Retrieval-augmented generation (RAG) performs well for localized factual queries but struggles with complex questions requiring multi-section evidence integration. Graph-based approaches introduce relational structure, yet their practical integration into QA pipelines involves significant query-time overhead. We present a practical KG-augmented RAG (KG-RAG) design that builds a knowledge graph offline with an LLM, converts graph communities into reusable summaries, and retrieves these summaries jointly with textual evidence at query time. We compare dense RAG, pure GraphRAG, and the proposed hybrid on two benchmarks representing complementary retrieval paradigms: QASPER (intra-document reasoning over scientific papers) and ObliQA (cross-document reasoning over regulatory texts). Results show that pure GraphRAG does not consistently outperform dense retrieval, whereas the hybrid configuration systematically improves relevance, correctness, and completeness while maintaining substantially lower latency than full graph-based inference.

Tables are central to business document workflows, but practical table-processing tasks vary substantially in structure, content, and required action. We compare eight ReAct-agent configurations built on the same Qwen3.6-35B-A3B model. The design crosses four tool profiles - Minimal, Search, Spreadsheet, and Full - with the presence or absence of Markdown-based procedural skills. We evaluate the configurations on 40 tasks from company workflows, FinQA, and SpreadsheetBench. Across three complete replicates, comprising 960 agent-task episodes, Full with skills obtains the highest aggregate success rate of 75.0% and a mean score of 0.8061. Full without skills reaches 65.0%, while Spreadsheet without skills reaches 73.3%. Aggregated across tool profiles, skills increase success from 69.4% to 72.3%, but their effect is heterogeneous: negative for Minimal and Spreadsheet and positive for Search and Full. Overall success remains stable across replicates at 70.3%, 70.9%, and 71.3%, although individual configuration results vary more substantially. These findings suggest that procedural guidance is most promising when an agent must coordinate a broad tool environment rather than as a universal augmentation.

Preparing graduate students for effective professional communication remains a central goal of higher education, yet consistently assessing the quality of presentation slide decks - particularly in fast-growing AI/ML programs - poses significant challenges.We introduce SlideGuard, an evaluation agent that assesses slide decks against a comprehensive framework of expert-defined criteria using a visual language model.The criteria, developed in collaboration with domain experts, span visual design, narrative coherence, and argumentative structure.SlideGuard delivers explicit, interpretable justifications for its scoring decisions, and its content-hash-based caching enables efficient re-evaluation after incremental edits, reducing the time educators spend on slide deck evaluation and accelerating feedback delivery to students.We evaluate the approach on a dataset of 150 annotated slide decks and show that it detects the majority of expert-identified issues, with stronger results on structural and visual criteria and known limitations on subjective dimensions such as research quality.SlideGuard is released under the Apache 2.0 license and is available on GitHub,[https://github.com/Industrial-AI-Research-Lab/SlideGuard] including all criterion prompts, configuration files, and evaluation scripts to facilitate replication.