Diffusion language models generate text through iterative denoising, allowing intermediate predictions to influence later denoising steps, a recurring practice we call trajectory self-feedback (TSF). TSF is adopted under inconsistent terminology, evaluated on incompatible benchmarks, and studied by disconnected communities, yet no prior review characterises it reproducibly. We present a systematic literature review under an evidence-based methodology: six pre-specified questions, a documented six-library search with snowballing, explicit selection and quality criteria, and a structured synthesis of 24 primary studies across seven clusters, with a released audit trail. We find: (i) a model-agnostic taxonomy resolves four operator families: Prediction-Space Concatenation, Confidence-weighted Path Signals, Reward-Reinforced Conditioning, and Correction-Targeted Feedback, that the literature conflates as self-conditioning; (ii) evidence of benefit is real but fragmented across incomparable settings; (iii) within our search window, no study jointly varies the TSF mechanism and measures faithfulness on knowledge-intensive tasks; and (iv) a discrete/continuous asymmetry consistent with the sampling-wall hypothesis is visible but never tested. We conclude with eight open problems, a dependency map, and review-derived diagnostics (Commitment Index, Correction Capacity) and a Standardised Evaluation Protocol (SEP-DLM) to make TSF measurable across quality, correction, trajectory commitment, and faithfulness.

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.