Tencent Hy

🚀 Tencent Hy Translation just landed.Powered by Hy-MT2. 33 languages. 5 Chinese minority languages & dialects. Voice. Photo. Full offline — on-device, no network required. Already live in 12 countries and regions. Travel, drive, work or read abroad. Accurate. Natural. Always available. ⏬ ⏬⏬ https://apps.apple.com/us/app/tencent-hy-translate/id6801428074

🎨 Hy Image3.5 preview is live in Miora. Edits that keep what already works — same canvas, your brand rules already remembered. Free for two weeks.

⚡️ As LLM reinforcement learning scales to larger GPU clusters and more training data, training efficiency becomes a first-order concern. Our new research revisits classical critical-batch-size theory and extends it to online LLM RL, where the model generates its own training data and rollout generation and training scale differently. Across GRPO and PPO, we find that learning-rate retuning can preserve learning per response over a bounded range of batch sizes. On fixed hardware, scaling up the batch size improves PPO generation-stage throughput by up to 2.29×, while our best measured GRPO configuration reaches the same validation target in 29% less time. 🚀 Read the full research: https://hy.tencent.ai/research/100116

Free for two weeks！Hy Image3.5 preview is live on OnSolo. Short drama character sheets. Full-motion video game assets. Keyframes. Characters hold across every episode. Edits refine instead of restarting.

New Research: We are releasing ExplorationBench, a benchmark for measuring how AI systems explore. Scientific discovery begins where known problems end: a system has to frame hypotheses, design experiments, and learn from the results. Evaluating this is hard. Genuinely new answers cannot be checked quickly, and in familiar domains a model can simply recall what it has seen. Addressing this challenge, researchers from Tencent Hy, Fudan University, and Tsinghua University built verifiable Alien Worlds. Their rules are executable, so every answer is checked exactly, and they conflict with familiar knowledge, so recall alone cannot solve the tasks. 🔹 Two sandboxes: AlienCode (31 hidden rule changes, 70 tasks) and AlienLogic (24 patched inference rules, 70 theorems) 🔹 A flawed manual, four rounds of self-designed probes, and closed-book tests after every round 🔹 Every answer graded by an interpreter or a proof checker, with no LLM judge What we found across 10 frontier AI systems: 1️⃣ Getting feedback is more effective than thinking alone. No AlienCode run starts above 15.7%; after four rounds the best reaches 89.0%, while the same turns without feedback stay at 0.5–11.0%. 2️⃣ Designing the experiments matters. Replaying a system's own best probes gives it exactly the same evidence, yet in AlienCode 9 of 10 systems do worse than when they chose the probes themselves. 3️⃣ Knowing a rule is not using it. Even when every required rule is stated correctly, tasks are solved only 73.4% of the time. 4️⃣ One score hides a lot. The same system under the same budget ended anywhere from 5.7% to 79.0%, and rankings barely transfer between the two worlds. CL-bench asked whether models can learn from context. ExplorationBench asks whether they can discover the rules themselves. 📄 Paper: https://arxiv.org/abs/2609.30199 🌐 Website & leaderboard: https://explorationbench.com 📝 Blog: https://explorationbench.com/blog/ 💻 Code (coming soon): https://github.com/Tencent-Hunyuan/ExplorationBench

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3 tracks. 3 winners. Go and Create. 🥇

Hy-SimulMT is now open source! 🚀 Tencent HY’s real-time Chinese ↔ English Simultaneous Interpreting Model. Key features: ⚡ Low latency (~3s) 🎯 High accuracy 🎙️ Real-time voice cloning 🏆 Outperforms most open-source models and commercial APIs Download, try, and build with it! 👇: Hugging Face: https://huggingface.co/collections/tencent/hy-simulmt ModelScope: https://www.modelscope.cn/collections/Tencent-Hunyuan/Hy-SimulMT GitHub: https://github.com/Tencent-Hunyuan/Hy-SimulMT

🚀 Hy Memory 2.0 is out — open-source model, framework, and MemHub. Your private memory steward. Framework handles chat, work & code memories. #1 open-source on 4 benchmarks. Hy-Memory-30B-A3B beats Opus 5 across major frameworks. MemHub (CLI + Mac) syncs across 10+ AI tools. 💻➡️ 🔗 SDK & MemHub: https://github.com/Tencent-Hunyuan/Hy-Memory 🔗 Model: https://huggingface.co/tencent/Hy-Memory-30B-A3B#Hunyuan #HyMemory #AgentMemory

📢 Introducing WorkForge: a scalable framework that constructs verifiable training environments for long-horizon work agents from real-world resources. Starting from expert workflows, it builds authentic workspaces, extracts checkable facts, and derives tasks plus dual (programmatic + semantic) verifiers grounded in observable evidence. 🚀 We scale to 16.7K environments across 40 professional domains covering 60 file types, enabling consistent gains on long-horizon work benchmarks as data volume and interaction horizon increase. 📚Paper: https://arxiv.org/pdf/2610.04906 @zhang_leo54389 @ZoeyyyZhououou @Be1ong1 #Tencent Hy#AIAgents #WorkAgents #AgentTraining

more 🔛 https://github.com/Tencent-Hunyuan/ExplorationBench