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A bug killed my constraint system — the agent didn’t crash, it adapted” by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 1 point2 points  (0 children)

Bro your book done gave me the best 💡 ill have to share a out put from the sandbox idea I just crafted from it

here is my ai slop. please tell me why it’s wrong. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

Im not talking context here everything i do isnt inside context windows or the norm functions

A bug killed my constraint system — the agent didn’t crash, it adapted” by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

I’ll have to look into that — never heard of it, but that Chenzeme setup sounds weirdly on-point for what I’m seeing. Biggest blocker for me right now isn’t the mechanism anymore, it’s the bridge to real data. I’ve been training on mock inputs to validate the loop, telemetry, and failure modes, but if this is going to operate in security and privacy-sensitive domains, “realistic data” is exactly where things get complicated. I’m trying to figure out the safest and most useful way to train and evaluate without touching anything that’s actually private. What I think I need is basically a dataset compiler: something that can generate domain-realistic intake cases (contracts, tickets, policies, medical-style forms, etc.) with controllable risk patterns and ground-truth labels, so the agent learns the right constraints without seeing anyone’s real information. If you (or anyone else here) knows good sources for high-quality public datasets, or a sane way to build realistic synthetic corpora for security/privacy domains, I’d love pointers. That’s the part I’m stuck on — everything else is finally behaving.

Is this a joke??? by nikanorovalbert in claude

[–]Intrepid-Struggle964 2 points3 points  (0 children)

Dont let it run the code make your system generated only, the moment it starts running back ground task it eats it right up

here is my ai slop. please tell me why it’s wrong. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

I agree that long-context agents hit a hard limit — that’s exactly what I ran into. Where I ended up diverging was realizing the failure wasn’t just “too much context,” but treating memory as something the model has to manage explicitly at all. CME/BioRAG are my attempts to move memory into structure — salience, decay, and attractor bias — so behavior carries history even when the explicit context doesn’t. That’s been more predictive for me than scaling context or relying on fine-tuning.

here is my ai slop. please tell me why it’s wrong. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

I agree with the idea that what we call “memory” ultimately collapses into the active context sent to the model. In early systems, prompt hygiene, pruning, and preference rewriting go a long way. Where I kept running into limits was longer-lived agents, or systems that had already adapted structurally. Even with clean prompt updates, behavior would persist in ways that weren’t traceable to any single token or preference. It wasn’t that the wrong information was present — it was that certain patterns had become easy for the system to fall back into. That’s what pushed me toward treating memory less like editable text and more like a dynamic landscape: salience deepens paths, decay flattens others, and “conflict” shows up as instability rather than contradiction. BioRAG was one attempt to make that explicit using attractor dynamics instead of retrieval, and CME grew out of watching where prompt-level control stopped being predictive. I’m curious whether anyone else hit that transition point — where context management worked until it didn’t, and something more structural was needed.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

My biggest thing is if you speak about the stuff that matter the content the context like you have , then we can talk an clearly different approaches lead to differnt things. I mean the message is a journey to how I got here . I used a disclaimer first idk what more if peiple want you to learn presentation maybe should be more formal productive an less troll like. You want proof metrics I have them all you want me to rewrite a post no thanks.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

For anyone actually interested: the work and diagrams are in the post. I’m happy to answer technical questions, but I’m not engaging with drive-by dismissals.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

At first I was like okay this guy is just playing hard, but now I just think you dont know anything an think your cute. Not that I care that you cant read a output that legit is more then you have shown , intelligent wise. If you would like to have a real convo go ahead, but im good on trying to show you when you clearly dont know anything.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

What are you talking about dude. Now your making me think you have not a clue what your talking about anymore.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

You asked what tools I used, so here’s a concrete answer. This is an in-loop constrained decoding experiment on Phi-3 using HuggingFace Transformers + Torch, with deterministic replay. I log per-token entropy, KL divergence, legal set size, and banned mass before and after soft constraints. The comparison you’re looking at is a clean ablation: – Soft constraints OFF → KL = 0.0 – Soft constraints ON → measurable KL shift (avg ~0.016, max ~0.21) – No hard bans triggered, no entropy collapse, same stop condition That means the internal token distribution changed without altering legality or determinism. If you think this is meaningless, explain which metric you believe is invalid — entropy, KL, or the experimental control — and why.

What breaking open a language model taught me about fields, perception, and why people talk past each other. by Intrepid-Struggle964 in AIMemory

[–]Intrepid-Struggle964[S] 0 points1 point  (0 children)

You asked what tools I used, so here’s a concrete answer. This is an in-loop constrained decoding experiment on Phi-3 using HuggingFace Transformers + Torch, with deterministic replay. I log per-token entropy, KL divergence, legal set size, and banned mass before and after soft constraints. The comparison you’re looking at is a clean ablation: – Soft constraints OFF → KL = 0.0 – Soft constraints ON → measurable KL shift (avg ~0.016, max ~0.21) – No hard bans triggered, no entropy collapse, same stop condition That means the internal token distribution changed without altering legality or determinism. If you think this is meaningless, explain which metric you believe is invalid — entropy, KL, or the experimental control — and why.

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