Add README.md

README.md

@@ -0,0 +1,57 @@
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- agent
+- reasoning
+- tool-use
+- simulative-planning
+base_model: Qwen/Qwen3-8B
+---
+
+# SR²AM-v0.1-8B
+
+![SR²AM Illustration](model.png)
+
+We argue that efficient agentic reasoning benefits from decomposing deliberation into three interacting systems: **reactive execution** (System I) for fine-grained reasoning and direct action; **simulative reasoning** (System II) that predicts consequences of proposed actions through a world model; and **self-regulation** (System III) that decides *when* and *how deeply* to plan through a learned **configurator**.
+
+**SR²AM** (Self-Regulated Simulative Reasoning Agentic LLM) is our instantiation: the configurator and simulative planner are realized as distinct stages within an LLM's chain-of-thought reasoning, with the LLM itself serving as the world model in language space.
+
+SR²AM-v0.1-8B achieves an overall Pass@1 of **57.0** across 11 benchmarks spanning math, science, tabular analysis, and web information seeking — competitive with systems at 120–355B parameters.
+
+More details: [project website](https://sr2am-agentic-llm.github.io/) | [paper](https://arxiv.org) | [GitHub](<GITHUB_URL_TBD>).
+
+## Key Features
+
+- **System I + II + III decomposition**: a configurator (System III) decides per-turn whether to plan, continue an existing plan, or act directly; a simulative planner (System II) constructs plans grounded in predicted future states; reactive execution (System I) handles fine-grained reasoning and tool use.
+- **SFT + RL training**: supervised learning on data encoding the self-regulated planning structure, followed by reinforcement learning (GRPO) for task success.
+- **Agentic tool use**: web search (SerpAPI), web browsing with LLM summarization, and stateless Python code execution (SandboxFusion).
+- **Compact and efficient**: 3,698 reasoning tokens per trajectory on average — fewer or comparable to other systems at the same scale while outperforming them in Pass@1.
+
+## Quick Start
+
+See the [GitHub repository](<GITHUB_URL_TBD>) for setup and inference instructions.
+
+## Main Results
+
+![Pass@1 vs. parameter size and reasoning-token count](main-results.png)
+
+SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the [paper](https://arxiv.org).
+
+## Citation
+
+```bibtex
+@inproceedings{sr2am2026,
+  title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
+  author={Deng, Mingkai and Hou, Jinyu and Sá Neves, Lara and Pimpalkhute, Varad and Killian, Taylor W. and Liu, Zhengzhong and Xing, Eric P.},
+  booktitle={Preprint},
+  year={2026}
+}
+```
+
+## License
+
+Released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).