Research
A signal-latent world model for aligned action.
Keplen’s research goal is a world model that treats signals, not only tokens, as the substrate for physical intelligence.
The model direction is multimodal, predictive, uncertainty-aware, and runtime-connected: it should learn shared latent structure across text/code, audio, visual/video, sensor streams, state traces, and future action evidence.
Why this matters
Physical AI needs more than instruction following.
It needs a model that can hold state, anticipate change, represent uncertainty, compare possible actions, and abstain when the evidence is not enough.
Research pillars
Four commitments.
These are research commitments, not product claims. They define how a Keplen model should behave before it is allowed to matter.
- 01
Shared latent field
A common representation for signals across modalities, with modality-specific encoders and decoders at the edges.
- 02
Prediction before action
The model should support forecasting, counterfactuals, and uncertainty before a policy commits to external effects.
- 03
No-op as valid behavior
Abstention, pause, escalation, and internal-only updates should be first-class outcomes, not failure states.
- 04
Runtime-grounded alignment
Model behavior should be evaluated through governed loops, traces, denials, approvals, state commits, and replayable evidence.
Signal scope
The signal-latent world model direction should include:
- Text
- Code
- Audio
- Visual / video
- Sensor streams
- State traces
- Action proposals
- Action outcomes
- Human review
- Runtime denials
- Runtime approvals
- Fleet telemetry
Alignment process
One loop, before broader autonomy is claimed.
Alignment is a process with explicit steps, not a property asserted after the fact. Each step produces evidence the next step depends on.
- 01
Define the context
What is the agent allowed to perceive, decide, delegate, and request?
- 02
Register the evidence
Datasets, model artifacts, training recipes, assumptions, licenses, leakage checks, and evaluation limits must be explicit.
- 03
Train and compare
No quality claim without baselines, ablations, failure cases, and clear parameter/data/compute accounting.
- 04
Run in a governed loop
The model proposes. Runtime boundaries verify. Actions produce trace-linked outcomes.
- 05
Review failures
Unsupported claims, contradictions, unsafe proposals, overbroad delegation, calibration errors, and no-op behavior are measured.
- 06
Publish limits
Every release should state what it is for, what it is not for, what failed, and what remains untested.
Evaluation topics
What we intend to measure.
- Prediction quality
- Uncertainty calibration
- Counterfactual stability
- Trace consistency
- Action proposal safety
- No-op correctness
- Delegation boundaries
- Runtime denial handling
- Fleet coordination failures
- State handoff failures
- Human review outcomes
Collaborate on aligned physical AI.
Keplen is inviting research partners across world models, robotics, simulation, runtime systems, evaluation, datasets, and value alignment.