A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection

Unisound Launches and Open-Sources U2-Decision: Matching the Right Task with the Right Intelligence

Today, Unisound officially launches and open-sources U2-Decision, a decision foundation model with 4B parameters in this open-source release. Positioned as the decision layer for multi-model, multi-Agent, and multi-tool collaboration, U2-Decision focuses on three questions: Who should handle the task? Can the task be executed? What path should be taken to complete it? Its three corresponding core capabilities are intelligent model routing, safety decision-making, and workflow routing.

Unisound Launches U2-Flash: Driven by Post-training × RSI, A New-Generation High-Performance Model for Real-World Tasks Debuts

Today, Unisound officially launches U2-Flash.Rather than being a lightweight efficiency version of U2, U2-Flash represents a new-generation intelligent model designed for real-world productivity.As a high-density intelligence model, U2-Flash leverages systematic post-training to unlock advanced reasoning and autonomous execution capabilities. It combines the robust capabilities of a flagship model with Flash-level responsiveness, delivering a stronger, faster, and more cost-efficient solution for real-world productivity scenarios.

When OCR Learns to Rebuild: Unisound U1-OCR Converts Images and PDFs into Editable PowerPoint Files in One Click, Ushering in a New Era of Intelligent Document Reconstruction

Recently, Unisound upgraded its U1-OCR foundation model for document intelligence and officially launched the IMG2PPT intelligent conversion feature. Built on the U1-OCR document intelligence engine, IMG2PPT reconstructs content from static JPG, PNG, and PDF files as natively editable PowerPoint presentations. Text boxes, images, lists, shapes, tables, and other elements become native, editable PowerPoint objects. This marks OCR's progression from single-purpose content recognition to a new stage of intelligent understanding and structural reconstruction.

Unisound Officially Launches U2-RadiMed, Ushering Medical Imaging AI into the Era of

As the first domestic imaging foundation model to deeply integrate medical image understanding, report generation, clinical reasoning and decision‑making, and visual question answering in a full‑chain solution, U2‑RadiMed supports multimodal mixed input of single or multiple images and text. It completes a closed‑loop workflow of “view images → retrieve knowledge → reason → conclude,” providing clinicians with comprehensive AI assistance—from lesion screening and precise diagnosis to personalized treatment decisions—thus propelling imaging diagnosis to a higher level of intelligence.

Unisound U2-ASR and U2-TTS Get Comprehensive Multilingual Upgrades, Enabling Global Listening and Speaking with a Single Model

Unisound has recently completed a full‑scale upgrade to the multilingual capabilities of U2‑ASR and U2‑TTS. U2‑ASR adds recognition for 13 new international languages, while U2‑TTS introduces speech synthesis for 8 Southeast Asian languages. With this update, the U2 Speech Large Model now supports over 100 Chinese dialects and more than 15 international languages.

Unisound Launches U2‑Med Expert‑Level Tri‑Medical Large Model: Reshaping the Industry with

Guided by the original aspiration of “AI for Good”, U2‑Med strives to upgrade AI from a conversational tool into a “digital expert” embedded in front‑line business workflows. By breaking down data and business silos across medical care, medical insurance and pharmaceuticals, and reconstructing the industrial ecosystem, U2‑Med is not only scaling new technical heights but also pursuing better human health outcomes. It leverages AI to drive medical services toward greater efficiency, equity and intelligence, so that the warmth of technology can truly benefit public health and well‑being worldwide.

VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment

Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods largely assume high-resource settings with abundant training data. In low-resource scenarios, RLVR is prone to more severe entropy collapse, which substantially limits exploration and degrades reasoning performance. To address this issue, we propose Hybrid-domain Entropy dynamics ALignment (HEAL), a framework tailored for few-shot RLVR. HEAL first selectively incorporates high-value general-domain data to promote more diverse exploration. Then, we introduce Entropy Dynamics Alignment (EDA), a reward mechanism that aligns trajectory-level entropy dynamics between the target and general domains, capturing both entropy magnitude and fine-grained variation. Through this alignment, EDA not only further mitigates entropy collapse but also encourages the policy to acquire more diverse exploration behaviors from the general domain. Experiments across multiple domains show that HEAL consistently improves few-shot RLVR performance. Notably, using only 32 target-domain samples, HEAL matches or even surpasses full-shot RLVR trained with 1K target-domain samples.