The Next Frontier of Generative Intelligence.
Exobyte is building a new continuous-time mathematical foundation for the next generation of artificial intelligence systems. Our algorithms learn generative AI models with task-optimized geometries at scale, removing the restrictive assumptions that scalable training has required until now.
We are building AI systems that shift the frontier of performance, scalability, and energy efficiency across language, vision, and scientific discovery.
The geometry of generative intelligence.
Picture intelligence as motion. A language model moves a prompt into a response. A diffusion model moves noise into images. A control policy moves a system toward a goal. Scientific inference moves observations and prior knowledge into predictive trajectories.
These problems share a common mathematical structure: learning a coherent stochastic path that generates the answer we seek, shaped by problem-specific data, constraints, and goals. We see generation, inference, and control as three views of one continuous-time stochastic bridge geometry. This geometry determines both the quality of a model's outputs and the energy consumed at inference.
How do language models create stochastic bridges?
The geometry of generation is fixed before a model ever learns.
Diffusion and flow models have driven remarkable progress in text-to-image, text-to-video, and now diffusion-based language models. They reached this scale by fixing the geometry of generation in advance: simplified, restrictive conditional path laws that make simulator-free training possible.
The route to tractability adopted by current approaches comes at a price. The conditional path law assumptions fix the geometry before learning begins, and with it, the ceiling on generation quality and inference efficiency.
Post-training corrections and quantization methods can trade quality for inference cost, but they cannot close the optimality gap a fixed geometry creates.
Energy is becoming AI's binding constraint.
Driven by AI, data-center electricity use is projected to reach around 1,600 terawatt-hours by 2035 – about 4.4% of global electricity. Increased adoption of agentic workflows is expected to steepen that curve further. A single task now involves multiple model calls that significantly increase compute requirements: planning and reasoning, tool execution, and verification in iterative loops orchestrated by complex agentic harnesses.
Better hardware is necessary but not sufficient.
Progress in AI accelerators has lowered the energy per operation, but cannot remove the fundamental inefficiency of a generative model trained to match a conditional path law prescribed in advance. Hardware advances alone cannot lower the true cost of training and inference. That also requires theoretical advances that shift the frontier.
The same logic applies to hardware on the horizon such as physical and analog computing, substrates that operate in continuous time. Today's discrete-time, transformer-based AI stacks do not natively map onto them. Unlocking the capabilities of that hardware will require a competitive stack of continuous-time AI architectures that do not yet exist.
This is the direction Exobyte is taking: a new continuous-time theoretical foundation for a broad class of generative modeling problems, built to shift the frontier on today's accelerators and to run natively on the hardware that follows.
Breaking the expressivity-tractability trade-off
Today’s generative AI models purchased scalability by constraining the geometry of generation. The question driving Exobyte is whether the next generation of AI systems can achieve scalability without paying that price.
This question is new in its modern AI form, but its mathematical roots are deep. It draws on a lineage of work spanning more than two centuries on the classical bridge learning problem: finding an optimal path between two distributions.
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1781
Gaspard Monge
Posed the optimal transport problem: how to move one distribution of mass to another at minimum cost.
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1931–32
Erwin Schrödinger
Posed the Schrödinger bridge problem: among all stochastic processes connecting two distributions, find the most likely one.
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1942
Leonid Kantorovich
Reformulated optimal transport as a linear programming problem. Later awarded the Nobel Prize in Economics.
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1987
Yann Brenier
Proved that the optimal transport map in Euclidean space is the gradient of a convex function.
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2000
Jean-David Benamou & Yann Brenier
Introduced a dynamic, path-space variational formulation of optimal transport.
This lineage of work provides a rich mathematical foundation, but it does not yield scalable simulator-free algorithms for learning high-dimensional stochastic bridges without simplifying assumptions. That is the gap we close. Our work builds upon and extends results in stochastic processes and optimal transport theory to address the demands of modern generative AI, where bridge learning must be expressive, scalable, and deployable across language, vision, and scientific discovery.
The generalized Schrödinger bridge principle
At Exobyte, we are developing the generalized Schrödinger bridge principle: a simulator-free, Lagrangian variational framework. This new theoretical foundation underpins our scalable algorithms that address a broad class of generative AI problems in a continuous-time setting: from stochastic bridges for language modeling to bridge learning tasks involving endpoint samples, multi-time observations, sampling from unnormalized densities, and control with complex reward models.
Our unified variational framework naturally accommodates domain-specific priors and constraints. We retain scalability while learning the conditional path laws over an unrestricted class. This enables the full stochastic bridge geometry to be optimally shaped, removing the geometric bottleneck in current approaches that rely on restrictive path laws for tractability.
Core capabilities
Three capabilities define what the generalized Schrödinger bridge principle makes possible:
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01
Scalable learning without restrictive path laws
Massively scalable, simulator-free training across a broad class of generative AI problems, retaining scalability even when the conditional path laws are complex and fully learnable.
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02
Optimal bridges for energy-efficient inference
Variational principles that optimize the full stochastic bridge path-space geometry, shaping not only what a model learns but how much energy it consumes during inference.
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03
One algorithmic stack across generative tasks
A single algorithmic stack for a broad class of generative modeling tasks that today require separate workflows, objectives, and architectures.
Today's continuous-time generative AI models are limited by the restrictive conditional path laws they assume to make training tractable. The next generation of models will learn the full stochastic bridge geometry instead, optimally and at scale, unlocking significant gains in performance, scalability, and energy efficiency across language, vision, and scientific discovery.
Papers, research previews, product releases.
AI today is held together by scale. The next era will be shaped by optimal bridges.
— Exobyte AI