Many Observations.
One Evidence Trail.
Arc-Fusion is the evidence layer of the Collaborative Autonomy Ecosystem. It brings supported sensor observations into a common frame, preserves provenance and uncertainty, and helps operators or authorized tools compare, alert, and act within program-defined bounds.
Many observations.
One accountable track.
Arc-Fusion normalizes observations from program-selected sensors, preserves where each claim came from, compares them in space and time, and exposes a confidence-aware evidence picture to operators and authorized mission tools.
- 01
Ingest
EO/IR, RF, LiDAR, acoustic, and TAK
- 02
Normalize
Time, location, identity, confidence
- 03
Correlate
Propose associations without hiding uncertainty
- 04
Review
Evaluate evidence against mission policy
- 05
Route
Alert, record, request, or publish when authorized
Configuration note / Fusion confidence and authorized actions depend on sensor quality, mission policy, model configuration, and human-oversight settings.
From Observations to Accountable Decisions.
Normalize
Configured adapters align supported observations by time, location, identity cues, and confidence while retaining the original source.
Correlate
Rules or validated models associate compatible evidence into candidate tracks and keep conflicts and uncertainty visible.
Review or Route
The system alerts an operator or routes an authorized request through explicit policy, approval, and integration boundaries.
Raw feeds become
an accountable picture.
Vision, telemetry, RF observations, and mission context can converge through program-selected adapters. Arc-Fusion preserves provenance, exposes uncertainty, and gives the operator a reviewable evidence picture instead of another wall of disconnected feeds.
Inspect fusion capabilitiesCorrelate Evidence. Keep the Source Visible.
Program-Selected Inputs
Adapters can bring EO/IR, RF, LiDAR, acoustic, telemetry, ADS-B, AIS, and TAK observations into a common evidence model. Available sources, timing quality, and local processing depend on the integrated sensor and platform configuration.
Evidence Correlation
Arc-Fusion can compare observations in time and space while retaining source, confidence, and uncertainty. Association logic proposes shared tracks without hiding contradictory evidence or treating a model output as ground truth.
Bounded Tool Orchestration
A configured local model can query evidence, prepare a collection request, or recommend an operator action through an explicit tool allowlist. Model choice, permissions, compute placement, and approval boundaries are set by the program.
Provenance-Rich Alerts
Configured rules and models can flag geofence, loiter, or sensor-corroboration conditions. The operator receives the contributing observations and confidence context; alert quality remains bounded by sensor coverage and model validation.
Policy-Bounded Workflows
Authorized integrations can record evidence, publish a CoT event, or prepare a re-task request. Execution authority, human approval, safety constraints, and downstream effects remain explicit parts of the system configuration.
Shared Evidence Picture
A common view organizes supported observations, candidate tracks, and their provenance for review and replay. Sharing is available through program-selected interfaces and PACE bearers rather than an assumed network or universal sensor picture.
Sovereign by design.
Open by choice.
Arc builds on open-source foundations, including LlamaFarm and Atmosphere. Programs can deploy core workflows inside customer-controlled infrastructure, define data boundaries, and negotiate model, sensor-data, and mission-log rights in the acquisition terms. Open interfaces preserve an exit path without promising that every mission dependency is open source.
Put Arc
on the mission.
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