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.
4
10 commits
1 linked in READMEs
updated May 22, 2026
README
SR²AM-v0.1-8B
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 | paper | GitHub.
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 for setup and inference instructions.
Main Results
SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the paper.
Citation
@article{deng2026sr2am,
title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
Pimpalkhute, Varad and Killian, Taylor W. and
Liu, Zhengzhong and Xing, Eric P.},
journal={arXiv preprint arXiv:2605.22138},
year={2026}
}
License
Released under the Apache License 2.0.
Contributors
Similar repos
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.
4
10 commits
1 linked in READMEs
updated May 22, 2026
README
SR²AM-v0.1-8B
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 | paper | GitHub.
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 for setup and inference instructions.
Main Results
SR²AM-v0.1-8B sits above the size-vs-accuracy trendline in (a). The full benchmark breakdown is in the paper.
Citation
@article{deng2026sr2am,
title={Efficient Agentic Reasoning Through Self-Regulated Simulative Planning},
author={Deng, Mingkai and Hou, Jinyu and Neves, Lara Sá and
Pimpalkhute, Varad and Killian, Taylor W. and
Liu, Zhengzhong and Xing, Eric P.},
journal={arXiv preprint arXiv:2605.22138},
year={2026}
}
License
Released under the Apache License 2.0.