Interpretable frame-to-knowledge-graph (Frame2KG) generation enables structured visual scene representation while supporting on-device inference to enhance privacy, improve interpretability, and minimise compute. We introduce Frame2KG-YC2, a synthetic, reproducible dataset derived from YouCook2 that pairs keyframes with schema-valid JSON knowledge graphs containing typed, spatially grounded entities and semantic predicates, alongside faithful textual paraphrases. Using this corpus, we fine-tune Qwen2.5-VL models (3B and 7B) with parameter-efficient LoRA adapters on attention layers (QKVO), with and without GateProj/Up/Down MLP projections. For evaluation and benchmarking, we propose a deterministic toolkit featuring two-stage node matching, an IoU gate followed by Hungarian assignment on blended spatial-semantic similarity, and comprehensive metrics spanning node/edge precision-recall-F1, matched-pair IoU, and structural validity. On a held-out test set, our models achieve Node F1μ up to 0.621 and Edge F1μ up to 0.208, with mean matched IoU of ≈0.61 and >98% schema conformity. We show that MLP gating consistently improves predicate accuracy and spatial grounding, while post-training quantisation maintains accuracy and improves deployability on edge hardware. We release the dataset, code, adapters, and evaluation toolkit to establish an open, interpretable baseline for future temporal and multi-view extensions.

Collaborative dialogue in multi-agent settings often requires interlocutors to integrate partially overlapping perceptual information in order to construct a shared representation of a dynamic environment. We introduce PAIR, a pilot conversational corpus designed to examine how humans coordinate under systematic perceptual asymmetry. The dataset comprises 15 dialogues in which participants observed the same activity from complementary egocentric and exocentric video perspectives and engaged in open-ended discussion to produce a joint account. All transcripts were manually verified and annotated with 42 dialogue act categories, enabling fine-grained analysis of interactional structure. Beyond descriptive statistics, PAIR supports examination of measurable conversational configurations, including turn distribution, participation symmetry, and dialogue act composition, which together provide structural indicators of how perspective integration unfolds in dialogue. Although intentionally lightweight, PAIR is positioned as a controlled benchmark for analysing collaborative dialogue mechanisms rather than a large-scale training resource. The corpus supports dialogue act classification, video-grounded dialogue modelling, and investigation of multi-agent reasoning under distributed perceptual access. By coupling dual-perspective grounding with explicit interactional annotation, PAIR offers a compact testbed for studying reconciliation dynamics in task-oriented dialogue.