KG-assisted supervised fine-tuning depends on evidence selection, context compilation, and data loading. We present KG-SFT Contracts, a provenance-aware interface that audits these stages and links model comparisons to their data and scoring records. In a retrospective Russian enterprise case, a pool of 484 graph candidates contributed no graph-origin distractors to the compiled corpus, and exact loader replay discarded 40 of 946 intended examples before splitting. Removing annotated non-support context restored every row while preserving questions, targets, and verified support. Downstream comparisons remained statistically inconclusive. Controlled checks on a public graph-bearing fixture show how cross-record validation detects unresolved graph references and conflicting source revisions alongside role-aware repair. The installable implementation includes fixture-level CPU measurements, incremental adoption guidance, and an official RuBQ schema-import check. The case illustrates how lineage and retention audits make adaptation pipelines inspectable before model effects are attributed to their configured inputs.

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.

Online advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users into visiting a corresponding webpage. Professional generation of banners requires creative and writing skills and a basic understanding of target products. The great variety of goods presented in the online market enforce professionals to spend more and more time creating new advertisements different from existing ones. In this paper, we propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. The important part of the approach is training on open data available online, which allows avoiding costly procedures of manual labeling. Collected open data consist of multiple subdomains with high data heterogeneity. The subdomains belong to different topics and vary in used vocabularies, phrases, styles that lead to reduced quality in adverts generation. We try to solve the problem of identifying existed subdomains and proposing a new ensemble approach based on exploiting multiple instances of a seq2seq model. Our experimental study on a dataset in the Russian language shows that our approach can significantly improve the quality of adverts generation.