Task Complexity
Estimate how much computation a task requires before executing the full inference path.
RESEARCH PROGRAM / V0.1
From hypothesis
to measurement.
LowJoule investigates whether artificial intelligence can allocate computation according to the expected utility of additional work, rather than applying a largely fixed computational budget to every task.
RESEARCH OBJECTIVE
minimize E
subject to Q ≥ q*
subject to Q ≥ q*
01 / RESEARCH AXES
Six directions. One objective.
Optimal Stopping
Determine when the expected benefit of additional computation no longer justifies its energy cost.
Dynamic Depth
Allow inference depth to vary according to task difficulty and intermediate model state.
Conditional Routing
Activate only the computational components expected to contribute meaningfully to the current task.
Reasoning Reuse
Retrieve reusable intermediate computation when retrieval costs less energy than recomputation.
Energy-Aware Scheduling
Incorporate measured or estimated energy cost directly into computational policy decisions.
02 / EXPERIMENTAL ROADMAP
Initial falsifiable experiments.
Each LowJoule experiment receives a persistent identifier and should define a hypothesis, baseline, intervention, quality metric, energy measurement method and acceptance criterion before execution.
LJ-EXP-001
Task Complexity Heterogeneity
Determine whether different tasks require measurably different computational depths to achieve the same target quality.
STATUS / PLANNEDVARIABLE / DEPTH
LJ-EXP-002
Marginal Utility Decay
Measure the quality improvement generated by successive computational steps and test for diminishing marginal returns.
STATUS / PLANNEDVARIABLE / ΔQ ÷ ENERGY
LJ-EXP-003
Adaptive Early Exit
Compare a fixed-depth baseline against an adaptive stopping policy under a predefined quality tolerance.
STATUS / PLANNEDVARIABLE / STOPPING POLICY
LJ-EXP-004
Future Utility Prediction
Evaluate whether the expected gain from additional computation can be predicted from intermediate model states with bounded error.
STATUS / PLANNEDVARIABLE / ΔQ̂
LJ-EXP-005
Measured Energy Dominance
Test whether an adaptive policy reduces measured energy consumption relative to a fixed-compute baseline while remaining inside a declared quality tolerance.
STATUS / PLANNEDVARIABLE / JOULES
03 / EXPERIMENT TEMPLATE
Every experiment begins before the result.
HypothesisWhat exactly is being tested?
BaselineWhat is the control policy?
InterventionWhat computational behavior changes?
WorkloadWhich tasks and samples are evaluated?
Quality metricHow is useful performance measured?
Energy metricHow are joules measured?
AcceptanceWhat result supports the hypothesis?
UncertaintyHow are variance and error reported?
04 / RESEARCH PIPELINE
Theory → prediction → experiment → evidence.
01Formalize
Define the mathematical claim.
→
02Predict
Derive an observable consequence.
→
03Measure
Execute a controlled experiment.
→
04Publish
Release methods and results.
05 / RESEARCH PRINCIPLE
Energy efficiency is scientifically meaningful only when the useful work preserved by the optimization is measured alongside the energy that was removed.
06 / NEXT MILESTONE
LowJoule Experimental Baseline.
The first implementation milestone is a reproducible measurement environment capable of recording model quality, inference latency, computational depth and physical energy consumption for identical workloads under fixed and adaptive policies.
Target
Establish the experimental infrastructure required to execute LJ-EXP-001 through LJ-EXP-005 and publish comparable results.