arXiv:2508. 09521v3 Announce Type: replace-cross Abstract: Emotional support conversations require more than fluent responses.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608.21925v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. How...
arXiv:2607. 21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new level that is complex emotion understanding with advanced video understanding abilities and natural language description.
The paper introduces the Affective Flow Language Model (AFlow), which treats multi‑turn emotional support conversations as an evolving affective utility flow along dialogue trajectories. AFlow searches diverse support paths, estimates utilities of intermediate states, and employs Affective Flow Preference Optimization (AFPO) to propagate downstream preference signals to earlier states, enabling consistent strategy transitions. Experiments on ExTES and ESConv demonstrate improved strategy alignment, response diversity, and generation quality across various model settings.
arXiv:2602. 23802v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture the complexity and subjectivity of human emotions.
SocialRL is a multi-turn reinforcement learning framework that refines large language models’ social intelligence. It uses PPO to propagate delayed outcome rewards across turns, enabling long‑horizon planning, and introduces six process reward dimensions—such as goal advancement and relational attunement—to capture the goal‑relationship trade‑off. A reward model provides fine‑grained scoring and a stage‑aware weight schedule prioritizes relationship building early, goal pursuit mid‑way, and balanced closure later, yielding an average 9.2 percentage‑point improvement in goal achievement across multiple social‑dialogue benchmarks.
arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.