Charting a trajectory towards autonomous superhuman agent for ultra-parallel research
Horizon Labs is dedicated to forging the future of scientific inquiry. We are engineering an autonomous agent capable of superhuman performance in physics and mathematics, designed to unravel complex phenomena and accelerate the pace of discovery through advanced computational modeling with unprecedented program space traversal.
Redefining the limits of machines doing mathematics
Reconceptualizing mathematical research through advanced program synthesis and the automated exploration of vast program spaces containing novel frameworks and proofs. Our goal extends beyond mere verification towards the active construction of new mathematical frameworks that can enable new scientific discoveries.
Orchestrate, simulate and validate your research via CUA Command
CUA UI
Experience seamless interaction with our Computational User Agent through an intuitive interface integrated directly into your OS. Effortlessly manage complex tasks and orchestrate system-level operations via a streamlined, powerful command bar.
Core Principles
Our foundational methodology synergizes advanced agentic reasoning and symbolic manipulation to accelerate the discovery pipeline in mathematical research.
Autonomous Foresight
Proactively identifies emerging scientific frontiers, charts potential research trajectories, and translates complex hypotheses into actionable, deployable insights.
Self-Adaptation
Continuously refines its methodologies and expands its knowledge base, mastering diverse domain-specific challenges and transcending initial objectives to unlock unforeseen possibilities.
Long-Running Execution
Navigates vast computational landscapes over extended durations, maintaining persistent operational state and dynamically adapting strategies through sophisticated replanning.
Scientific Multi-Agent Thinking
Employs a synergistic approach integrating formal systems, advanced program reasoning, automated theorem proving, and rigorous validation within a cohesive, closed-loop discovery engine.
The Scaling Era
A paradigm shift in scientific simulation and reasoning
Compute-Optimal Scaling
Leverages pre-trained models and enhances their output by increasing inference-time resources (FLOPs, steps, external tools). Rather than relying on ever-larger pretraining budgets, test-time methods use dynamic inference strategies that allow models to "think longer" on harder problems through strategies like iterative self-refinement or using a reward model to perform search over the space of solutions.
Limitations
- Diminishing returns, performance saturates
- Applicable to many problems, agent workflows
- Constrained by pre-trained capacity
- Drawbacks of resulting inefficiency
- Not inherently adaptive
Horizon ScalingCore
Our paradigm to achieve superhuman-nearing capability aiming to increase performance through adaptive foresight reward, dynamic feedback-driven evolution process, and combining with meta-learning at scale. Rather than relying on limited compute-optimal scaling over constrained search paths, we aim to implement ultra-extended reasoning windows to explore vast program spaces, allowing the system to "improve longer" on harder problems.
Key Components
- Meta-Learning at Scale, continuously optimizes domain mastery
- Resource Bootstrapping, acquires more compute, data or tools
- Task-Agnostic Growth, improves general capability
- Feedback-Driven Evolution, dynamically adapts to planning and action
- Self-adaptation, continuously expands possibilities beyond initial objectives
Attaining near-superhuman performance demands a paradigm shift—a system that transcends existing limitations through dynamic scaling, continuous adaptation, and the ability to push beyond predetermined boundaries.
