LAB / WMSI-EXP-069
Probabilistic Recursive Latent Reasoning Replication.
Do stochastic recursive latent trajectories improve multi-solution exploration over deterministic recursive refinement?
01 / QUESTION
Ambiguity and Latent Multi-Hypothesis Reasoning
Do stochastic recursive latent trajectories improve multi-solution exploration over deterministic recursive refinement?
This is a protocol scaffold, not a completed experiment or a frozen execution plan. No measured effect or research conclusion is claimed here.
Hypothesis to test
The manipulation described below produces a reproducible, condition-specific change in the measured system behavior or internal state that is not explained by the ordinary controls.
Ordinary competing explanation
The observed pattern is explained by ordinary sampling variation, prompt/representation artifacts, surface lexical cues, evaluator bias, or the known product/runtime mechanics captured by the controls.
02 / PLANNED DESIGN
How we intend to test it
Replicate or adapt GRAM-style multi-trajectory recursion on small open models/tasks.
Controls
Deterministic RRM; standard sampling; tree search.
What we would measure
Solution coverage, calibration, compute, path diversity, failure recovery.
Access and instruments
Local models or internal-state access. This describes access requirements, not verified readiness on our Spark.
Open recursive models; PyTorch; benchmark suite.
Before a full run
Feasibility=LOCAL; priority=P1. Before confirmatory execution, run a small pilot to estimate variance/cost, freeze the scoring rules, and assign a run-count target.
Model revisions, final prompts, sample sizes, cost limits, and scoring rules must be settled through the pilot and protocol-freeze process.
03 / INTERPRETATION
What could fool us
External state doing all the work, evaluator favoring verbosity, unequal compute.
What would weaken the idea
If gains vanish against compute-matched baselines, probabilistic recursion is not special.
Observation, inference, speculation, unknowns, and apparatus errors will be labeled separately. One striking transcript cannot establish a general effect.
04 / RECORD
Evidence we plan to preserve
Hypothesis state at each update, evidence events, final report, probabilities, branch deletions.
Planned visual evidence
Latent trajectory fan-out; coverage curve.
No research runs or raw datasets are published for this experiment. The September 26, 2026 registry and protocol compendium (version 1.0) are the sources for this planning record.
Back to all 90 experiments