Da Yin

Multi-turn, multi-modal interaction is a common interaction pattern in human–AI collaborative coding, where users iteratively refine implementations using both language and visual feedback. For example, in front-end development, users often combine textual instructions with visual artifacts such as sketches, mockups, and annotated screenshots. Despite its prevalence, such workflow remains largely overlooked: existing research primarily studied simplified single-turn or text-only settings. To address this gap, we introduce **FronTalk**, a benchmark for **multi-turn, multi-modal code generation** in front-end development. FronTalk has 100 multi-turn dialogues from diverse real-world websites. Each turn includes a textual instruction and an equivalent visual instruction. We further propose a novel *agent-based evaluation framework* that leverages a web agent to simulate user interactions and measure both implementation correctness and user experience of the generated websites. Evaluation of 20 models reveals two fundamental challenges: (1) a *forgetting issue*, where models often overwrite previously implemented features, and (2) a *visual interpretation gap*, where models struggle to translate visual inputs into functional requirements. To address these challenges, we propose ACECoder, which employs a web agent to autonomously critique implementations and verify compliance with prior instructions. This reduces the forgetting rate from 8.0–28.2\% to **nearly zero**. In terms of overall performance, ACECoder improves the textual feedback setting by up to **9.3\%** (56.0\%$\rightarrow$65.3\%) and also mitigates the visual interpretation challenge, boosting performance by up to **5.2\%** (55.0\%$\rightarrow$60.2\%) with visual feedback.

When a colleague starts explaining something you already understand, you interrupt them. This simple act, a listener taking control of the conversation, is natural in human communication but absent in current verbose LLM multi-agent systems. Current approaches address communication efficiency only from the speaker side, compressing messages before they are sent. We flip the perspective: rather than making speakers more concise, we let listeners decide when they have heard enough. We propose a new communication paradigm in which the listener can interrupt the speaker mid-generation. We find that LLMs, given this ability, are overconfident and interrupt too early, before receiving sufficient information. This finding motivates HANDRAISER, a learning method that predicts the right moment to interrupt based on estimated future reward and communication cost. We evaluate our framework on three multi-agent tasks: 2-agent text pictionary, 3-agent meeting scheduling, and 3-agent debate. HANDRAISER reduces communication cost by 32.2% over the non-interruptible baseline while achieving comparable or superior task performance, with interruption behavior that generalizes across different agents and tasks