Fusion Model - The intelligent allocation model launched by FUMO Lab

What is Fusion Model

Fusion Model is the intelligent allocation model launched by FUMO Lab. The model can dynamically allocate computing needs to the most suitable heterogeneous models based on continuously evolving task states, achieving "one fusion, harness the strengths of all." The model ranks first on all four public test sets: reasoning, industry, programming, and science, while reducing costs by 30%-40% compared to directly calling authoritative models. The model's core innovation is shifting from static consumption intelligence to adaptive allocation intelligence, allowing the scenario-specific advantages of multiple models to produce compounding effects during execution.

Main features of Fusion Model

Dynamic intelligent allocation: Automatically selects the most suitable models to participate in collaboration based on task progress, with no need for users to manually switch or compare.
Uncertainty management: Identifies in real time the key uncertainties affecting decisions and determines whether to continue computing, obtain new evidence, or stop submission.
Unified state maintenance: Multiple models share the same task state, avoiding each maintaining a branched worldview and ensuring the final output is consistent.
Cost efficiency optimization: While maintaining top-tier performance, it reduces task costs by about 30%–40%, reserving strong computing power for the parts that truly bear the load.
Heterogeneous capability fusion: Integrates the expertise of different models in reasoning, code, science, industry, and other fields to output complete, clear, and directly executable answers.

Technical principles of Fusion Model

Intelligent allocation problem: Fusion Model treats task execution as a continuously evolving process. At each step, it re-answers based on the current state, "What is the most valuable next computation?" This shifts the decision unit from "who should own the initial request" to "the most valuable next computation under the current state."
Closed-loop evidence acquisition: The system turns reasoning into a loop of "read state→obtain evidence→update state→submit or stop." At each step, it evaluates four questions: what uncertainties remain, what evidence would change the answer, who is best suited to obtain the evidence, and whether the expected value of this computation exceeds the cost and latency.
Unified task state: Multiple models can explore together, but must share the same task state rather than each branching separately; tool calls serve only as suggestions, and only the authoritative path can execute external actions; all evidence retains source attribution and scope of applicability, and final decision authority always remains singular.
From combination to compounding: The system can learn how to make the scenario-specific advantages of each model interact with real tasks, enabling capabilities to produce compounding effects; model progress expands the intelligence supply, and allocation progress determines how much of that supply can be converted into useful system capability.
Conditional capability graph: Fusion builds a graph of "the probability that a given model provides useful new evidence under a specific task and existing evidence," making allocation strategies versionable, evaluable, constrained, and rollback-capable, continuously learning from tasks and results while remaining bounded.

How to use Fusion Model

Apply for trial: Visit the Fusion Model official website https://fumolab.ai/ to submit an application.
Unified entry point: Just describe the task you want to complete; there is no need to research what different models are each good at.
Automatic collaboration: The system automatically selects appropriate models in the background based on task progress to participate, supplement information, or check results.
Continuous verification: If the existing information is insufficient, it continues searching and verifying; if it is sufficient to reach a conclusion, it stops promptly.
Delivering results: What is ultimately presented to you is a complete, clear, and consistent answer, or an action that can be directly executed.

Core advantages of Fusion Model

Dynamic intelligent allocation: It evolves in real time based on task progress and automatically allocates computation to the most suitable heterogeneous models, without the need for manual selection or switching.
Unified task state: Multiple models share the same state for collaborative exploration, avoiding divergence and ensuring that the final output is consistent, authoritative, and trustworthy.
Excellence in both cost and efficiency: While maintaining top-tier performance, it reduces task costs by about 30%–40%, achieving a Pareto improvement on the capability-efficiency frontier.
Uncertainty-driven: It organizes computation around "what uncertainties affecting the decision still remain" rather than around fixed models, precisely identifying the stages that truly require strong compute.
Robust scientific reasoning: In the presence of "correct but non-decisive" distracting information, it more stably identifies constraints, covers knowledge, and judges causal relationships.
Compounding capability growth: The allocation strategy continuously learns from task results, enabling the system's capabilities to grow faster than would be possible by simply increasing compute investment.

Application scenarios of Fusion Model

Complex scientific reasoning: In fields such as materials science, biomedicine, and physical chemistry, it identifies constraints, covers the latest research findings, and judges causal chains, avoiding being led astray by "correct but non-decisive" knowledge.
Programming and software development: It handles multi-step code generation, debugging, and testing tasks, dynamically adjusting the model participation strategy based on execution feedback, reaching top-tier levels on code benchmarks such as Terminal Bench.
Finance and industry analysis: It addresses complex industry benchmarks such as τ³-Banking, dynamically invoking models skilled in reasoning, retrieval, or verification to handle boundary-sensitive issues such as contract terms and product specifications.
Long-horizon Agent workflows: It supports the complete execution trajectory of continuous observation, decision-making, action, and verification, adjusting the next computation strategy in real time based on unexpected data returned by tools or supplementary user information.
Enterprise-level knowledge Q&A: It provides a unified interface for scenarios such as R&D, legal affairs, and compliance, maintaining stable output in internal knowledge base retrieval, cross-verification of multi-source information, and conclusion convergence.