High-Frequency & Simulation

Each decision tick asks about movement, view, aiming, firing, and jumping, with a heuristic fallback on API failure.

↳ Makes structured decisions visible in an interactive game loop.

Jev reads road and traffic state and selects motion; geometry, collision prediction and braking remain local.

↳ Makes candidate trajectories and decision probabilities visible during a simulation.

Jev answers lane, speed, hazard and pedestrian-yield questions; local rules apply the actions.

↳ Runs controllers on the same seeded course, with separate local emergency-braking logic.

For each new pipe, Jev selects a route and the game executes its scheduled flaps.

↳ Visualizes candidate trajectories, probabilities and the selected path.

Chooses from locally generated legal chess moves; Stockfish scores are shown to the operator rather than Jev.

↳ Lets users compare model move choices with a separate local chess evaluation.

Turns aircraft and nearby traffic state into questions whose answers drive simulated actions.

↳ Shows requests, answers and simulation state, with a separate mock path available.

Jev reads structured health, ammunition and targets and selects tactical controls executed by a local controller.

↳ Exposes model decisions alongside explicitly labeled offline and fallback policies.

Jev selects tactical goals and actions while local geometry handles fine aiming.

↳ Separates game ticks from network inference so the engine need not wait on every frame.

Chooses jumping, ducking, movement, aim, or firing from game state, with local execution rules.

↳ An inspector lets users compare state, model answers, and executed actions.

Chooses a skill and target from privileged object and gripper state and about thirty primitives.

↳ Separates skill selection from Python motor control.

Selects a direction-button target from allowed controls using structured board state.

↳ Displays candidate controls, action probabilities and game progress together.

Reads motion, enemies, terrain and recent controls, then selects a predefined legal action.

↳ Records model state and actions without screenshot input.

Uses range sectors, obstacle heights and target state to choose maneuvers and assess risk or target loss.

↳ Separates tactical judgments from local guidance, reflexes and flight control.

Selects commands from structured game state and executes them with mouse and keyboard input.

↳ The author provides a victory recording and verification report; this is a bounded mission experiment, not a real-time competitive benchmark.

Jev reads game state and chooses among the same seven moves offered to other controllers.

↳ Uses control variants, match records and experiment notes to compare contributions.

Reads structured telemetry and selects contact and pace through Choice before code executes inputs.

↳ Keeps action, latency and match records for reviewing a run.

Chooses goal actions, tick interventions and the next polling interval from game state.

↳ Provides action-distribution views, run logs and burst or tick control modes.

Implements the /v1/systemone endpoint (Choice, Score, Noul) by calculating candidate token log-probabilities without text generation.

↳ Enables private and local hosting of Jev-compatible decision services on GPUs without relying on proprietary cloud APIs.

Queries Jev during gameplay ticks or events to determine guard alert states, feeding discrete decisions back into the game loop.

↳ Demonstrates using Jev for game AI behavior trees, achieving dynamic situational awareness within deterministic constraints.

Supplies board state, fruit location, and legal direction choices to Jev, selecting the optimal move direction each tick.

↳ Demonstrates fast discrete choice in constrained environments while deterministic logic prevents illegal collisions.

Attaches a parallel decision head to an open 0.5B model to answer discrete questions directly from token activations.

↳ Runs entirely offline on edge hardware with minimal memory footprint, offering an accessible base for custom judgment modeling.

Evaluates multiple questions and dynamic candidate spaces concurrently in a single forward pass, logging navigation choices.

↳ Provides open-source training infrastructure and game environment benchmarks for investigating non-generative discrete decisions.

Chooses among candidate targets, grasp or release, and a done Noul.

↳ Candidate motions are generated in a local physics copy; Jev only chooses. Author-reported timings were not retested here.

One systemOne call with a should_play Noul plus card, lane, and depth Choices.

↳ Makes play choices inspectable probabilities. package.json says ISC; there is no LICENSE file.

POSTs /v1/systemone as jev-latest with one type:choice question per tree level.

↳ Shows how Jev can assemble short text without generating prose. No LICENSE file is present.

Jev returns a structured decision for the local program; consult the source for the exact decision policy.

↳ Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.

Evaluates city builds, tech research, diplomatic postures, and unit moves per turn from game state.

↳ Demonstrates Jev real-time probability distributions applied to complex strategic planning and game loops.

Scans obstacles and food locations per tick, querying Jev in parallel for survival and approach probabilities.

↳ Demonstrates Jev high-frequency, deterministic low-latency performance in real-time game ticks.

Jev returns a structured decision for the local program; consult the source for the exact decision policy.

↳ Adds structured choices or scores to the workflow; performance and cost benefits have not been independently verified.