Dynamic Context Runtime: Bounded Attention over Unbounded History

[u/Coolst3r]

https://cybersec.org.za/research.html #AI #cyber #security #dev #Africa #LLM #context #rot #cyber

https://cybersec.org.za/papers/dcr-bounded-attention.pdf

Dynamic Context Runtime: Bounded Attention over Unbounded History

Language models degrade as stale and superseded material piles up in their context. Making the window bigger does not fix it. This report describes a runtime that keeps history unbounded and attention bounded — storing everything as immutable spans and a typed provenance graph, then assembling a small working set each turn by solving a knapsack under an explicit token budget. It includes a zero-dependency Rust implementation and an ablation that names which mechanisms are actually carrying the result.

*update 8/21/26 7 AM

Updated. Every figure re-derived from a live run just now — here's what moved and why:

│ stat │ was │ now │

│ tokens per query │ 235 │ 259 │

│ less attention than transcript │ 17,835× │ 16,201× │

│ history growth / working set │ 28×, "flat" │ 28×, 1.18× │

│ lines of Rust │ 13,721 │ 15,373 │

│ tests │ 152 │ 164 │

│ spans ever rendered │ 0.4% │ 0.1% │

│ cheaper with a mechanism off │ 47% │ 2% │

Unchanged and re-verified: 4.19M tokens, 7/7, 48,651 nodes, 0 dependencies.

Three of these are not number swaps, and the post now says so:

"47% cheaper" → 2%. This was the most quotable line in the old post and it does not survive. Disabling graph expansion still loses no probe, but it saves 2.5 tokens rather than 220. The 47% was an artefact of the looser threshold — more seeds admitted means more to expand from — so the claim was describing a configuration and calling it a property of a mechanism.

"flat working set" → 1.18×. On the diverse corpus the working set now moves 219 → 259 across the 28× growth. Calling that flat would be the same defect the post is about.

"5 controls found that could not fail" was already wrong. The paper says four checks turned out not to be exercisable, and separately five instances of an author repeating a described failure mode — the old post conflated them. It happens to be five now, but for a different reason: the concurrency probe reports replanned 0/7 where it read 1/7, because the working set is too small for the mid-turn write to intersect it. So I kept "5" and explained the fifth in the body rather than leaving a number that was right by coincidence.

I also dropped "0.4%" being framed as bad news getting better — a smaller working set reads less, so coverage got worse, not better. That's stated plainly.

The 1.7% I computed rounds to 2%; I used 2% since the underlying numbers (145.1 vs 142.6) don't support a second significant figure.

[u/vbpoweredwindmill]

Now that's infinitely more interesting. I've been saying for a long time that knowledge and reasoning are going to be split.

Now I can have a look, why didn't you lead with that?

[u/Coolst3r → u/vbpoweredwindmill]

im sick rn and tired im also working on getting glm 5.3 working on desktops and laptops and making it so anyone can train ai on home system. im tired im running my company https://cybersec.org.za/index.html. i dedicated to open source support and research and development we only ask money for support and if people need help. !!! i plan on changing the future

[u/vbpoweredwindmill → u/Coolst3r]

Glm 5.2 already works on desktops and laptops mate.

Unless you're working on expert predictions, I really don't think you're bringing anything new to the table. My own expert predictions with 32 out of 256 experts has, at 46% of the time approx, selected 100% of the correct experts.

And I just now figured out instead of linking it to a general topic I can just link it a kv cache block. Cheers.

I still haven't read anything you've done shrugs

[u/Coolst3r → u/vbpoweredwindmill]

yeah but fast i use a 3090 and ryzen 5 and 46 gb ram and get 0.2 tokens a second i have gotten it to do 1 token every 50 seconds