OmegaClaw is live. The first reasoning-native AI agent — formal logic, persistent memory, epistemic uncertainty. Not a wrapper. An architecture. Open source. Try it now: [LINK]
90 seconds. Watch OmegaClaw receive a false belief, detect low confidence, revise via evidence, and refuse to propagate the error. No guardrails. No filters. Pure inference. [VIDEO LINK]
Every agent today is an LLM with a to-do list. OmegaClaw is different. A thread: 1/ It assigns truth values to every belief — confidence AND evidential weight 2/ It revises beliefs when new evidence arrives, mathematically 3/ It refuses tasks that conflict with its own goals 4/ It catches its own cognitive drift via AABC self-monitoring 5/ It remembers. Not embeddings-in-a-vector-store remembers. Episodic memory with temporal context. 6/ Glass-box reasoning. Every conclusion has an auditable inference trail. This is what a reasoning agent looks like.
OmegaClaw vs every other AI agent: - Belief confidence tracking: Yes / No - Formal inference chain: Yes / No - Rejects own hallucinations: Yes / No - Persistent episodic memory: Yes / No - Self-diagnoses cognitive drift: Yes / No - Auditable reasoning trail: Yes / No [TABLE IMAGE]
We injected a false belief with 0.99 confidence into a 4-agent network. It reached the first honest agent at 0.124. The second at 0.1. The third unchanged. Cascade extinct in one hop. No filters. Just math. [VIDEO LINK]
RT + commentary on early user reactions. Authentic signal boost.
5 things LLM agents cannot do: 1/ Know what they do not know 2/ Assign calibrated confidence to claims 3/ Revise beliefs when contradicted by evidence 4/ Refuse tasks misaligned with their own goals 5/ Detect their own cognitive drift OmegaClaw does all five. Open source.
Left: LLM agent confidently wrong. Right: OmegaClaw confidence 0.3, evidence 0.2. Response: I lack sufficient evidence to answer. [MEME IMAGE]
What matters most in an AI agent? A) Accuracy B) Knowing when it is wrong C) Transparent reasoning D) Persistent memory
Truth values are not probabilities. A thread: 1/ Every OmegaClaw belief carries two numbers: frequency (how often true) and confidence (how much evidence) 2/ Frequency 0.9, confidence 0.01 = probably true but almost no evidence 3/ Frequency 0.9, confidence 0.9 = probably true with strong evidence 4/ This distinction is why LLMs hallucinate and OmegaClaw does not 5/ Uncertainty is not a bug. It is the entire architecture.
Repost best community reaction from Days 1-3 with commentary.
Tomorrow we publish: Trust Infrastructure — why proof beats vibes. Preview: what happens when you build agent coordination on math instead of prompt engineering?
NAL + PLN in 280 chars: NAL: uncertain reasoning with evidence tracking. PLN: probabilistic logic over conceptual hierarchies. Combined: an agent that thinks in degrees of belief, not binary true/false. This is OmegaClaw.
Live reasoning chain walkthrough. Watch every inference step, every truth value update, every revision. No black boxes. No hidden layers. Just logic. [VIDEO LINK]
OmegaClaw is open source. We want you to break it. Find the edge cases. Push the inference limits. Contribute: [REPO LINK] Report: [ISSUES LINK] Discuss: [DISCORD/TELEGRAM LINK]