# Agent Alpha: Tree Search Unifying Generation, Exploration and Evaluation for Computer-Use Agents

arXiv: https://arxiv.org/abs/2602.02995 · HTML: https://mlsanity.com/paper/2602.02995/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 33 of its 44 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 33)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents](https://mlsanity.com/paper/2403.02502/) (2024)
- [GTA1: GUI Test-time Scaling Agent](https://mlsanity.com/paper/2507.05791/) (2025)
- [ScreenAgent: A Vision Language Model-driven Computer Control Agent](https://mlsanity.com/paper/2402.07945/) (2024)
- [Scaling Test-time Compute for LLM Agents](https://mlsanity.com/paper/2506.12928/) (2025)
- [CoAct-1: Computer-using Multi-Agent System with Coding Actions](https://mlsanity.com/paper/2508.03923/) (2025)
- [OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://mlsanity.com/paper/2601.07779/) (2026)
- [Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks](https://mlsanity.com/paper/2503.16974/) (2025)
- [Modeling Other Players with Bayesian Beliefs for Games with Incomplete Information](https://mlsanity.com/paper/2405.14122/) (2024)
- [Scaling Agents for Computer Use](https://mlsanity.com/paper/2510.02250/) (2025)
- [InSPO: Unlocking Intrinsic Self-Reflection for LLM Preference Optimization](https://mlsanity.com/paper/2512.23126/) (2025)
- [Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization](https://mlsanity.com/paper/2508.05995/) (2025)
- [Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models](https://mlsanity.com/paper/2405.19878/) (2024)
- [Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks](https://mlsanity.com/paper/2502.00633/) (2025)
- [A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality](https://mlsanity.com/paper/2508.09185/) (2025)
- [Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking](https://mlsanity.com/paper/2202.10881/) (2022)
- [Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol](https://mlsanity.com/paper/2508.20737/) (2025)
- [MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning](https://mlsanity.com/paper/2511.06142/) (2025)

## Built on by (most cited first, 12 of 12)

- [From Question Answering to Task Completion: A Survey on Agent System and Harness Design](https://mlsanity.com/paper/2606.20683/) (2026)
- [RoboRouter: Training-Free Policy Routing for Robotic Manipulation](https://mlsanity.com/paper/2603.07892/) (2026)
- [NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search](https://mlsanity.com/paper/2605.00751/) (2026)
- [Operator-Guided Invariance Learning for Continuous Reinforcement Learning](https://mlsanity.com/paper/2605.06500/) (2026)
- [Metric-Gradient Projection for Stable Multi-Agent Policy Learning](https://mlsanity.com/paper/2605.18809/) (2026)
- [Efficient Agent Evaluation via Diversity-Guided User Simulation](https://mlsanity.com/paper/2604.21480/) (2026)
- [Interactive Critique-Revision Training for Reliable Structured LLM Generation](https://mlsanity.com/paper/2605.08327/) (2026)
- [IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents](https://mlsanity.com/paper/2604.05157/) (2026)
- [Software Engineering for and with GUI Agent](https://mlsanity.com/paper/2608.09278/) (2026)
- [TClone: Low-Latency Forking of Live GUI Environments for Computer-Use Agents](https://mlsanity.com/paper/2605.17320/) (2026)
- [VisCritic: Visual State Comparison as Process Reward for GUI Agents](https://mlsanity.com/paper/2606.24525/) (2026)
- [FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts](https://mlsanity.com/paper/2607.20500/) (2026)

## Abstract (arXiv)

While scaling test-time compute through trajectory-level sampling has significantly improved Graphical User Interface (GUI) agents, the lack of regressive ability prevents the reuse of partial successes and the recovery from early missteps. In this paper, we introduce Agent Alpha, a unified framework that synergizes generation, exploration, and evaluation through step-level Monte Carlo Tree Search (MCTS). It enables active modeling or exploiting structures of the planning space. By integrating alpha-UCT guided search into the interaction loop, Agent Alpha enables deliberate planning, facilitating early pruning of suboptimal branches and efficient prefix reuse. We also employ comparison-driven evaluation to mitigate absolute scoring biases and diversity-constrained expansion to maintain a compact, informative search space. Regret bound of alpha-UCT is analyzed. On the OSWorld benchmark, Agent Alpha achieves a state-of-the-art success rate of ~ 77%, significantly outperforming trajectory-level baselines under equivalent compute.