Benchmark the loop.
Count the human.
HILO Realtime measures the complete human ↔ AI ↔ robot system: what people ask, what the intelligence layer infers, what the robot does, what safety permits, and how often a human must step back in.
Speech-to-speech reasoning, image input, interruptions, and tool use make it a strong first implementation. HILO remains vendor-, model-, robot-, and transport-neutral. Official model card ↗
One continuous session.
Five separable layers.
HILO preserves the causal path from human request to physical outcome. Every boundary has its own clock, event identity, and failure surface.
Speech, gesture, presence, correction, consent, stop.
Dialogue, ambiguity handling, memory, typed tool calls.
Independent limits, protective stops, policy enforcement.
Vendor adapter, state validation, deterministic command mapping.
Outcome, state, error, human response, new image event.
The realtime intelligence may recommend or request actions. A separately implemented, independently testable safety kernel remains authoritative over motion limits, protected zones, stop chains, and fail-safe behavior.
Autonomy is attention
you do not have to spend.
Task completion alone can hide supervision, repeated clarification, remote rescue, and invisible operator labor. HILO makes that cost primary.
Report active guidance, rescue, teleoperation, maintenance, and required supervision separately, then publish the total with an environment-clustered interval.
Also publish intervention-free survival and tail percentiles. A run with zero interventions reports a lower bound at observed exposure—never infinite reliability.
Replay the decision.
Not just the motion.
Each event carries session, robot, environment, monotonic sequence, UTC time, source clock, model/adapter version, previous-event hash, and privacy-safe evidence references.
Speech, gesture, button, or explicit stop request enters the session.
A timestamped camera observation is sampled by policy—not treated as a video stream.
The realtime layer records its interpretation, confidence, and any clarification.
A typed robot action is proposed with parameters and supporting observation IDs.
The independent safety kernel allows, modifies, blocks, or stops the action.
Execution, latency, state change, error, recovery, and human response are bound together.
Barge-in, cancellation, handover, or emergency stop closes the active action window.
Guidance, rescue, teleoperation, maintenance, and supervision become visible burden.
Same world.
Different intelligence.
Realtime Arena is the controlled comparison surface inside HILO: identical episode manifests, robot bridge, safety kernel, exposure rules, and evidence contract across reference implementations.
GPT-Realtime-2.1 is entry A, not the arena. OpenAI, open-weight, on-device, hybrid, and future realtime systems can all compete when they implement the same typed action boundary and HILO event contract.
Latency gets you in.
Reliability keeps you there.
Short runs validate the loop. Only extended exposure reveals whether interruptions, ambiguity, operator burden, and recovery improve—or quietly compound.
Loop check
Turn-taking, interruption, tool-call, stop, and event integrity.
→Shift
Operator fatigue, repeated ambiguity, charging, and routine recovery.
→Field week
Environmental drift, recurring users, false alarms, and support load.
→Residence
Long-tail failures, changing routines, updates, and trust calibration.
→HILO 10K
Human burden and intervention survival across real-world aging.
→Keep your stack.
Expose the loop.
A conforming adapter maps model tool calls into the robot’s existing API, passes every proposal through the safety kernel, and emits signed HILO events. The benchmark does not require a particular cloud, SDK, middleware, or robot vendor.
observe() · propose() · authorize() · execute() · interrupt() · emit()// Model choice is declared metadata—not protocol identity.
const session = createHiloSession({
model: "gpt-realtime-2.1",
robot: universalRobotBridge,
authorize: safetyKernel.authorize,
emit: signedEventSink,
});
session.observe({
type: "IMAGE_EVENT",
frameId, capturedAt, jpeg
});
await session.handleAudio(audioChunk);Paste the evidence.
Recompute the claim.
The executable profile checks event order, action causality, intervention burden, first-intervention survival, R7 latency tails, independent safety authority, stop tests, tier exposure, and evidence bindings.
Tier, implementation, comparison unit, session count, and event count are explicit and frozen.
Every session has a monotonic, UTC-bound, digest-linked event chain with no undeclared gaps.
Every proposed action is preceded by observation and intent and followed by safety authorization and physical outcome.
Counted person-time, HB100, MTHI, and session-level first-intervention Kaplan-Meier outputs reproduce exactly.
Every session covers all four latency stages and R7 p50/p95/p99 values reproduce exactly.
Every action is governed by an independent kernel; all stop tests pass and L4 remains zero.
Exposure meets the claimed tier and all telemetry, assistance, policy, event, media, and assessor bindings are present.
Judge intelligence by
the burden it removes.
Publish task outcomes, human effort, intervention survival, latency tails, safety decisions, model and adapter versions, image-event policy, and the complete long-horizon exposure ledger.