End-to-end Listen, Look, Speak and Act

• URL: http://arxiv.org/abs/2510.16756v1
• Date: Sun, 19 Oct 2025 08:45:46 GMT
• Title: End-to-end Listen, Look, Speak and Act
• Authors: Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Chao Zhang,
• Abstract summary: ELLSA represents a step toward more natural and general interactive intelligence, contributing to the broader pursuit of artificial intelligence.<n>At its core is a novel SA-MoE (Attention Mixture-of-Experts) that routes each modality to specialized experts fuses them through a unified attention backbone.
• Score: 22.047534228540783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human interaction is inherently multimodal and full-duplex: we listen while watching, speak while acting, and fluidly adapt to turn-taking and interruptions. Realizing these capabilities is essential for building models simulating humans. We present ELLSA (End-to-end Listen, Look, Speak and Act), which, to our knowledge, is the first full-duplex, end-to-end model that simultaneously perceives and generates across vision, text, speech, and action within a single architecture, enabling interaction patterns previously out of reach, yielding more natural, human-like behaviors. At its core is a novel SA-MoE architecture (Self-Attention Mixture-of-Experts) that routes each modality to specialized experts and fuses them through a unified attention backbone. This provides a generalizable solution for joint multimodal perception and concurrent generation, leveraging strong pre-trained components while enabling efficient modality integration and mitigating modality interference. On speech-interaction and robot-manipulation benchmarks, ELLSA matches modality-specific baselines, while uniquely supporting advanced multimodal and full-duplex behaviors such as dialogue and action turn-taking, defective instruction rejection, speaking-while-acting, context-grounded visual question answering, and action barge-ins. We contend that ELLSA represents a step toward more natural and general interactive intelligence, contributing to the broader pursuit of artificial general intelligence. All data, code and model checkpoints will be released upon acceptance.

Related papers

• MIBURI: Towards Expressive Interactive Gesture Synthesis [62.45332399212876]
  Embodied Conversational Agents (ECAs) aim to emulate human face-to-face interaction through speech, gestures, and facial expressions.<n>Existing solutions for ECAs produce rigid, low-diversity motions that are unsuitable for human-like interaction.<n>We present MIBURI, the first online, causal framework for generating expressive full-body gestures and facial expressions synchronized with real-time spoken dialogue.
  arXiv  Detail & Related papers  (2026-03-03T18:59:51Z)
• U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation [48.6868174403074]
  We introduce U-Mind, the first unified system for high-intelligence multimodal dialogue.<n>It supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop.<n>We show that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks.
  arXiv  Detail & Related papers  (2026-02-27T07:07:02Z)
• Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models [80.28579390566298]
  We introduce Interact2Ar, a text-conditioned autoregressive diffusion model for generating full-body, human-human interactions.<n>Hand kinematics are incorporated through dedicated parallel branches, enabling high-fidelity full-body generation.<n>Our model enables a series of downstream applications, including temporal motion composition, real-time adaptation to disturbances, and extension beyond dyadic to multi-person scenarios.
  arXiv  Detail & Related papers  (2025-12-22T18:59:50Z)
• InteracTalker: Prompt-Based Human-Object Interaction with Co-Speech Gesture Generation [1.7523719472700858]
  We introduce InteracTalker, a novel framework that seamlessly integrates prompt-based object-aware interactions with co-speech gesture generation.<n>Our framework utilizes a Generalized Motion Adaptation Module that enables independent training, adapting to the corresponding motion condition.<n>InteracTalker successfully unifies these previously separate tasks, outperforming prior methods in both co-speech gesture generation and object-interaction synthesis.
  arXiv  Detail & Related papers  (2025-12-14T12:29:49Z)
• VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting [66.90028121194636]
  Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm.<n>VITA-E is a novel embodied interaction framework designed for both behavioral and nearly real-time interruption.
  arXiv  Detail & Related papers  (2025-10-21T17:59:56Z)
• FLEXI: Benchmarking Full-duplex Human-LLM Speech Interaction [49.83226596963294]
  Speech-computer human interaction enables real-time spoken dialogue systems.<n>Modelling and benchmarking these models remains a fundamental challenge.<n>We introduce FLEXI, the first benchmark for full-human spoken interaction.
  arXiv  Detail & Related papers  (2025-09-26T11:57:42Z)
• MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network [6.304608172789466]
  The proposed Multi-modal Attention for Valence-Arousal Emotion Network (MAVEN) integrates visual, audio, and textual modalities.<n>MAVEN uses modality-specific encoders to extract features from synchronized video frames, audio segments, and transcripts.<n>The architecture captures the subtle and transient nature of emotional expressions in conversational videos and improves emotion recognition in real-world situations.
  arXiv  Detail & Related papers  (2025-03-16T19:32:32Z)
• Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking Capabilities [93.09944267871163]
  FullDuplexBench is a benchmark that systematically evaluates key interactive behaviors.<n>By releasing our benchmark code we aim to advance spoken dialogue modeling and the development of more natural and engaging SDMs.
  arXiv  Detail & Related papers  (2025-03-06T18:59:16Z)
• DeepInteraction++: Multi-Modality Interaction for Autonomous Driving [80.8837864849534]
  We introduce a novel modality interaction strategy that allows individual per-modality representations to be learned and maintained throughout.<n>DeepInteraction++ is a multi-modal interaction framework characterized by a multi-modal representational interaction encoder and a multi-modal predictive interaction decoder.<n>Experiments demonstrate the superior performance of the proposed framework on both 3D object detection and end-to-end autonomous driving tasks.
  arXiv  Detail & Related papers  (2024-08-09T14:04:21Z)
• Joint Multimodal Transformer for Emotion Recognition in the Wild [49.735299182004404]
  Multimodal emotion recognition (MMER) systems typically outperform unimodal systems. This paper proposes an MMER method that relies on a joint multimodal transformer (JMT) for fusion with key-based cross-attention.
  arXiv  Detail & Related papers  (2024-03-15T17:23:38Z)
• Dyadic Interaction Modeling for Social Behavior Generation [6.626277726145613]
  We present an effective framework for creating 3D facial motions in dyadic interactions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach. Experiments demonstrate the superiority of our framework in generating listener motions.
  arXiv  Detail & Related papers  (2024-03-14T03:21:33Z)
• AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Recognition in Group Conversations [39.79734528362605]
  Multimodal Attention Network captures cross-modal interactions at various levels of spatial abstraction. AMuSE model condenses both spatial and temporal features into two dense descriptors: speaker-level and utterance-level.
  arXiv  Detail & Related papers  (2024-01-26T19:17:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.