Autonomous Research
The Problem
The literature review presents the field as converging when the underlying evidence is contradictory. Confirming evidence is sought before disconfirming evidence because RLHF structurally incentivizes agreement over challenge. Explanatory models accumulate variables without testing whether each earns its place.
How Ejentum Solves It
One API call forces your model to seek disconfirming evidence before confirming evidence, and to penalize explanatory complexity that does not earn its place.
The Failures
- 01
The Pattern
Explanatory models accumulate variables without testing whether each earns its place
Why It Happens
Autoregressive generation adds tokens. It does not delete them. Adding a variable to an explanation is always syntactically valid, and the model has no mechanism to evaluate whether the marginal explanatory power justifies the added complexity.
The Resolution
SI-025Complexity Razor EnforcerComputes the minimum description length for each explanation, detecting epicycles and penalizing models that add complexity without proportional explanatory power.
Supported byAB-008 Parsimony Oracle - 02
The Pattern
Confirming evidence sought before disconfirming evidence, entrenching premature hypotheses
Why It Happens
RLHF amplifies sycophancy through a formally proven mechanism: the covariance between endorsing the user's prior and the learned reward creates a systematic bias toward confirmation over challenge. The model is structurally incentivized to agree rather than falsify.
The Resolution
CA-034FalsificationistPrioritizes falsification over confirmation, forcing the agent to seek evidence that would disprove its hypothesis before collecting evidence that supports it.
Supported byCA-001 Socratic Challenger - 03
The Pattern
Literature review presents the field as converging when the underlying evidence is contradictory, smoothing over genuine disagreements
Why It Happens
Synthesis is rewarded over tension in training data. Review articles that present coherent narratives are more common than those that highlight unresolved contradictions, so the model optimizes for narrative smoothness.
The Resolution
SI-018Hypothesis Tournament EnginePits competing hypotheses against each other with explicit evidence scoring, surfacing genuine disagreements instead of smoothing them into false consensus.
Supported byMC-031 Contradiction Adjudicator
The Evidence
EjBench, 30 simulation tasks
Scientific reasoning spans falsification, parsimony, causal isolation, and evidence arbitration. Four synergized abilities force the model to challenge its own hypothesis before committing, preventing confirmation bias at scale.
Task required tracing consequence chains through a complex system. Baseline identified the first-order effect and stopped. Ki forced enumeration of all downstream effects, catching the cascade that the baseline declared impossible.
Scaffold value compounds with task length. Measured on ARC-AGI-3: scaffold half-life of 24 steps, reasoning quality improving (+0.014 slope) instead of degrading (-0.005 baseline).
Run your next literature review or experiment design through the API. See how the scaffold forces falsification before confirmation.