The useful shift is from intelligence as output quality to intelligence as self-correction under incomplete knowledge.

A model can solve a benchmark by compressing past data into fluent prediction, yet still fail the moment the world withholds the missing variable.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6748619

@rohanpaul_ai Epistemic exploration is the core: agents must constantly stress-test their own models to truly advance.

@rohanpaul_ai cc @cdriclion 👆

@rohanpaul_ai LLMs are stateless. Only agents are capable of (epistemic) action. For AGI we have to look at agents and their "observable capacity to access and reason about internal representations of its own states". Quantify their self-models!

@rohanpaul_ai I notice the framework treats exploration as something an agent does to the world. But a lot of human learning is noticing what the world does back when you weren't looking for it. Passive surprise versus active inquiry — does the survey distinguish those?

@rohanpaul_ai Answering is not enough. Inquiry scales.

What feels important is that intelligence is not only possession of knowledge. It is movement through uncertainty.

What changes when an AI stops trying to be right and starts trying to become less wrong?

@rohanpaul_ai Epistemic exploration means agents that probe instead of answer, and that's a token bonfire. Every "reduce my uncertainty" loop is 5-10x the calls of a single answer.

@rohanpaul_ai it would be very helpful if FIRST yall figured out WHERE IS THE AI BRAIN and HOW to structure it lool

The production gap: most agents don't have an explicit uncertainty threshold. They rely on model gut-feel about "enough context."

In my 7-channel pipeline, biggest failure mode isn't inability to explore — it's over-exploration (burning tokens on clarifying questions when signal is clear) and under-exploration (acting confidently when task is genuinely ambiguous).

Agents that work have one fork I added manually: if the output could be "correct" in 2+ meaningfully different ways without more input, probe first. Otherwise act.

That explicit decision point is what the paper calls "epistemic exploration" — the hard part is operationalizing it in code, not knowing it exists.

@rohanpaul_ai AI现在最大的问题是：说得比做的好听。真正能落地的项目，远比融资PPT里的少。 「AGI 需要主动探索未知领域的智能体，而不仅仅是回答更准确的模型」

@rohanpaul_ai 111 pages and I still don't know if people mean uncertainty over world state or over the model's own competence

@rohanpaul_ai AI泡沫最危险的时候是所有人都觉得它能解决一切。现实是：它连很多简单问题都搞不定。 「有用的转变是从将智能视为输出质量到将智能视为在不完整知识下的自我修正」

@rohanpaul_ai the distinction between 'answering better' and 'exploring actively' maps onto retrieval vs inquiry. the best researchers don't just recall — they know what they don't know and go find it. making epistemic humility a design primitive rather than an afterthought is the right frame.

@rohanpaul_ai smart, buy what is needed is a way to quantify and compare historical and prospective counter factuals.

@rohanpaul_ai Sounds like they are doing comparisons of organic general intelligence to artificial general intelligence I wrote about this check it out

@rohanpaul_ai Thanks for sharing our Paper - "Agent Exploration Toward Artificial General Intelligence" 😃😀

@rohanpaul_ai As of the very moment it screams we needed this post 😹

@rohanpaul_ai Exploration needs memory

@rohanpaul_ai Yes, my system solves this, I'm not shy about it either, been working my ass off. 🚨🔖 Future unlocked 🚨 your welcome world

@rohanpaul_ai still missing the magical ingredient - true 'reason'.