Vetch

With the aid of SOTA LLMs, you can encode specialist intelligence into computer programs without going through the roundabout route of training small LLMs. And even when you do go the route of ML, it doesn't necessarily have to be an LLM or even a Neural network.

The more interesting challenge is the one you eliminated, complexity, in getting efficient logistics and arrangements. Dyson Sphere Program (until late in its existence) and Satisfactory don't have waves of enemies attacking your bases but remain challenging and a satisfying puzzle. I remember I used a constraint solver to help me optimize some late game recipes for Satisfactory.

Deterministic yes, robust deterministic no. The most likely conjunction is not always the best nor representative of what the model is considering unless its certainty is high.

Putting hallucination aside, LLM "theory of mind" has gotten worse over time. I feel it peaked in Opus 3 and Sonnet 3.5, GPT 4 and then GPT 4.5 for OpenAI. Since then, even with Opus 5.5, phrasing has needed careful crafting, in order that it not be taken too literally. OpenAI models suffer from this much more than Anthropic models but Claudes have backslid over time too.

This means when writing documentation, tutorials or commit messages, their output is often a garbled jumble. Assuming shared context, using invented terminology without explaining, leaking conversational states due to improper epistemic boundaries and failing to model the reader. This all usually leads to their freely generated explanations being terrible. Getting good explanations requires chaining questions that force them to line things up properly, which is not easy the less you know. These failures as something LLMs naturally struggle with make sense, given the nature of attention and RL with weak signals from human data.

Math is not merely a collection of proofs, it's a way of understanding. A proof presented in a manner that cannot be incorporated remains useless. It does not make it's way to physics like Riemannian geometry and matrix math did. This is no less true when done by humans too.

Your hallucination conclusion, checking if a proof is one, is exactly the counterproductive cost.

Most of us cannot verify that the claims in the OpenAI lore dump are in fact all correct. It will take tons of work from experts to do this. It took subject expert mathematicians to identify the discrepancy and disconnect in the Navier Stokes proofs, for example. LLMs will struggle to make use of their own proofs or turn them into knowledge that accumulates over time.

The act of proving is often more valuable than the proof itself. Human constraints and limitations force us to invent tools and abstractions that a 100,000 x 1M context swarm can bypass. The tradeoff from that AI swarm advantage is work that doesn't usually lend itself to being built upon. It's like doing all the side quests and reading all the books of an RPG versus min maxing a straight path with a guide. We might try to identify new abstractions, but the fact that we don't get access to CoT and that much of it will be illegible means mining LLM traces for what human mathematicians produce naturally will be a tedious chore.

Permutation City is not "far more interesting", it is a complementary exploration of consciousness and the nature of reality. Both are excellent (also both very relevant i-pumps). Permanence by Karl Schroeder is another really good complementary exploration of the idea of consciousness as transitory, a symptom of not being good at the thing that is being consciously cogitated. A cat is so good at being a cat, it does not need to waste resources on clumsy meta-aware calculations on how to cat.

DebertaV3's architecture and noising should be even better as a basis because it had a couple inductive biases (cross encoder, disentangled attention and RTD corruptions) that enabled it to have unmatched weight performance ratio on such tasks.

My gut tells me that a better approach to a calibrated 0-shot classifier than shoehorning DiffusionGemma would be starting from another gemma, T5GemmaV2. Take its encoder and do continual training on an RTD objective and a large relational synthetic data mix. Then finetuning (multi-annotator data will help calibration) on as many proper NLI datasets as possible. That still lacks the DebertaV3 disentangled attention's inductive bias, however.

Jev also has its calibrated predictions component which is important. Temperature scaling is probably the easiest first pass. But there's lots of sensible options to improve on that.

ModernBERT might be the easier, more stable starting point than T5Gemma though.

QAT, which bitnet training is a form of, helps a ton in preserving accuracy at such low bits per parameter. There are also better quantization approaches that try to preserve the most sensitive weights† but are computationally expensive and so not typically done. Another complementary option is, if the model is fast enough, we should be able to push up correctness by self-consistency voting at close to T=1. Smart/fast Zero-shot classifiers like the recent Jev could help with aggregation across answers too, extending applicability.

†Every paper I've read estimates the average information content of transformer LLMs at about 3-4 bits per parameter. Curiously, biological synapses are also estimated to be about 4-5 bits per synapse, possibly a bit lower.

The most charitable explanation I can think of for this is something like regression to the mean. When a model is first released, there'll be a subset of users who, just by chance, sample the highest quality band of the distribution that answers their query. Some of them will rush over to social media and post about how amazing a model is. Over time, those users' mental model of responses will converge but they'll perceive the model's return to typical performance as a downgrade.

This guess/explanation predicts that most users won't match what the initial social media hype claims, doesn't discount user experience as simple habituation nor does it assume companies are lying when they say there have been no changes to the model itself (quantization included).

I also think there's an aspect where initial testing is more forgiving because the more persnickety polish bits can be ignored and tests are likely to have similar structure to things that can be trained for. Meanwhile, actual specific work items are a broader unusual distribution with more stringent acceptance criteria.

Personally, I can detect a separation between Sol and Astra (but not as large as that between Opus and Fable). While they can solve most of the same problems, Astra takes less time, is less frustrating to talk to, is cleaner, notices more, spins wheels less and requires less corrections.

This work feels more like The Truth Mines in Diaspora. Permutation city seems relevant only if you think LLMs are hosts to minds.

The weights + the architecture are already 100% of the code, the transformer is just a mathematical expression + helper programs whose sources are provided. The transformer itself is not even a stateful program, so a it is no more a binary than Piet or Tromp's BLC are. It's merely incomprehensible. Training isn't compilation either, since training a model is closer to program induction and the data are samples defining the solution space.

What constitutes serious work and how seriously have you tried to do serious work with them? While those trying to claim a 30B dense model can match Opus 4.6 are engaging in either beyond over-excessive over-exaggeration or performing rather routine tasks, it's disingenuous in the other direction to claim the latest open 1T models are not useful for serious work. I find those making such claims have rarely spent more than a few minutes on halfhearted attempts and often on recently obsoleted models.

Openweight models turned a corner around kimi 2.6, deepseek v4 pro/flash, hy3 and mimo 2.5 pro. Similar to how closed LLMs turned a corner around gpt 5.2 and opus 4.5.

While they remain a step behind closed frontier models, for real world tasks ranging across functional reactive programming, distributed systems, mathematical modeling, to-the-millisecond highly optimized spatial data-structures, complex compute shaders and shader effects and non-trivial systems involving parser combinators and algebraic effect systems, I can say that open models have very recently gone from useless to productive. For my work, mimo v2.5 pro is hands down better than sonnet 4.6.

Interesting thought to consider in principle but fails because gorilla brains continued to evolve too, just along a different path. They're not snapshots of ancestral species locked in time.

The relu/if-then-else is in fact centrally important as it enables computations with complex control flow (or more exactly, conditional signal flow or gating) schemes (particularly as you add more layers).