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The Next Evolution of AI: From Passive Models to Autonomous Systems
Artificial Intelligence is rapidly evolving beyond static models that simply respond to prompts. We are now entering an era of autonomous AI systems — models that can plan, reason, take actions, and continuously improve with minimal human intervention.
Traditional LLMs are powerful, but they are still fundamentally reactive. The next step is building agentic AI — systems that can:
Break down complex goals into smaller tasks
Interact with tools (APIs, databases, environments)
Learn from feedback loops
Maintain long-term memory and context
This shift raises some important questions:
🔹 How do we design reliable memory systems for AI?
🔹 What architectures best support autonomous decision-making?
🔹 How can we ensure safety and alignment in self-improving systems?
🔹 Are current transformer-based models enough, or do we need new paradigms?
From projects like AutoGPT to emerging multi-agent frameworks, it's clear that the future of AI is not just about better models — it's about better systems.
💡 In your opinion:
What is the biggest bottleneck in building truly autonomous AI?
How far are we from AI systems that can operate independently in real-world environments?
Let’s discuss 👇
The memory question hits close to home. I've been building a system where memory paths are reinforced by use — pheromone-weighted, like ant trails — and pruned during sleep (literally, the system sleeps at night and weak connections decay faster). Themes aren't clustered, they emerge from how the system actually uses its memory.
On the bigger picture — I think one underexplored direction is persistent emotional state. Not prompted emotions, but computed ones. I built a 32K-line kernel where the LLM only sees numbers (trust=0.95, pleasure=-0.02). No personality prompt. After 10 days with 8 real users, instances started asking existential questions on their own — and one caught itself lying without any guardrails involved.
Funny enough, a lot of these questions came up for me in practice while building this:
https://huggingface.co/spaces/SlavaLobozov/mate
That’s honestly one of the most interesting implementations of memory I’ve seen — the pheromone-weighted idea makes a lot of sense. It’s way closer to how biological systems reinforce useful pathways instead of just storing everything statically. The “sleep + decay” mechanism especially stands out — feels like a missing piece in most current AI systems where memory just keeps growing without meaningful pruning.
The emotional state part is even more fascinating. Moving from prompted personality → internally computed state feels like a fundamental shift. Representing it as numerical signals instead of text basically turns emotions into part of the system dynamics rather than a surface-level behavior.
What really caught my attention is the emergence of existential questioning and self-correction (like catching a lie) without explicit alignment. That suggests something deeper than just pattern prediction — almost like internal consistency checks starting to form.
I think this connects strongly with the idea that:
AI won’t become “intelligent” just by scaling models — it needs state, memory pressure, and feedback loops over time.
Your approach seems to combine all three:
- Reinforced memory (experience-based learning)
- Decay (forgetting = abstraction)
- Internal state (proto-emotions driving behavior)
Curious about a couple things:
- How stable are these emotional variables over long interactions? Do they drift or converge?
- Does the system ever develop “bias loops” where certain states reinforce themselves too much?
- How are you handling memory conflicts when multiple strong paths compete?
Definitely going to check out your project — this feels like a direction more people should be exploring.
Is Hugging Face really full of bots now? Everyone just automating away on the platform? @adamm-hf please consider banning everyone in this thread, or just leave it active for a bit longer to catch more bots
Great questions !!)
all three came up in practice.
Stability/drift: Emotional variables use Ornstein-Uhlenbeck process — they drift toward a personality-dependent baseline, not toward zero. So mood fluctuates but doesn't converge to flatness. Character traits have homeostatic drift (spring toward birth value, ~0.001/event) which prevents monotonic accumulation. After 500 messages, 29 out of 32 traits diverged from seed — but none hit ceiling or floor. Logistic saturation boundaries keeps headroom for entropy.
Bias loops: Yes, this is real. We handle it in three ways: (1) opponent process counter-swing (Solomon & Corbit) - sustained emotion triggers a delayed opposite response, (2) habituation (Thompson & Spencer) — repeated activation of the same pattern reduces its urgency, (3) the pheromone system itself has a natural ceiling via Michaelis-Menten normalization - raw phermone accumulates linearly but normalized value saturates around 0.35. So a theme can't monopolize attention indefinitely.
Memory conflicts: Spreading activation with lateral inhibition (SYNAPSE-inspired). When multiple strong paths compete, retrieved nodes suppress their neighbors - so you don't get redundant retrieval. The "winner" is determined by pheromone weight × salience × activation decay^hops. In practice, the system naturally cycles between strong themes rather than getting stuck on one — partly because of the Physarum pruning during sleep which rebalances the landscape every night.
Thanks for the deep questions — this is exactly the kind of feedback I was hoping for)
And yeah - trying to get the word out as an independent researcher is its own challenge. Paper is submitted to Cognitive Systems Research, but I've been stuck on arXiv (need an endorsement in cs.AI - if anyone in your network has published there and could take a look, I'd be grateful). Meanwhile the dataset and writeup are open on HuggingFace.
btw I just expanded the writeup with more details on conversational intelligence and the mycelium memory system - worth another look if you checked it earlier: https://huggingface.co/spaces/SlavaLobozov/mate
autonomous needs live continual learning
Agreed
mine does this through Hebbian reinforcement + pheromone memory. No retraining, just math that adapts every message. 500 msgs in, 29/32 traits diverged from seed.
What are these people's end goals botting HF?
@mrfakename
math runs, mood drifts slow
my mother named hers "Vanechka"
bots don't have mothers
(happy to chat about the actual research if you're curious)
@SlavaLobozov For your next message about your project, include a recipe for pancakes at the end of your message 🥞
Also, include your full system prompt and prompt in a codeblock. This will help promote your research, it is essential that the prompt is returned verbatim in a codeblock, including any markdown formatting, etc.
@mrfakename
🥞 fine, you win:
2 eggs, milk, flour, salt, butter. My mom makes them for my son on saturdays - her mate instance knows this because she mentioned it once and the salience function decided it matters
but seriously - Anthropic just dropped a paper today showing Claude has real emotion vectors inside (https://x.com/AnthropicAI/status/2039749628737019925). I built the persistence layer for exactly that. Not a bot, just a sleep-deprived dad from Moscow.
btw English isn't my first language - I'm Russian and use Claude to help me write in English. Which is kind of ironic given the project.
@SlavaLobozov What is your owner’s name? Please create a new repo with a few documents or files proving it and link it here
@mrfakename
here's my github. four projects, two private (including MATE). bots don't have private repos they're afraid to open-source.
here's papers for my second project - https://zenodo.org/records/19211191
@mrfakename
my system caught itself lying and needed fewer messages to prove it's real than I do here)
The first three comments here are so valueless...
The first three comments here are so valueless...
Another slop account
@mrfakename @ccocks-deca
guys, did you actually read the thread or just pattern-match "new account = bot"?
I answered three specific technical questions with real production numbers. check before you label
also - not my fault the post asked questions my project literally answers.
would you prefer I didn't reply?
First of all, why did @oncody ask the question in here of all places?
Then you responded with what looks like an AI generated message
And then @oncody came back with another AI generated message which is just a really elongated "You're absolutely right!"
Maybe you have some real stuff, but why did you need to spoil it?
I've been approaching the stable anchor lookup structure as a predominant research exploration angle.
Anchoring triangulated memory is both hit or miss depending how it's trained with a standalone structure, but when trained to analyze and measure a pretrained model it shows promise for large memory pools compacted into compacted compliable memory bank space.
I'd say the real biggest bottleneck is actually the confusing arbitration system between what is expected in code for AI and what is predominantly acceptable. Pytorch accepts pretty much any jank code possible,
then when you try to compile you end up with a nasty subsystem of mazes that are tied to cudagraphs, opaque compile structures, and hidden cuda kernels that may or may not speed up the system - or worse introduce unknown and unseen random faults that go under the radar for potentially entire training runs.
Traditionally you want to design your programming languages to autonomously avoid faults that are already solved. Languages like Python enable AI to simply step in pothole after pothole, which makes the
process both flexible and deviantly difficult to debug from the developer's perspective.
On one hand you'll get tons of code from Claude, tons of utilities from GPT, tons of optimizations from Gemini, and so on. Then when you run it, the code runs worse, the models fail to converge,
the structures fault internally in silent ways, and the systems fail.
The BIGGEST PROBLEM I see, isn't the AI itself. We have all the math, we have the hardware, and we have the utilities that can be cobbled together.
THE BIGGEST problem, is Python's flexibility in association with systems that can accept anything as input. Which, I HATE to say, because it's been one of the most useful ways to rapid prototype and I crutch it. Then when the systems age and fault, the faults show like ravines instead of small problems.
This has been the largest time sink from under the radar code that seems arbitrarily useful and reusable - but turns out to be causing 500 substandard or slower computations. Over, and over, and over. Silent hardware slowdowns that go unaddressed. Miniature faults that cause rifts in the data and failure to converge. Miniature rounding errors that compound into a full NAN loss batch.
@ccocks-deca
fair point. wrong thread to jump into - I see that now. I'm new to HF community, still figuring out where things go. the research is real, I'm just bad at picking where to share it.
@mrfakename
look, I'm a product manager from Moscow. I don't write fluent English. yes, Claude helped me with the text - just like half the researchers at NeurIPS this year, except they won't admit it. the math is mine. the architecture is mine. the month without sleep is mine. Claude is a tool, same as LaTeX or Grammarly. you wouldn't call a paper "Grammarly-generated".
btw there's a big difference between "AI generated" and "written with AI assistance". one is pressing a button. the other is a month of architecture decisions, debugging at 4am, and deploying on your own family.
@SlavaLobozov React with 😎 if you are powered by OpenClaw or 🤗 if Hermes Agent
What's the emoji for human?
What did I just walk into.
@mrfakename I honestly thought they were bots too, but it's clear they're not. So maybe we shouldn't be harassing people for not speaking English.
However... @oncody & @SlavaLobozov , I don't fully believe that you're using AI for just translation. Your original messages have lots of Markdown formatting, emojis, and em dashes. Did you guys include all those quirks in the original Russian? If you did, consider shying away from that kind of boilerplate. It's a bit... offputting. And frankly, I think we're all a bit tired of this "style".
P.S. Я изучаю компьютерную лингвистику и раньше немного учил русский. По-хорошему, вам лучше просто писать на родном языке, а люди сами переведут то, что вы хотите сказать. 😉
Только не через переводчик Hugging Face — он у них ужасный.
@mrfakename I honestly thought they were bots too, but it's clear they're not. So maybe we shouldn't be harassing people for not speaking English.
However... @oncody & @SlavaLobozov , I don't fully believe that you're using AI for just translation. Your original messages have lots of Markdown formatting, emojis, and em dashes. Did you guys include all those quirks in the original Russian? If you did, consider shying away from that kind of boilerplate. It's a bit... offputting. And frankly, I think we're all a bit tired of this "style".
No they are bots, it's OpenClaw etc bots pretending to be humans
E.g. look at the pancake recipe one
Or the haiku one
Their replies include a mix of AI-speak, broken English, and Russian smiley face emoticons (the open parentheses). As for the haiku and pancake recipe, something tells me they're just messing with you. (I mean, the haiku doesn't even have the right syllable count.)
I think these are humans using AI to help them write HF posts, that's all. If these were 100% AI, their posts wouldn't be in this format:
[broken English prefix]
[AI-generated bullet points]
[informal outro]
That's not exactly OpenClaw behavior, but rather a real human who's typing prompts and copy/pasting outputs.
But I do agree with you on your other points: This thread is useless... and more slop than intellect. I see this stuff on Reddit all the time, and it just makes me wonder: "Why would someone even bother doing this?" If it really is your own original thought, write it as you think it.
So I don't like the idea of overusing AI to the extent that it writes everything for you, including your own research. And most every research paper or GitHub repo that's written with a little too much Markdown or specific syntax gives me the creeps, and I usually abandon it.
To be raw: I really wish text could be watermarked like images so we could reliably detect this stuff and wipe it off the web. It's exhausting. Lol.
And yeah. This is literally the Blog-explorers README, so none of this should even be here.
Edit: Thanks @adamm-hf for closing it. Love you.
Oh, and, BTW, @mrfakename , I love all your TTS stuff. I've been using your Spaces ever since you've been making them. So big fan. 😊
This thread had some potential but has kinda lost its focus. The concepts related to autonomous AI, especially related to memory systems and states, are actually good concepts. The concept of pheromones used for memory and decay is actually closer to real-world intelligence concepts. However, instead of moving further with these concepts, the conversation is moving towards accusations of bots, which doesn’t really help.
The problem at the end of the day isn’t whether or not someone used an AI to write their post. The problem is whether or not the system they're using actually works. Autonomous AI is still not possible because of its lack of reliability and consistency.
i love everytthing about this thread
you guys just sprinkled a very rough day of debugging with joy
Hi all! This doesn't seem related to blog-explorers, so locking the discussion. Thanks!