Fusion Model Operational

Fusion Model is an intelligent allocation model developed by FUMO Lab. Based on continuously evolving task states, it dynamically assigns computational needs to the most suitable heterogeneous models, achieving "one fusion, the best of many." The model ranked first across all four public benchmarks covering reasoning, industry, programming, and science, while reducing costs by 30%-40% compared with directly calling authoritative models. Its core innovation is the shift from static intelligence consumption to adaptive intelligence allocation, enabling the contextual strengths of multiple models to produce a compounding effect during execution.

Dynamic intelligent allocation: Automatically selects the most suitable models for collaboration based on task progress, with no need for users to switch between or compare models manually.
Uncertainty management: Identifies key uncertainties affecting decisions in real time and determines whether to continue computing, obtain new evidence, or stop and submit.
Unified state maintenance: Multiple models share the same task state, avoiding divergent worldviews and ensuring consistent final output.
Cost-efficiency optimization: Maintains top-tier performance while reducing task costs by approximately 30%-40%, reserving substantial computing power for the stages that truly need it.
Heterogeneous capability fusion: Combines model expertise in reasoning, code, science, industry, and other fields to produce complete, clear, and directly actionable answers.

Intelligent allocation: Fusion Model treats task execution as a continuously evolving process. At every step, it reassesses "what is the most valuable next computation," shifting the decision unit from "who should own the initial request" to "what is the most valuable next computation given the current state."
Closed-loop evidence acquisition: The system transforms reasoning into a cycle of "read state → acquire evidence → update state → submit or stop." At each step, it evaluates four questions: What uncertainties remain? What evidence could change the answer? Who is best suited to obtain that evidence? And is the expected value of this computation greater than its cost and latency?
Unified task state: Multiple models can explore together, but they must share one task state rather than diverging into separate branches. Tool calls serve only as suggestions; only the authoritative path can execute external actions. All evidence retains its source attribution and scope of applicability, while final decision-making authority remains centralized.
From combination to compounding: The system learns how to make the contextual strengths of each model interact with real tasks, creating a compounding effect. Improvements in models expand the supply of intelligence, while improvements in allocation determine how much of that supply can be converted into useful system capabilities.
Conditional capability map: Fusion builds a map of "the probability that a given model will provide useful new evidence for a specific task and existing evidence." This makes allocation strategies versionable, evaluable, constrained, and rollback-capable, allowing continuous learning from tasks and outcomes while remaining bounded.

Request a trial: Visit the Fusion Model website at https://fumolab.ai/ and submit an application.
Unified entry point: Simply describe the task you want to complete; there is no need to research what each model does best.
Automatic collaboration: In the background, the system automatically selects appropriate models to participate, supplement information, or check results based on task progress.
Continuous verification: If the available information is insufficient, it continues searching and verifying; when enough information is available to reach a conclusion, it stops promptly.
Get results: You receive a complete, clear, and consistent answer, or an action that can be executed directly.

Dynamic intelligent allocation: Evolves in real time with task progress and automatically assigns computation to the most suitable heterogeneous models, without manual selection or switching.
Unified task state: Multiple models share one state for collaborative exploration, avoiding divergence and ensuring final output that is consistent and authoritative.
Cost and efficiency optimization: Maintains top-tier performance while reducing task costs by approximately 30%-40%, delivering a Pareto improvement along the capability-efficiency frontier.
Uncertainty-driven: Organizes computation around "which decision-relevant uncertainties remain" rather than around fixed models, precisely identifying the stages that truly require substantial computing power.
Robust scientific reasoning: More reliably identifies constraints, covers relevant knowledge, and determines causal relationships when faced with information that is "correct but not decisive."
Compounding capability growth: Continuously learns allocation strategies from task outcomes, enabling system capabilities to grow faster than simply increasing computing investment.

Complex scientific reasoning: In materials science, biomedicine, physical chemistry, and other fields, it identifies constraints, incorporates the latest research, and evaluates causal chains, avoiding distraction by knowledge that is "correct but not decisive."
Programming and software development: Handles multi-step code generation, debugging, and testing tasks, dynamically adjusting model participation based on execution feedback and achieving top-tier performance on coding benchmarks such as Terminal Bench.
Financial and industry analysis: Addresses complex industry benchmarks such as τ³-Banking by dynamically calling models specialized in reasoning, retrieval, or verification to handle boundary-sensitive issues such as contract terms and product specifications.
Long-horizon Agent workflows: Supports complete execution trajectories of continuous observation, decision-making, action, and verification, adjusting the next computational strategy in real time based on unexpected tool data or additional user information.
Enterprise knowledge Q&A: Provides a unified interface for R&D, legal, compliance, and other scenarios, maintaining stable output for internal knowledge-base retrieval, cross-validation of multi-source information, and convergence of conclusions.