Gander combines real time chat and smart agent tasks across media
Omni Interaction Agent Technical Report
Artificial IntelligenceMachine LearningMultimediaSound
Summary
People often interact with digital assistants one step at a time, but this can feel unnatural and slow. The authors created Gander, a smart system that listens and talks continually, using video, speech, and text all at once for smoother conversations. It has two parts: one that handles quick, ongoing chatting and another that does complicated thinking and problem solving. Gander can handle interruptions and even ask questions on its own, making it more like a real conversation partner. The creators also tested Gander’s abilities with real people and difficult situations like noisy rooms and group talks.
omni perceptionfull duplex interactionmulti modal inputconversational AIagentic capabilitiesrealtime interactionThinker-Talker architectureCerebellum-Brain frameworktoken streamagent orchestration
Authors
Orantqing, Shengpeng Ji, Junlong Tong, Jialong Zuo, Dongjie Fu, Di Cao, Yangzhuo Li, Shangda Wu, Franz, Evan, Theron Veyra, Changhao Pan, Jingyu Lu, Dongchao Yang, Zhifei Xie, Yang Tan, Xiaoyu Shen, Xiaoda Yang, Wenfu Wang, Teddysun, Steveyves, Zhou Zhao, Bryanytian
Abstract
In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities within a single framework. In contrast to turn-based conventional paradigms, Gander continuously receives streaming inputs across multiple modalities, including video, speech, and text, enabling natural full-duplex interaction in both everyday conversations and complex workflow-oriented agent scenarios. Users can interrupt the model at any time, while the model can also proactively provide intermediate feedback or ask follow up questions. To natively support these capabilities, Gander adopts two key architectural designs: 1) It employs a Cerebellum-Brain collaborative framework, in which the Cerebellum is responsible for realtime interaction and omni conversational capabilities, while the Brain handles complex reasoning and higher-level agentic tasks. The two components interact continuously through tool calling and the agent orchestration runtime. 2) The Cerebellum is built upon a streaming Thinker-Talker architecture, user inputs and model outputs are further flattened into an ordered token stream at the chunk level, providing a unified representation for low latency, continuous interaction. We conduct comprehensive evaluations of Gander across four dimensions: conversational ability, omni understanding, interactive capability, and agentic intelligence. Internal human evaluations demonstrate that Gander maintains the natural and expressive spoken dialogue capabilities of SOTA open source models while achieving competitive performance in omni interaction. Gander also demonstrates robustness in challenging real-world scenarios, including background noise interference, multi-party interactions, and backchannel communication. We release Gander together with its models, code, and data to facilitate further research and development in the community.