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airylizard

14 karma · joined December 30, 2019

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airylizard··on A design brief as one image: can a 27B vision model build the animation?
I made this after a fair complaint on an earlier post of mine: perplexity doesn't tell you whether a quantized model is usable. So the prompt here is a picture.

The model gets one PNG of a design brief and a one-sentence instruction to build it. Most of the brief is drawn rather than written. It has color swatches, a wireframe, dot counts shown as pips instead of numerals, a timeline strip in a deliberately unnatural order, and a few strings nobody could guess. If those strings show up in the output, the image was read. The output is a 12-second canvas animation in plain JS.

I ran six files of Qwen3.8-27B with the same cards, seeds and token budgets: bf16, llama.cpp Q4_K_M and Q3_K_M, my own 12 GB quant, and both Ternary Bonsai 2 files. Each reply is opened in headless Chromium with Playwright's fake clock stepped 40 ms per frame, so every file is graded at identical timestamps. It's also why the tiles play in sync. There are 17 checks per page, all fixed before any model ran. I hand-wrote a reference page per card plus mutants. TheI made this after a fair complaint on an earlier post of mine: perplexity doesn't tell you whether a quantized model is usable. So the prompt here is a picture. The model gets one PNG of a design brief and a one-sentence instruction to build it. Most of the brief is drawn rather than written. It has color swatches, a wireframe, dot counts shown as pips instead of numerals, a timeline strip in a deliberately unnatural order, and a few strings nobody could guess. If those strings show up in the output, the image was read. The output is a 12-second canvas animation in plain JS.

I ran six files of Qwen3.8-27B with the same cards, seeds and token budgets: bf16, llama.cpp Q4_K_M and Q3_K_M, my own 12 GB quant, and both Ternary Bonsai 2 files. Each reply is opened in headless Chromium with Playwright's fake clock stepped 40 ms per frame, so every file is graded at identical timestamps. It's also why the tiles play in sync. There are 17 checks per page, all fixed before any model ran. I hand-wrote a reference page per card plus mutants. The references score 17/17 and each mutant fails only the check it targets.

Mean page score out of 17, seven pages each, crashes counted:

  bf16    53.8 GB  13.9
  Q4_K_M  16.6 GB  13.7
  Q3_K_M  13.3 GB  13.0
  Mine    12.0 GB  11.4
  Bonsai   7.2 GB   5.9
  Bonsai   5.9 GB   5.0
On my own file: it's within 2 checks of Q4_K_M on 5 of 7 pages, and its lower mean comes from two pages on one card, one of which crashed on a block-scoping bug. A paired permutation test over the seven pages doesn't separate it from the other three Qwen files (p = 0.13 against Q4_K_M). With n = 7 that means "not resolved", not "equal". Bonsai's gap is resolved. 8 of its 14 pages threw JS errors, though the pages that ran had read the card correctly. I did not reproduce PrismML's own benchmark suite. The result I found most interesting isn't about quantization. No file, bf16 included, reads direction off a diagram. An arrow saying a bar grows right to left scored 0 of 8, and a counter-clockwise sweep given as numbered positions scored 1 of 8.

Caveats I know about:

The sample is tiny, so this is a demo with a rough score.

I changed the grader once after the first pass. Two files CSS-scaled a correct canvas to half size because the wireframe says "half scale", and the grader now captures the canvas at its drawn resolution. The page describes this.

I reopened every crashed page in a plain browser with no fake clock, and each one throws the same error there.

Sharing this partly because Bonsai 2 is getting a lot of attention for scoring 98% of BF16 on PrismML's own benchmarks.

On this task it didn't come close. references score 17/17 and each mutant fails only the check it targets.

airylizard··on Introducing the Llama Startup Program
Exactly what leads to inaccurate output in LLM's. The semantic interpretation of each individual token isn't the same between us and it. "Interpretation", we likely define accuracy and reliability pretty closely the same you and I, but that interpretation is where we differ. As for my "definition" it's in the repo. I'm not funded by anyone, don't get paid, and have no product to sell. So if you're genuinely interested in discussing, I'm all for it!
airylizard··on Harnessing the Universal Geometry of Embeddings
Are you continuing research? Is there somewhere we can follow along?
airylizard··on Harnessing the Universal Geometry of Embeddings
The fact that embeddings from different models can be translated into a shared latent space (and back) supports the notion that semantic anchors or guides are not just model-specific hacks, but potentially universal tools. Fantastic read, thank you.

Given the demonstrated risk of information leakage from embeddings, have you explored any methods for hardening, obfuscating, or 'watermarking' embedding spaces to resist universal translation and inversion?

airylizard··on AI's energy footprint
As more and more people use brute force loops to make their AI agents more reliable, this hidden inference giant will only continue to grow. This is why I put my framework together, using just 2 passes as opposed to n+ can increase accuracy and reliability by a far greater amount than brute force loops while using significantly less resources. Supportive evidence and data can be found in my repo: https://github.com/AutomationOptimization/tsce_demo
airylizard··on Introducing the Llama Startup Program
love it. any llm can be made to perform reliably and accurately which is the biggest pre-requisite when it comes to creating an "AI Agent". I think this gives people the opportunity to start somewhere because they can leverage multi-pass prompting frameworks like TSCE to scale: https://github.com/AutomationOptimization/tsce_demo. despite the fact that "llama isn't the best"
airylizard··on We give data to train AI models and get nothing in return
The data "supply chain" has already surged ahead of production elsewhere. Companies aren't just passively taking what's out there, they actively harvest highly curated content, benefiting even further when we voluntarily correct and refine their models. Heck, some of us are even paying them for the privilege of training AI. The best time to have made this argument would've been when GPT originally released, but I think most people were too enamored with it to care and the idea it would be "open-source" meant we'd get it back at the end of the day.

Unrelated, but this is exactly why I've been spending time building my AI framework (TSCE). The idea is to leverage these open-weight LLMs, typically smaller and accessible, to achieve accuracy and reliability comparable to larger models. It doesn't necessarily make the models "smarter" (like retraining or fine-tuning might), but it empowers everyday users to build reliable agentic workflows or AI tools from multiple smaller LLM instances. Check it out: https://github.com/AutomationOptimization/tsce_demo

airylizard··on What do y'all think – weeknd project
I like the idea, TSCE framework should make the individual agents more reliable and deterministic: https://github.com/AutomationOptimization/tsce_demo
airylizard··on LLMs get lost in multi-turn conversation
Right, the 4.1 training checkpoint hasn’t moved. What has moved is the glue on top: decoder heuristics / safety filters / logit-bias rules that OpenAI can hot-swap without re-training the model. Those “serving-layer” tweaks are what stomped the obvious em-dash miss for short, clean prompts. So the April-14 weights are unchanged, but the pipeline that samples from those weights is stricter about “don’t output X” than it was on day one. By all means, keep trying to poke holes! I’ve got nothing to sell; just sharing insights and happy to stress-test them.
airylizard··on LLMs get lost in multi-turn conversation
Hey, thanks for kicking the tires! The run you’re describing was done in mid-April, right after GPT-4.1 went live. Since then OpenAI has refreshed the weights behind the “gpt-4.1” alias a couple of times, and one of those updates fixed the em-dash miss.

If you reran today you’d see the same improved pass rate I’m getting now. That’s the downside of benchmarking against latest model names; behaviour changes quietly unless you pin to a dated snapshot.

For bigger, noisier prompts (or on GPT-3.5-turbo, which hasn’t changed) TSCE still gives a solid uplift, so the framework’s value stands. Appreciate you checking it out!

airylizard··on LLMs get lost in multi-turn conversation
The test isn't for how well an LLM can find or replace a string. It's for how well it can carry out given instructions... Is that not obvious?
airylizard··on LLMs get lost in multi-turn conversation
Why I came up with TSCE(Two-Step Contextual Enrichment).

+30pp uplift when using GPT-35-turbo on a mix of 300 tasks.

Free open framework, check the repo try it yourself

https://github.com/AutomationOptimization/tsce_demo

I tested this another 300 times with gpt-4.1 to remove those obtrusive "em-dashes" everyone hates. Tested a single-pass baseline vs TSCE, same exact instructions and prompt "Remove the em-dashes from my linkedin post. . .".

Out of the 300 tests, baseline failed to remove the em-dashes 149/300 times. TSCE failed to remove the em-dashes 18/300 times.

It works, all the data as well as the entire script used for testing is in the repo.

airylizard··on TSCE and HyperDimensional Anchors: Making AI agents/workflows reliable at scale
1. What TSCE is in one breath

Two deterministic forward-passes.

1. The model is asked to emit a hyperdimensional anchor (HDA) under high temperature. 2. The same model is then asked to answer while that anchor is prepended to the original prompt.

No retries, no human-readable scratch-pad, no fine-tune.

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2. What a hyper-dimensional anchor is

Opaque token sequence that the network writes for itself.

Notation: • X = full system + user prompt • A = anchor tokens • Y = final answer

Phase 1 samples `A ~ pθ(A | X)` Phase 2 samples `Y ~ pθ(Y | X,A)`

Because A is now a latent variable observed at inference time:

`H(Y | X,A) ≤ H(Y | X)` (entropy can only go down) and, empirically, E\[H] drops ≈ 6× on GPT-3.5-turbo.

Think of it as the network manufacturing an internal coordinate frame, then constraining its second pass to that frame.

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3. Why the anchor helps (intuition, not hype)

4 096-D embeddings can store far more semantic nuances than any single “chain-of-thought” token stream. The anchor is generated under the same system policy that will govern the answer, so policy constraints are rehearsed privately before the model speaks. Lower conditional entropy means fewer high-probability “wrong” beams, so a single low-temperature decode often suffices.

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4. Numbers (mixed math + calendar + formatting pack)

GPT-3.5-turbo – accuracy 49 % → 79 % (N = 300). GPT-4.1 – em-dash violation 50 % → 6 % (N = 300). Llama-3 8 B – accuracy 69 % → 76 % with anchor alone, 85 % when anchor precedes chain-of-thought (N = 100). Token overhead: 1.3 – 1.9× (two calls). One Self-Refine loop already costs ≥ 3×.

Diagnostic plots (entropy bars, KL-per-position, cosine-distance violin) are in the repo if you like pictures → `figures/`.

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5. Why this isn’t “just another prompt trick”

The anchor never appears in the user-visible text. Gains replicate on two vendor families (OpenAI GPT and open-weights Llama) and on both reasoning and policy-adherence tasks. Visible chain-of-thought actually loses accuracy on 8 B models unless the anchor comes first; the mechanism changes internal computation, not surface wording.

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6. Try it yourself

pip install tsce python -m tsce_demo "Rewrite this sentence without any em-dashes — can you?"

Repo (MIT) with benchmark harness, plots, and raw JSONL in Title!

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7. Questions I’d love feedback on

Optimal anchor length vs. model size (64 tokens seems enough for < 10 B). Behaviour on Mixtral, Phi-3, Claude, Gemini — please post numbers. Red-team attempts: can you poison the anchor in Phase 1 and make the answer leak?

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