Conversational AI is a central application of NLP, yet ensuring high response quality remains challenging due to the inherently subjective nature of user satisfaction. Dialogue evaluation can be performed manually—through expert or user ratings—or automatically, using methods that aim to predict quality scores consistent with human judgment. In this work, we present a reference-free automatic dialogue evaluation system that predicts user ratings from a dataset of real human–chatbot interactions collected during the Alexa Prize Socialbot Grand Challenge 5, combining multiple complementary models to enhance correlation with human scores. Experimental results indicate that the model that achieves the highest Pearson correlation with users’ ratings is an XGBoost regression model that combines different features such as conversation length, engineered flags capturing conversation characteristics, predictions from an Encoder-based Panel of Experts (PoE), and instruction-based outputs from a fine-tuned LLM. The overall Pearson Correlation on the eval set is 0.404, which is competitive with prior work trained on an order of magnitude more dialogues, albeit using different datasets and system configurations.

An ongoing challenge in multimodal language research is creating and interpreting dialogues that preserve visual and cultural consistency across turns. We introduce DREAM (Dialogue to REAlistic Multicultural Image Sequences), a multicultural multimodal resource that ties dialogues grounded in explicit persona profiles to photorealistic, storyboard-like image sequences. Each of the 1,000 dialogues includes two rich persona profiles (structured traits plus descriptive language), two matching photorealistic portraits, and a collection of scene-level images depicting key dialogue moments. The pipeline integrates profile augmentation, culturally-sensitive prompt engineering, and turn selection to craft cohesive visual narratives, promoting character consistency across images. This is accomplished through a controlled generation process employing large language and image models. Beyond dialogue grounding, DREAM supports appearance-based demographic perception and culture-aware rendering: models can be evaluated on their ability to (i) perceive age, gender presentation, and broad ethnicity appearance clusters from profile portraits, and (ii) maintain these characteristics in dialogue scenes. We provide a unified JSON format integrating profiles, dialogue text, and visual turns, facilitating research on visually anchored dialogue understanding, consistency, and generation. A dual evaluation protocol combines human judgments (realism, coherence, consistency, and demographic perception) with automated portrait analysis via GPT-5. Ethical considerations, limitations, and recommended applications are discussed.

Industry stakeholders are willing to incorporate AI systems in their pipelines, therefore they want agentic flexibility without losing the guaranties and auditability of fixed pipelines. This paper describes ORCHESTRA, a portable and extensible microservice architecture for orchestrating customizable multimodal AI workflows across domains. It embeds Large Language Model (LLM) agents within a deterministic control flow, combining reliability with adaptive reasoning. A Dockerized Manager routes text, speech, and image requests through specialist workers for ASR, emotion analysis, retrieval, guardrails, and TTS, ensuring that multimodal processing, safety checks, logging, and memory updates are consistently executed, while scoped agent nodes adjust prompts and retrieval strategies dynamically. The system scales via container replication and exposes per-step observability through open-source dashboards. We ground the discussion in a concrete deployment: an interactive museum guide that handles speech and image queries, personalizes narratives with emotion cues, invokes tools, and enforces policy-compliant responses. From this application, we report actionable guidance: interface contracts for services, where to place pre/post safety passes, how to structure memory for RAG, and common failure modes with mitigations. We position the approach against fully agentic and pure pipeline baselines, outline trade-offs (determinism vs. flexibility, latency budget), and sketch near-term extensions such as sharded managers, adaptive sub-flows, and streaming inference. Our goal is to provide a reusable blueprint for safely deploying agent-enhanced, multimodal assistants in production, illustrated through the museums use case.

Recent developments in Multimodal Large Language Models (MLLMs) have provided novel insights into Speech Emotion Recognition (SER). However, combining high-dimensional speech signals with textual tokens can lead to a rapid growth in input tokens, increasing computational costs and inference times. This “token overload” also risks shadowing essential textual cues, affecting the reasoning capabilities of the language model and diluting emotional information crucial to accurate SER. In this paper, we explore different token drop methods that mitigate excessive token counts while preserving both emotional nuances and the core linguistic capabilities of the model. Specifically, we compare various pooling approaches to produce a compact representation. Our preliminary findings suggest that these techniques can reduce computational costs without decreasing SER accuracy.