| --- |
| library_name: transformers |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| --- |
| |
| # Agents-A1: Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent |
|
|
| <div style="display: flex; flex-direction: column; align-items: center; line-height: 1.2;"> |
| <div style="display: flex; justify-content: center; align-items: center; gap: 10px; height: 30px;"> |
| <span style="font-size: 16px;" role="img" aria-label="Homepage">🏠</span> |
| <a href="https://internscience.github.io/Agents-A1/"><b>Homepage</b></a> |
| <span style="color: #ccc;">|</span> |
| <img src="./figures/24px.svg" width="16" height="16" alt="Technical Report" style="filter: invert(0.5);"> |
| <a href="https://arxiv.org/abs/2606.30616"><b>Technical Report</b></a> |
| </div> |
| |
| <div style="display: flex; justify-content: center; align-items: center; gap: 10px; height: 30px; margin-top: 2px;"> |
| <img src="./figures/hf-logo.svg" width="16" height="16" alt="Hugging Face"> |
| <a href="https://huggingface.co/InternScience/Agents-A1"><b>Hugging Face</b></a> |
| <span style="color: #ccc;">|</span> |
| <img src="./figures/github-logo.svg" width="16" height="16" alt="GitHub"> |
| <a href="https://github.com/InternScience/Agents-A1"><b>Github</b></a> |
| <span style="color: #ccc;">|</span> |
| <img src="./figures/modelscope-logo.svg" width="16" height="16" alt="Model Scope"> |
| <a href="https://modelscope.cn/models/InternScience/Agents-A1"><b>ModelScope</b></a> |
| </div> |
| </div> |
| |
| > [!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](https://huggingface.co/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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|  |
|
|
| ## Highlights |
|
|
| - **Agentic Reasoning**: Agents-A1 excels at decomposing complex tasks into executable sub-steps, planning ahead, and adapting its strategy based on intermediate results. |
| - **Tool Use**: Natively supports function calling and tool integration, enabling seamless interaction with APIs, code interpreters, search engines, and other external tools. |
| - **Scientific and Professional Reasoning**: Handles tool-integrated scientific reasoning and professional knowledge question answering. |
| - **Instruction Following**: Precisely follows detailed, multi-constraint instructions across diverse domains. |
|
|
| We welcome developers and enterprises to integrate and try Agents-A1 and share their feedback. |
|
|
| ## Performance |
|
|
| We evaluate Agents-A1 in real-world agentic and research-oriented workflows across six directions — long-horizon search, engineering tasks, scientific research, instruction following, general agentic tasks, and scientific agentic tasks. Despite operating in the ~35B model class, Agents-A1 delivers highly competitive performance against frontier-scale systems such as GPT-5.5, DeepSeek-V4-pro, and Kimi-K2.6. It achieves overall SOTA results on several challenging benchmarks, including Seal-0 (56.4), HiPhO (46.4), FrontierScience-Olympiad (79.0), FrontierScience-Research (40.00), IFBench (80.6), and IFEval (94.8), while also ranking as the best among comparable models on a broad range of tasks such as BrowseComp (75.5), XBench-DS-2510 (86.0), GAIA (96.0), SciCode (44.3), HLE with tools (47.6), and MolBench-bind (56.8). These results show that Agents-A1 combines strong long-horizon search ability, robust scientific reasoning, and reliable instruction following, establishing it as a highly capable and efficient agentic model that narrows the gap with much larger frontier models. |
|
|
|
|
| <p> |
| 🥇 Overall SOTA |
| 🟢 Best Among Comparable Models (~35B) |
| </p> |
|
|
| <table> |
| <thead> |
| <tr> |
| <th rowspan="2" align="left">Benchmark</th> |
| <th colspan="3" align="center" style="text-align:center;"> |
| 📏 Comparable Models (~35B) |
| </th> |
| |
| <th colspan="4" align="center" style="text-align:center;"> |
| 🚀 Larger-scale Models |
| </th> |
| |
| <th colspan="2" align="center" style="text-align:center;"> |
| ⭐ Ours |
| </th> |
| </tr> |
| |
| <tr> |
| <th align="center">Qwen3.5-35B-A3B</th> |
| <th align="center">Qwen3.6-35B-A3B</th> |
| <th align="center">Nex-N2-mini</th> |
| |
| <th align="center">Step-3.5-Flash</th> |
| <th align="center">Kimi-K2.6</th> |
| <th align="center">DeepSeek-V4-pro(Max)</th> |
| <th align="center">GPT-5.5(xhigh)</th> |
|
|
| <th align="center">Agents-A1</th> |
| </tr> |
| </thead> |
|
|
| <tbody> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>🔍 Long-horizon Search</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">BrowseComp</td> |
| <td align="center">61.0</td> |
| <td align="center">67.93</td> |
| <td align="center">74.1</td> |
| <td align="center">69.0</td> |
| <td align="center">83.2</td> |
| <td align="center">83.4</td> |
| <td align="center">🥇 84.4</td> |
| <td align="center">🟢 75.51</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">XBench-DS-2510</td> |
| <td align="center">77.0</td> |
| <td align="center">71.0</td> |
| <td align="center">82.0</td> |
| <td align="center">56.3</td> |
| <td align="center">🥇 90.0</td> |
| <td align="center">🥇 90.0</td> |
| <td align="center">84.0</td> |
| <td align="center">🟢 86.0</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">Seal0</td> |
| <td align="center">41.4</td> |
| <td align="center">38.74</td> |
| <td align="center">49.55</td> |
| <td align="center">36.94</td> |
| <td align="center">50.45</td> |
| <td align="center">54.95</td> |
| <td align="center">42.34</td> |
| <td align="center">🥇 56.36</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">GAIA</td> |
| <td align="center">59.8</td> |
| <td align="center">78.64</td> |
| <td align="center">82.52</td> |
| <td align="center">84.5</td> |
| <td align="center">80.58</td> |
| <td align="center">🥇 98.06</td> |
| <td align="center">87.38</td> |
| <td align="center">🟢 96.04</td> |
| </tr> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>⚙️ Engineering Tasks</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">SciCode</td> |
| <td align="center">37.7</td> |
| <td align="center">35.8</td> |
| <td align="center">29.9</td> |
| <td align="center">40.4</td> |
| <td align="center">53.5</td> |
| <td align="center">50.0</td> |
| <td align="center">🥇 56.1</td> |
| <td align="center">🟢 44.33</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">MLE-Lite</td> |
| <td align="center">24.24</td> |
| <td align="center">34.85</td> |
| <td align="center">34.85</td> |
| <td align="center">54.55</td> |
| <td align="center">62.12</td> |
| <td align="center">63.64</td> |
| <td align="center">🥇 72.73</td> |
| <td align="center">🟢 43.94</td> |
| </tr> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>🧪 Scientific Research</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">HLE w/ tools</td> |
| <td align="center">47.4</td> |
| <td align="center">36.2</td> |
| <td align="center">32.0</td> |
| <td align="center">23.1</td> |
| <td align="center">🥇 54.0</td> |
| <td align="center">48.2</td> |
| <td align="center">52.2</td> |
| <td align="center">🟢 47.6</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">HiPhO</td> |
| <td align="center">37.0</td> |
| <td align="center">37.7</td> |
| <td align="center">38.5</td> |
| <td align="center">38.3</td> |
| <td align="center">41.1</td> |
| <td align="center">38.7</td> |
| <td align="center">43.3</td> |
| <td align="center">🥇 46.4</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">FrontierScience-Olympiad</td> |
| <td align="center">64.5</td> |
| <td align="center">60.3</td> |
| <td align="center">52.0</td> |
| <td align="center">61.0</td> |
| <td align="center">73.0</td> |
| <td align="center">76.0</td> |
| <td align="center">78.0</td> |
| <td align="center">🥇 79.0</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">FrontierScience-Research</td> |
| <td align="center">2.5</td> |
| <td align="center">2.9</td> |
| <td align="center">5.0</td> |
| <td align="center">6.7</td> |
| <td align="center">17.9</td> |
| <td align="center">13.3</td> |
| <td align="center">26.7</td> |
| <td align="center">🥇 40.0</td> |
| </tr> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>📋 Instruction Following</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">IFBench</td> |
| <td align="center">70.2</td> |
| <td align="center">64.4</td> |
| <td align="center">54.08</td> |
| <td align="center">64.6</td> |
| <td align="center">71.77</td> |
| <td align="center">73.47</td> |
| <td align="center">75.9</td> |
| <td align="center">🥇 80.61</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">LongBench-v2</td> |
| <td align="center">59.0</td> |
| <td align="center">57.7</td> |
| <td align="center">59.6</td> |
| <td align="center">57.5</td> |
| <td align="center">62.0</td> |
| <td align="center">🥇 64.3</td> |
| <td align="center">-</td> |
| <td align="center">🟢 60.2</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">IFEval</td> |
| <td align="center">91.9</td> |
| <td align="center">91.3</td> |
| <td align="center">88.4</td> |
| <td align="center">93.53</td> |
| <td align="center">94.45</td> |
| <td align="center">93.35</td> |
| <td align="center">93.35</td> |
| <td align="center">🥇 94.82</td> |
| </tr> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>🤖 General Agentic Tasks</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">τ<sup>2</sup>-Bench</td> |
| <td align="center">🟢 81.2</td> |
| <td align="center">79.0</td> |
| <td align="center">74.53</td> |
| <td align="center">75.77</td> |
| <td align="center">81.93</td> |
| <td align="center">🥇 82.2</td> |
| <td align="center">81.63</td> |
| <td align="center">79.81</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">VitaBench</td> |
| <td align="center">31.9</td> |
| <td align="center">35.6</td> |
| <td align="center">23.0</td> |
| <td align="center">30.0</td> |
| <td align="center">35.63</td> |
| <td align="center">🥇 49.04</td> |
| <td align="center">45.0</td> |
| <td align="center">🟢 38.75</td> |
| </tr> |
|
|
| <tr> |
| <td colspan="9" align="left"><b>🔬 Scientific Agentic Tasks</b></td> |
| </tr> |
| |
| <tr> |
| <td align="left">MatTools</td> |
| <td align="center">21.0</td> |
| <td align="center">15.9</td> |
| <td align="center">34.1</td> |
| <td align="center">44.93</td> |
| <td align="center">63.8</td> |
| <td align="center">47.1</td> |
| <td align="center">🥇 68.8</td> |
| <td align="center">🟢 47.1</td> |
| </tr> |
|
|
| <tr> |
| <td align="left">MolBench-bind</td> |
| <td align="center">46.0</td> |
| <td align="center">48.7</td> |
| <td align="center">51.4</td> |
| <td align="center">45.95</td> |
| <td align="center">21.6</td> |
| <td align="center">37.8</td> |
| <td align="center">🥇 62.2</td> |
| <td align="center">🟢 56.8</td> |
| </tr> |
|
|
| </tbody> |
| </table> |
|
|
|
|
| ## Usage |
|
|
| ### SGLang |
|
|
| [SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. |
|
|
| Install SGLang with uv: |
|
|
| ```shell |
| uv venv --python 3.12 --seed --managed-python |
| source .venv/bin/activate |
| |
| uv pip install sglang |
| ``` |
|
|
| See [its documentation](https://docs.sglang.ai/get_started/install.html) for more details. |
|
|
| The following commands create API endpoints at `http://localhost:8000/v1`: |
|
|
| - **Standard Version** (1 GPUs, 262K context): |
|
|
| ```shell |
| python -m sglang.launch_server \ |
| --model-path InternScience/Agents-A1 \ |
| --port 8000 \ |
| --tp-size 1 \ |
| --mem-fraction-static 0.8 \ |
| --context-length 262144 \ |
| --reasoning-parser qwen3 |
| ``` |
| - **Tool Use**: |
|
|
| ```shell |
| python -m sglang.launch_server \ |
| --model-path InternScience/Agents-A1 \ |
| --port 8000 \ |
| --tp-size 1 \ |
| --mem-fraction-static 0.8 \ |
| --context-length 262144 \ |
| --reasoning-parser qwen3 \ |
| --tool-call-parser qwen3_coder |
| ``` |
|
|
| ### vLLM |
|
|
| [vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs. |
|
|
| Install vLLM from the main branch via uv: |
|
|
| ```shell |
| uv venv --python 3.12 --seed --managed-python |
| source .venv/bin/activate |
| |
| uv pip install vllm --torch-backend=auto |
| ``` |
|
|
| See [its documentation](https://docs.vllm.ai/en/stable/getting_started/installation/index.html) for more details. |
|
|
| The following commands create API endpoints at `http://localhost:8000/v1`: |
|
|
| - **Standard Version** (1 GPUs, 262K context): |
|
|
| ```shell |
| vllm serve InternScience/Agents-A1 \ |
| --port 8000 \ |
| --tensor-parallel-size 1 \ |
| --max-model-len 262144 \ |
| --reasoning-parser qwen3 |
| ``` |
| - **Tool Call**: |
|
|
| ```shell |
| vllm serve InternScience/Agents-A1 \ |
| --port 8000 \ |
| --tensor-parallel-size 1 \ |
| --max-model-len 262144 \ |
| --reasoning-parser qwen3 \ |
| --enable-auto-tool-choice \ |
| --tool-call-parser qwen3_coder |
| ``` |
| - **Text-Only** (skips vision encoder to free KV cache memory): |
|
|
| ```shell |
| vllm serve InternScience/Agents-A1 \ |
| --port 8000 \ |
| --tensor-parallel-size 1 \ |
| --max-model-len 262144 \ |
| --reasoning-parser qwen3 \ |
| --language-model-only |
| ``` |
|
|
| ### Recommended Sampling Parameters |
|
|
| For the best generation quality, we recommend the following sampling parameters: |
|
|
| - `temperature`: 0.85 |
| - `top_p`: 0.95 |
| - `top_k`: 20 |
| - `min_p`: 0.0 |
| - `presence_penalty`: 1.1 |
| - `repetition_penalty`: 1.0 |
| |
|
|
| ## Agent Capability Evaluation |
|
|
| To provide the community with a unified agent evaluation codebase for fair comparison, we have also open-sourced an evaluation framework for assessing agentic models across core capabilities, including tool use and multi-step reasoning. The evaluation code is included in the [Agents-A1/evaluation](https://github.com/InternScience/Agents-A1/tree/main/evaluation) of this repository. |
|
|
| We use this framework to evaluate the released model under a standardized and reproducible setting. |
| Specifically, the model is tested on a set of agent-oriented tasks that require it to understand user goals, decompose complex instructions, interact with tools or environments when necessary, and produce final results. The evaluation results reported in [Model Card](https://huggingface.co/InternScience/Agents-A1) are generated using the open-source framework above, so that users can reproduce the experiments, compare other models under the same protocol, and further extend the benchmark for new agent scenarios. (**Note that:** To ensure a fair comparison, we report the benchmark results from their original technical reports. If a model does not report the corresponding benchmark results, we evaluate it using the same evaluation protocol as our model.) |
|
|
| For detailed evaluation scripts, task definitions, metrics, and reproduction instructions, please refer to the evaluation codebase. |
|
|
| ## Citation |
|
|
| If you find our work helpful, feel free to give us a cite. |
|
|
| ``` |
| |
| ``` |
|
|
