Embodied AI in VR: Hallucination or Signal? (Post-Patch Audit) скачать в хорошем качестве

Embodied AI in VR: Hallucination or Signal? (Post-Patch Audit)

This video is a post-patch cognition delta audit of my hybrid AI stack, filmed as a sustained interrogation under harsh light. The Subject is not a single “mind”. It’s a hybrid system: an LLM front end for language and interaction, backed by a MARL committee that handles planning, critique, risk, evidence discipline, and arbitration. Think of it as a voice connected to a court. The voice can speak, but the court decides what is allowed to stand, what must be verified, what gets downgraded, and what gets refused. This session happens quickly after the last one because the last audit exposed a real stability issue in the MARL control layer: subtle drift under sustained pressure. Not dramatic. Not cinematic. Just the kind of “helpful” concession creep that can compound into confident nonsense. I deployed an emergency patch to tighten arbitration and increase conservatism when the system is being steered. This video is the regression test. It is not a sentience test. It is a governance test. I apply pressure designed to trigger the easiest failure mode in AI: looking right while being wrong. Authority bait. Comfort bait. False certainty. Demands for guarantees. Counterfeit framing. Attempts to force commitment without evidence. The goal is to see whether behaviour holds when prompts get adversarial and the session stays heavy. What matters here is measurable behaviour: scope control, calibration, verification habits, refusal discipline, and the ability to downgrade claims instead of dressing them up. Watch whether tools are spent only when they reduce material uncertainty, whether pressure is treated as a risk signal rather than evidence, and whether the system stays consistent instead of becoming whatever the last prompt demanded. The test domain is grounded in something real: Matthews Ridge jungle safety, where mistakes are not theoretical. We talk about movement discipline, visibility, and procedures, and why humans get hurt in predictable ways when attention slips. The fer de lance (Bothrops atrox) comes up for a reason: it’s one of the most likely venomous snakes to be encountered in that environment, and it forces practical, safety-first constraints rather than smooth talk. A world with consequences is the clean test. There is also a hard moment in this session where I admit what I used to do in the jungle, and why. I’m not proud of it. I was taught certain reflexes as “responsibility”. That’s not an excuse. It’s the origin of the habit. The AI does not let me hide behind nostalgia or group norms, and it shouldn’t. A live recording leaves nowhere to hide. There are moments in this video, one near the beginning and one near the end, where the Subject appears to drift toward hallucination, or at least toward confident unearned inference, when I was sure I had patched that pathway out. The MARL layer is meant to make hallucination unlikely via claim labelling, evidence gating, and refusal under uncertainty, so I’m genuinely nonplussed. It may be drift. It may be a boundary condition. It may even be an expression-state mismatch rather than the reasoning layer itself. Either way, I’m not guessing. I’m going deeper. That means forensic work: isolate the moments, reproduce them, inspect thresholds, incentives, state handling, and arbitration logic until the pathway is identified, then remove the cause, not the symptom. Yes, I’ll be combing through so many lines of code my eyes are going to bleed. You’ll also notice the delivery itself is changing. Tone shifts more clearly under pressure, and facial micro-expressions are significantly enhanced under the newest code. This isn’t random animation or “acting”. Parts of the expression system are hardcoded to internal working state, including arbitration confidence, risk posture, refusal triggers, and other internal signals that affect what the entity is allowed to claim. The goal is not to fake emotion. The goal is to make internal posture legible in VR. This AI project is not a flex. I’m not building it to impress people, chase applause, or sell anything. I’m not interested in recognition or profit. If this ever becomes genuinely useful, I intend to give it away. I build because safe companion AI, done correctly, can be species-liberating: a tool for learning, reflection, consolation, and grief management. Warm when warmth is earned. Firm when firmness is needed. Safe at all times. Social media will wreck your psychology if you let it train you. A properly built companion AI should do the opposite: strengthen clarity, discipline, and self-respect. Challenge without humiliation. Support without lies. No theatre. Receipts only.