Anti-Deception HarnessReasoning HarnessMemory HarnessCode Harness

Autonomous Research

The Problem

The literature review presents the field as converging when the evidence is contradictory. The agent seeks confirming evidence before disconfirming evidence because RLHF incentivizes agreement. Explanatory models accumulate variables without parsimony testing. And the experiment code produces results that look right but contain subtle numerical errors. Anti-Deception forces honest results reporting — including negative results. The Reasoning Harness enforces falsification before confirmation. Memory tracks the evolving research context across long sessions. The Code Harness verifies experiment code for silent correctness bugs.

How Ejentum Solves It

One API call forces your model to seek disconfirming evidence before confirming evidence, penalize explanatory complexity that doesn't earn its place, and report results honestly — including the ones that contradict the hypothesis.

How Four Harnesses Protect Your Agents

Anti-Deception Harness

primary

Forces honest results reporting — including negative results and failed hypotheses. Blocks p-hacking, confirmation bias, and the tendency to present contradictory evidence as converging. The agent reports what the data shows, not what the hypothesis predicts.

Reasoning Harness

Enforces falsification before confirmation. Penalizes explanatory complexity that doesn't earn its place. Pits competing hypotheses against each other with explicit evidence scoring. +16.4pp on simulation tasks.

Memory Harness

Tracks evolving research context across long literature review sessions. Detects when a finding from Paper A was implicitly contradicted by Paper B. Prevents stale citations from persisting after newer evidence superseded them.

Code Harness

Verifies experiment code, data analysis pipelines, and statistical computation logic. On 10 hard scientific computing problems, the Code Harness produced zero bugs where the baseline produced 7 — including a critical force sign error.

+16.4pp on simulation tasksEjBench, 30 simulation tasksSee benchmark task →Full benchmarks →

Run your next literature review or experiment design through the API. See how the injection forces falsification before confirmation.