From perception to execution.
We use large language and vision models to interpret the world and reason about intent.
We deliberately own the boundary where interpretation becomes action. This boundary is where most systems fail. Xolver is built to hold it.
What exists today
Xolver's foundation model work is real and active today, with the architecture already shaped around bounded physical intelligence rather than unconstrained model output.
In development
The deterministic safety and enforcement layer is in development, along with the edge runtime that will carry bounded execution and observability into production environments.
Why the split matters
We do not treat interpretation, enforcement, and execution as the same problem. Physical systems need these boundaries to stay safe, explainable, and operationally useful.
How the layers interact.
Xolver is not a single model making unchecked decisions. The system is deliberately separated so each stage has a clear role and a clear boundary.
1. Interpret
Perception and state understanding form a probabilistic view of the scene, task, and likely next move.
2. Propose
The model proposes intent and candidate action sequences within a bounded domain.
3. Constrain
Deterministic enforcement checks policy, timing, kinematic feasibility, and safety limits before anything physical happens.
4. Execute
The runtime carries out allowed actions locally, logs deviations, and escalates rather than improvising when conditions fall outside tolerance.
Robotics Foundation Models
Research and Pilot
These models convert raw sensory input into structured understanding. They ingest vision, depth, motion, and proprioceptive signals and produce a probabilistic view of the world along with inferred task intent.
They are powerful interpreters, not decision makers.
By leveraging self-supervised learning and large-scale behavior cloning, our models generalize across cluttered and unstructured environments. We do not attempt to build a single general intelligence for every body and every environment. Our models are domain bounded by design, allowing them to reason deeply about specific physical contexts like industrial sorting, mobile manipulation, and last-mile logistics.
What happens here
- Vision, depth, and internal state ingestion
- World state hypotheses generation
- Task intent inference
What does not happen
- No direct actuation
- No safety guarantees
- No irreversible decisions
Model X1-D
Our flagship Vision-Language-Action (VLA) foundation model.
Translation and Enforcement
Pilot and Production Adjacent
This is the most critical layer in the system.
Outputs from learning-based models are probabilistic and unconstrained. Physical systems are not. Before any action can occur, intent must be translated into something that is safe, valid, and executable.
The Xolver Enforcement Layer acts as a Deterministic Gatekeeper. It uses formal methods to verify that the high-level intent from the foundation models does not violate collision boundaries, singularities, or safety zones. This ensures that the robot never performs a move that is kinematically impossible or operationally prohibited.
This layer enforces reality.
Validation Composition
- Symbolic constraints and rules
- Kinematic feasibility
- Timing and ordering guarantees
- Operational policy gates
Explicit Failure Behavior
Halt. Log. Escalate.
Refusal is not an error; it is a designed outcome. Escalation routes to a human interface for explicit acknowledgement.
Why this cannot be a single model
A model can be very good at interpreting the world and still be the wrong place to assign final authority over physical action. Probabilistic inference is useful for perception and intent. It is not enough on its own for bounded motion, policy adherence, or deterministic failure handling.
Enforcement Layer
The deterministic gatekeeper for physical AI.
Edge Runtime
Production in Constrained Environments
Xolver runs where the world is.
Closed-loop control executes locally with strict latency budgets. Decisions that affect physical systems do not depend on round trips to the cloud.
Local execution matters because physical systems do not wait for network conditions to improve. Safety, timing, and bounded response have to hold at the point of action.
The runtime continuously
- Executes local control loops
- Monitors system health and state drift
- Verifies execution against intent
Architectural Privacy
Raw sensor data stays local. Privacy is an architectural choice, not a compliance afterthought. Cloud connectivity exists only for observability and audit.
Xolver Edge Console
Interactive telemetry, joint diagnostics, and local NPU performance.
* Hardware and robotics platform currently under development.
Core Concepts
World models
A world model is not a map. It is a persistent representation of physical state that evolves over time and explicitly represents uncertainty.
Planning happens across sequences, not frames. This enables safer behavior in noisy, partially observed environments.
What this enables
- Tracking entities through occlusion and ambiguity
- Establishing causal ordering of events
- Planning safely under sensor noise and drift
Time and causality
Physical systems unfold over time. Order matters. Xolver treats time as a first-class variable. Events are ordered. Causes are inferred.
This matters for safety and accountability. When something goes wrong, the system can explain not just what happened, but why it happened.
Why this matters operationally
- Events can be reconstructed in order
- Operator acknowledgement paths stay visible
- Audit trails remain tied to actual physical outcomes
Failure handling
Failure is inevitable; silent failure is unacceptable. Xolver follows a deliberate cycleValidate → Execute → Monitor → Refuse → Recover.
Refusal is a feature. When ambiguity exceeds tolerance, the system stops and escalates rather than guessing. Recovery paths are explicit and observable.
Read more on safety constraintsWhat we do not do
We draw boundaries deliberately
- We do not manufacture hardware
- We do not replace PLCs or existing control systems
- We do not promise general intelligence
- We do not remove humans from oversight
Boundaries are not a lack of ambition. They are how physical systems achieve reliability.
Where Xolver fits.
Xolver sits between interpretation and actuation. We do not replace the entire automation stack. We provide the intelligence boundary that connects learned perception to bounded physical behavior.
- Models interpret state and propose intent
- Enforcement checks what is allowed
- Runtime carries out bounded execution locally
- Existing plant systems and human oversight remain part of the loop
Failure without this architecture.
Models alone are too probabilistic to hold final authority over motion. Control logic alone becomes brittle when environments drift. Cloud-dependent autonomy introduces the wrong failure modes into physical systems.
Xolver's architecture exists to separate these concerns so the system can adapt where it should, constrain where it must, and refuse when confidence is not enough.
A concrete example.
Consider a warehouse vehicle asked to move inventory to a staging area while avoiding a restricted zone and adapting to a blocked aisle.
Foundation model
Interprets the environment, tracks obstacles, and proposes a task-level reroute based on the current world state.
Enforcement
Rejects any candidate route that crosses a restricted zone, violates traffic rules, or exceeds motion constraints.
Runtime
Executes the allowed route locally, monitors drift during motion, and keeps response inside latency limits.
Failure behavior
If no safe route exists, the system refuses, logs the reason, and escalates instead of improvising a potentially unsafe action.
Decades of breakthroughs in months.
Explore the chronological timeline of algorithmic and architectural advancements that power Xolver's physical intelligence.
FAQ
Does Xolver replace robotics middleware or controllers?
No. Xolver sits at the intelligence boundary between learned perception and physical execution, while existing middleware and control systems remain part of the stack.
What happens when a model proposes an action that fails enforcement?
The action is rejected before it reaches the machine. The system can halt, log the reason, and escalate rather than turning an invalid proposal into physical behavior.
Why can't critical physical loops run through the cloud?
Critical physical loops are sensitive to latency, connectivity loss, and timing drift. Running them locally keeps response bounded where the machine is actually operating.