> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Agents‑A1** is a 35B Mixture‑of‑Experts agentic model from InternScience, built to scale heterogeneous agentic abilities across multiple domains including **Long‑horizon Search, Engineering, Scientific Research, Instruction Following, and Tool-calling**. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. From the scaling of long-horizon trajectories, **Agents‑A1** is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. From the scaling of heterogeneous agent abilities, **Agents‑A1** presents a three-stage training paradigm for building scalable general-purpose agentic model. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose multi-teacher multi-domain on-policy distillation with heterogeneity-aware optimization to improve knowledge transfer efficiency across different domains.
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> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
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> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
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> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. - **2026.7.8**: 🔥🔥 By popular demand from the community, our 4B model is coming in the next few days — making it faster and easier to build your own local AI assistant. - **2026.7.2**: 🔥🔥 Based on Agents-A1, we have released a series of quantized model variants. Please refer to the Agents-A1 collection. Besides, we’d like to thank the mlx-community for providing quantized versions at multiple scales. Try running Agents-A1 on your Mac!
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> [!Note] > This repository contains model weights and configuration files for Agents-A1 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Agents‑A1** is a 35B Mixture‑of‑Experts agentic model from InternScience, built to scale heterogeneous agentic abilities across multiple domains including **Long‑horizon Search, Engineering, Scientific Research, Instruction Following, and Tool-calling**. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. From the scaling of long-horizon trajectories, **Agents‑A1** is trained with the assistance of a domain-grounded knowledge-action infrastructure that jointly constructs actions, observations, and verifier outcomes, turning the agent's process into a trainable target. From the scaling of heterogeneous agent abilities, **Agents‑A1** presents a three-stage training paradigm for building scalable general-purpose agentic model. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose multi-teacher multi-domain on-policy distillation with heterogeneity-aware optimization to improve knowledge transfer efficiency across different domains.
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> [!Note] > This repository contains model weights and configuration files for Sage-A1.5 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
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> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5 in the **F16** (unquantized) format, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
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> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q4_K_M**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
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> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q6_K**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
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> [!Note] > This repository contains **GGUF** model weights for Sage-A1.5, quantized to **Q8_0**, for deployment with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. > > For the original Hugging Face Transformers weights (compatible with Transformers, vLLM, SGLang, etc.), see InternScience/Sage-A1.5. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **Agents‑A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
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