 Examination: PhD Critical Reading Exam (Doctoral Commission, June 2026)
👤 Candidate: Stergios Konstantinidis • Department of Information Systems (DESI), HEC Lausanne, University of Lausanne (UNIL)
📖 Dissertation:Topic: Large Language Models for Historical Document Intelligence: Retrieval-Augmented Generation, Automated Evaluation, and Agentic Architectures
🔗 Repositories: GitHub CRE Repository • Overleaf Exam Workspace
📥 Primary Examination Documents: Exam Paper (PDF, 8 Pages) • Presentation Deck (PDF, 13 Slides) • Presentation Deck (PPTX, 11 MB) • View Presentation Slides →
1. The 5 Literature Review Articles (Critical Reading Exam)
The exam selection systematically examines five external cornerstone publications spanning four interconnected theoretical pillars:pillars in historical document intelligence:
| Article | Primary Contribution | Addressed Architectural Vulnerability | |
|---|---|---|---|
| Tran et al. (JCDL 2024) RAG for Historical Newspapers |
First end-to-end RAG system over multilingual historical newspapers (NewsEye); proposed hybrid dense retrieval and |
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| Guan et al. ( |
Principled synthetic OCR degradation generation and test-time adaptation targeting proper nouns and out-of-vocabulary entities. | Severe training data scarcity for historical periods; catastrophic forgetting of rare historical entities. | |
| Gu et al. (The Innovation 2026) A Survey on LLM-as-a-Judge |
Systematic taxonomy of LLM judges: position bias, verbosity bias, self-enhancement bias, and calibration protocols. | Unreliable evaluation in ground-truth-free RAG evaluation pipelines. | Establishes the theoretical and methodological foundation for validating automated evaluation metrics in |
| Sun et al. (EMNLP 2025) DocAgent: Multi-Modal Long-Context Framework |
Agentic architecture featuring selective retrieval, multimodal document inspection tools, and iterative answer verification agents. | Context window limits; inability of pure text LLMs to resolve visual layout artifacts. | |
| Lewis et al. (NeurIPS 2020) Foundational Retrieval-Augmented Generation |
The foundational dual-encoder dense retrieval and seq2seq generator architecture trained end-to-end with latent documents. | Hallucination in closed-book parametric memory; inability to update knowledge. | The theoretical origin of RAG; reveals how modern assumptions (clean English Wikipedia) break down when deployed over |
2. Theoretical & Architectural Synthesis
The Foundational Tension in Historical Document Intelligence
Across all reviewed articles and our doctoral systems,articles, a persistent architectural tension emerges: the fundamental assumptions that make LLM-based document intelligence effective are systematically violated by the historical archives that most urgently need it:
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Assumption of Orthographic Cleanliness: Standard RAG models assume high-quality input text. In
18th-to-20th-centuryhistorical newspapers, character error rates (CER) frequently exceed 15–25% due to font fading, broken typefaces, ink bleed, and complex multi-column typography.OurDocEng andGuan et al. research demonstrates that denseDense vector spaces must therefore be explicitly regularized against OCR degradation. -
Assumption of Monolingual Modernity: Foundational retrievers are optimized for contemporary English.
RegionalArchivalarchives (e.g., Swiss historical press)collections encompass archaicFrench,dialects,SwisshistoricalGermanspellingdialectal shifts,variants, and evolving syntactic conventions acrossthreemultiple centuries.As shown inTran et al. and ourIP&M pipeline, multilingualMultilingual embeddings combined withmetricrobusttreespatial indexing are essential to bridge temporal linguistic drift. -
Assumption of Annotation Abundance: Modern benchmarks rely on millions of human QA annotations. Archival collections possess virtually zero labeled question-answering pairs. Reconciling this gap requires robust ground-truth-free evaluation frameworks
(Tran et al.,Gu et al.)carefully calibrated against promptbiasbias, verbosity artifacts, andverbositypositionalartifacts.skew. -
Assumption of Flat Screen Interfaces: Traditional document retrieval presents flat lists of snippets. Navigating
centuries ofinterrelated historical narratives demands agenticreasoningreasoning,(DocAgent)multimodal layout inspection, andimmersiveinteractivespatial computing (AppleVision Cultural Archives)exploration that turn passive archivalretrieval into active sensemaking.
3. Research Milestone & Examination Context
- Doctoral Commission: Faculty of Business and Economics (HEC Lausanne), University of Lausanne.
- Examination Date: June 2026.
- Outcome: Passed
4. The 5 Doctoral Papers in this Research Portfolio ("Our Papers")
The doctoral dissertation synthesizes five authored publications addressing the architectural failure modes identified in the literature, evaluated directly on the multi-century collections of the Bibliothèque Cantonale et Universitaire de Lausanne (BCUL):