# Gander 全双工全模态交互智能体技术报告发布并开源

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-08 08:00
- AIHOT 分数：57
- AIHOT 链接：https://aihot.news/items/cmttmozwp0ejerofp2q3jo16m
- 原文链接：https://arxiv.org/abs/2609.08977

## AI 摘要

作者发布 Gander 技术报告，这是一个端到端模型，在单一框架内统一全模态感知、实时交互与智能体能力，支持视频、语音、文本流式输入与随时打断的全双工交互。其采用 Cerebellum-Brain 协同框架，小脑负责实时交互与对话，大脑负责复杂推理与智能体任务，并基于流式 Thinker-Talker 架构实现低延迟；模型、代码和数据将开源。

## 正文

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.