SimuRA: A World-Model-Driven Simulative Reasoning Architecture for General Goal-Oriented Agents
- URL: http://arxiv.org/abs/2507.23773v2
- Date: Fri, 24 Oct 2025 17:44:52 GMT
- Title: SimuRA: A World-Model-Driven Simulative Reasoning Architecture for General Goal-Oriented Agents
- Authors: Mingkai Deng, Jinyu Hou, Zhiting Hu, Eric Xing,
- Abstract summary: SimuRA is a goal-oriented architecture for generalized agentic reasoning.<n>We release ReasonerAgent-Web, a web-browsing agent built on SimuRA, as an open-source research demo.
- Score: 15.91448165400836
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: AI agents built on foundation models hold enormous promise. Current practice, however, focuses on a one-task-one-agent approach, which not only falls short of scalability and generality, but also faces practical limitations from black-box autoregressive reasoning, where decisions unfold token by token without explicit simulation or counterfactual evaluation of outcomes. Humans, on the other hand, reason and plan by mentally simulating the consequences of actions within an internal model of the world -- a capability that supports flexible, goal-directed behavior across diverse contexts. Moving towards a more general and powerful AI agent, we introduce SimuRA, a goal-oriented architecture for generalized agentic reasoning. Based on a principled formulation of an optimal agent in any general environment, SimuRA addresses the limitations of black-box autoregressive reasoning by incorporating the world model for planning via simulation. Our prototype world model is implemented using LLMs as a substrate, leveraging the natural language as a discrete, hierarchical representation grounded in concepts for planning, while remaining model-agnostic. On complex web-browsing tasks such as flight search, SimuRA improves the success rate from 0% to 32.2% compared to a representative open-web agent baseline. Across tasks, world-model-based planning achieves up to 124% higher task completion rates than a matched black-box autoregressive baseline, demonstrating the advantages of simulative reasoning. We release ReasonerAgent-Web, a web-browsing agent built on SimuRA, as an open-source research demo.
Related papers
- The Hierarchy of Agentic Capabilities: Evaluating Frontier Models on Realistic RL Environments [0.11586753333439907]
We present an empirical study evaluating frontier AI models on 150 workplace tasks within a realistic e-commerce RL environment from Surge.<n>Our analysis reveals an empirically-derived emphhierarchy of agentic capabilities that models must master for real-world deployment.<n>Weaker models struggle with fundamental tool use and planning, whereas stronger models primarily fail on tasks requiring contextual inference beyond explicit instructions.
arXiv Detail & Related papers (2026-01-13T23:49:06Z) - SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning [3.1436750864792375]
We introduce SimuAgent, an LLM-powered modeling and simulation agent tailored for Simulink.<n>SimuAgent replaces XML with a concise, dictionary-style Python representation, dramatically cutting token counts.<n>A lightweight plan-execute architecture, trained in two stages, equips the agent with both low-level tool skills and high-level design reasoning.
arXiv Detail & Related papers (2026-01-08T18:10:35Z) - EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence [17.644658293987955]
Embodied AI agents are capable of robust spatial perception, effective task planning, and adaptive execution in physical environments.<n>Current large language models (LLMs) and multimodal LLMs (MLLMs) for embodied tasks suffer from key limitations.<n>We propose EmbodiedBrain, a novel vision-language foundation model available in both 7B and 32B parameter sizes.
arXiv Detail & Related papers (2025-10-23T14:05:55Z) - SPACeR: Self-Play Anchoring with Centralized Reference Models [50.55045557371374]
Sim agent policies are realistic, human-like, fast, and scalable in multi-agent settings.<n>Recent progress in imitation learning with large diffusion-based or tokenized models has shown that behaviors can be captured directly from human driving data.<n>We propose SPACeR, a framework that leverages a pretrained tokenized autoregressive motion model as a central reference policy.
arXiv Detail & Related papers (2025-10-20T19:53:02Z) - VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents [130.70999337445468]
Key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, is shift from textual states to complex visual observations.<n>We ask: Can VLM agents construct internal world models through explicit visual state reasoning?<n>We architecturally enforce and reward the agent's reasoning process via reinforcement learning (RL)<n>We find that the agent's reasoning into State Estimation and Transition Modeling is critical for success.
arXiv Detail & Related papers (2025-10-19T16:05:07Z) - Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents [57.35214204211501]
We propose Dyna-Think, a thinking framework that integrates planning with an internal world model with reasoning and acting to enhance AI agent performance.<n>DIT reconstructs the thinking process of R1 to focus on performing world model simulation relevant to the proposed (and planned) action, and trains the policy using this reconstructed data.<n>DDT uses a two-stage training process to first improve the agent's world modeling ability via objectives such as state prediction or critique generation, and then improve the agent's action via policy training.
arXiv Detail & Related papers (2025-05-31T00:10:18Z) - AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability [84.52205243353761]
Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment.<n>We investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation.
arXiv Detail & Related papers (2025-04-06T20:35:44Z) - Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
arXiv Detail & Related papers (2025-02-17T18:49:25Z) - Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents [22.608219492706876]
We propose a model-based planning framework for web agents that employs a world model to simulate and deliberate over the outcome of each candidate action before committing to one.<n> Empirical results demonstrate that WebDreamer achieves substantial performance improvements over reactive baselines.<n>Our trained world model, Dreamer-7B, performs comparable to GPT-4o, highlighting the potential of specialized world models for efficient and effective planning in complex web environments.
arXiv Detail & Related papers (2024-11-10T18:50:51Z) - Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation [25.26545170310844]
We present a World-model-augmented (WMA) web agent, which simulates the outcomes of its actions for better decision-making.<n>Experiments on WebArena and Mind2Web show that our world models improve agents' policy selection without training.
arXiv Detail & Related papers (2024-10-17T05:37:00Z) - ReasonPlanner: Enhancing Autonomous Planning in Dynamic Environments with Temporal Knowledge Graphs and LLMs [0.32141666878560626]
We introduce ReasonPlanner, a novel generalist agent designed for reflective thinking, planning, and interactive reasoning.
ReasonPlanner significantly outperforms previous state-of-the-art prompting-based methods on the ScienceWorld benchmark by more than 1.8 times.
It relies solely on frozen weights thus requiring no gradient updates.
arXiv Detail & Related papers (2024-10-11T20:58:51Z) - On the Modeling Capabilities of Large Language Models for Sequential Decision Making [52.128546842746246]
Large pretrained models are showing increasingly better performance in reasoning and planning tasks.
We evaluate their ability to produce decision-making policies, either directly, by generating actions, or indirectly.
In environments with unfamiliar dynamics, we explore how fine-tuning LLMs with synthetic data can significantly improve their reward modeling capabilities.
arXiv Detail & Related papers (2024-10-08T03:12:57Z) - GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
We propose a novel large language model (LLMs)-based simulation platform called textitGenSim.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
arXiv Detail & Related papers (2024-10-06T05:02:23Z) - Sparse Rewards Can Self-Train Dialogue Agents [22.799506097310008]
We introduce a novel self-improvement paradigm that empowers LLM agents to autonomously enhance their performance without external human feedback.<n>We present ToolWOZ, a sparse reward tool-calling simulation environment derived from MultiWOZ.<n>We demonstrate that models trained with JOSH, both small and frontier, significantly improve tool-based interactions while preserving general model capabilities across diverse benchmarks.
arXiv Detail & Related papers (2024-09-06T21:00:57Z) - WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
Large language models (LLMs) can mimic human-like intelligence.<n>WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
arXiv Detail & Related papers (2024-07-07T07:15:49Z) - Modelling Multi-Agent Epistemic Planning in ASP [66.76082318001976]
This paper presents an implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings.
The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
arXiv Detail & Related papers (2020-08-07T06:35:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.