Have you ever had an AI agent confidently say, "Task completed successfully!" only to find out it didn't actually change the file, or swallowed an error and returned exit code 0?
We call this False Completion. It's the silent killer of AI agent reliability.
Recently, we ran a simple experiment: we gave an untrained base model ten completed trajectories from a code-execution sandbox. Four of them had demonstrably failed (the code didn't run), and six had succeeded. We asked the model to act as a judge.
The result? It answered PASS on all ten trajectories.
Not only was the evaluator optimistic, but it was a constant. And this isn't just an anecdote—it's a structural mathematical failure.
Why LLMs are structurally blind to their own mistakes
In the AI industry, the standard solution is to use a stronger LLM as a judge (LLM-as-a-Judge). But this creates a problem called Same-Origin Complicity.
In our latest paper, The Mathematical Limits of AI Reliability, we formalized this using causal graphs (DAGs). When the evaluator and the generator share the same observation and the same prior (the same base model), the verdict is just a measurable function of that pair.
In information theory terms: I(Verdict; Truth | Observation, Prior) = 0.
This isn't an accuracy problem that can be fixed by better prompts or more fine-tuning. It’s an identity. If you and the judge share the same blind spots, no amount of retraining will make the judge see the truth.
The Solution: Causal Grounding and Four-Valued Verdicts
To solve this, we stopped trying to fix the judge and built an independent Causal Execution Layer (Sidecar).
It completely ignores the agent's narration and only checks physical witnesses outside the agent's control:
- Git diffs and file hashes
- Actual exit codes
- Third-party API responses
Instead of a binary Pass/Fail, we use a four-valued verdict:
-
PASS: Claim matches the physical witness. -
FAIL: Claim was attempted, but nothing landed. -
INVALID: No independent witness was found. (The most important distinction). -
NOT_RUN: The task was never attempted.
See it in action
Here is a screenshot from our live demo dashboard, running on a mobile 5G network. The system just caught an agent (robot_arm_01) claiming success while the physical witness denied it. The agent's trust score was immediately penalized by 25 points, and the ledger recorded a FAIL.
(Insert your mobile screenshot here)
The Math and the Code
We wrote two 40-page preprints detailing the engineering and the mathematical limits of this architecture. The core discrete logic is machine-verified in Lean 4 (0 errors, 0 warnings, no sorry). We even did a DPO fine-tuning experiment to prove that text-only alignment just makes models stay silent; it doesn't teach them to seek truth.
If you are building agentic workflows, you can't just rely on self-evaluation. You need a witness.
👉 Read the papers (DOI):
- Paper 1 (Causal Evolution Dynamics): https://doi.org/10.5281/zenodo.24032997
- Paper 2 (Mathematical Limits): https://doi.org/10.5281/zenodo.24041061
👉 GitHub Repository (Source + Lean 4): https://github.com/liao-zhizhi/causal-evolution-paper
👉 Live Interactive Demo: https://demo.silicon-probe.com (Click "Simulate False Completion" to see the red light)
We don't claim unconditional superiority; we claim a verifiable baseline. Would love to hear how you handle "silent failures" in your agent harnesses.
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