Complex multi-hop question answering over text, tables, and images requires integrating heterogeneous evidence with inspectable reasoning. Single-LLM chain-of-thought is opaque, while planner–executor pipelines concentrate faults in a central plan, propagating early errors. We introduce DeALOG, a decentralized, central-planner-free multi-agent framework whose specialized agents, i.e., Table, Context, Visual, Summarizing, and Verification, coordinate only by reading and appending to a shared, typed natural-language log as persistent memory, with a lightweight scheduler rather than a planner. Rather than competing on raw accuracy, DeALOG targets what a central planner forfeits: robustness, since no single plan concentrates errors and evidence stays globally visible, and transparent, since the append-only log yields a provenance-anchored, human-readable reasoning trace. We position decentralized log-mediated coordination as a robustness-and-transparency primitive for multimodal reasoning

Multi-Modal Large Language Models (MLLMs) can now solve entire exams directlyfrom uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and credentials. Existing watermarking techniques either operate at the token level or assume control over the model’s decoding process, making them ineffective when students query proprietary black-box systems using instructor-provided documents. We present INTEGRITYSHIELD,a document-layer watermarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged. These watermarks consistently prevent MLLMs from answering shielded exam PDFs and encode stable, item-level signatures that can be reliably recovered from model or student responses. Across 30 question papers spanning STEM, humanities, and medical reasoning, INTEGRITYSHIELD achieves exceptionally high prevention (91-94% exam-level blocking) and strong detection reliability (89-93% signature retrieval) across four commercial MLLMs. Our demo showcases an interactive interface where instructors upload an exam, preview watermark behavior, and inspect pre/post AI performance and authorship evidence.

We introduce GameDAI, a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. Built on phase-based LangGraph sub-graphs, deterministic Quality Gates, and structured Pydantic schemas, GameDAI supports two template families encompassing 15 interaction mechanics across spatial reasoning, procedural execution, and higher-order Bloom’s Taxonomy objectives.Evaluated on 200 questions spanning five subject domains, the system achieves a 90% validation pass rate, 98.3% schema compliance, and 73% token reduction over ReAct agents 73,500 → 19,900 tokens/game) at 0.46 per game. Within this model configuration, these results suggest that phase-bounded architectural structure correlates more strongly with alignment quality than prompting strategy alone.Our demonstration lets attendees generate Bloom's-aligned games from natural language in under 60 seconds, inspect Quality Gate outputs at each pipeline phase, and browse a curated library of 50 games spanning all 15 mechanic types.