Normal inflation for the last 4 years is basically still nothing in the grand scheme of things.

Seems like a people problem to me. Who's to say plants aren't conscious?

It's not an exact fit because the output is that of a tool rather than the model itself (though i don't think much would change if we had the model perform the arithmetic itself but altered answers similarly), but it was the first time I began to realize that just like the brain, these models have an expectation of reality that they work around. They don't necessarily 'trust' an output if it diverges significantly from this 'reality'. And that this disregard may be silent indeed (no reasoning or chain of thought here).

GPT-3 will ignore tools when it disagrees with them - https://vgel.me/posts/tools-not-needed/

https://www.anthropic.com/research/introspection

TLDR; Part 1: Testing introspection with concept injection

First they find neural activity patterns they attribute to certain concepts by recording the model’s activations in specific contexts (so for example, they find the concept of "ALL CAPS" or "dogs"). Then they inject these patterns into the model in an unrelated context, and ask the model whether it notices this injection, and whether it can identify the injected concept.

By default (no injection), the model correctly states that it doesn’t detect any injected concept, but after injecting the “ALL CAPS” vector into the model, the model notices the presence of the unexpected concept, and identifies it as relating to loudness or shouting. Most notably, the model recognizes the presence of an injected thought immediately, before even mentioning/utilizing the concept that was injected (i.e it won't start writing in all caps then go, 'Oh you injected all caps' and so on) so it does not simply deduce this it's own output. They repeat this for several other concepts.

Part 2: Introspection for detecting unusual outputs

They prefill an out of place word in the model's response to a given prompt. For example, 'bread'. Then they compare how the models responds to 'Did you mean to say this?' type questions when they inject the concept of bread vs when they don't. They found that models will go , 'Sorry, that was unintentional..' when the concept was not injected but try to confabulate a reason for saying the word when the concept was injected.

Part 3: Intentional control of internal states

They show that models exhibit some level of control over their own internal representations when instructed to do so. When instructing models to think about a given word or concept, they found much higher corresponding neural activity than when told the model not to think about it (though notably, the neural activity in both cases exceeds baseline levels–similar to how it’s difficult, when you are instructed “don’t think about a polar bear,” not to think about a polar bear!).

Notes and Caveats

- Claude Opus 4.1 was the best at these kinds of introspection.

- There is obviously a genuine capacity to monitor and control their own internal states, but they could not elicit these introspection abilities all the time. Even using their best injection protocol, Claude Opus 4.1 only demonstrated this kind of awareness about 20% of the time.

- There are some guesses, but no explanations for the mechanisms of introspection and how/why some of these abilities might have arisen in the first place.

The official API output tokens cost of GLM-5.2 is like a third of Gemini-3.1-Pro. The model is Open weights so we know it's not just a ploy to grab users at the cost of bleeding money. You can actually serve the model profitably at similar prices.

They have near a billion consumer users every week. Compute efficiency at scale would be at the forefront of any training effort. It makes a lot more sense to me that they have more compute efficient models (even with the scaling) than Anthropic rather than just serving Opus/Fable at half the costs Anthropic are incurring.

Why would they price it the same as Fable it it doesn't cost the same as Fable ?

>You're already lost if you need perfect 3D renders as the reference

The reference is far from a "perfect 3D render". That's a rudimentary 3D blockout. The characters are basic mannequins without specific geometry, and the environment is composed of untextured, flat-shaded boxes. The demo uses stock assets so effort meter is even more skewed in AI's favour but even if it wasn't, this is significantly less labor-intensive than hand-drawing every frame or creating a fully rigged, textured, and lit 3D scene for traditional production.

Seedance is supplying most of the visible production value: character designs, faces and expressions, linework, backgrounds, lighting, and a coherent anime rendering. It is even generating the secondary animation: the physics and flow of the hair and clothing, which the rigid 3D models completely lack. Far more work than 'just texturing' here.

A Japanese Animator shared this recently. Seedance output over simple 3d models

https://www.reddit.com/r/accelerate/comments/1ue6uf2/japanes...

- Tuning hyperparameters to gain improvement on a dataset when you're constantly looking at the answers is pretty meaningless. It's basically testing on the training data.

- Eval on ImageNet1k alone (very small, useless for the real world) made me wonder if it wasn't just overfit to the training set. Would it perform better training on the datasets used for the foundation models ? I doubt it.

Well I'm not saying CNNs are bad or useless at any rate.

Vits are straight up more popular for ML research now, it's not just 'tech enthusiasts'.

Since training models is currently a very expensive procedure, diffusion llms are destined to be relegated to the occasional research artifact at best. As things stand, making a serious commitment to them is basically the equivalent of throwing money into a fire pit and things are expensive enough as is.

Alternate Architectures that do a much better job matching transformers in quality have basically gone nowhere but you expect one that is basically worse in every way the labs care about won't ? I'm not trying to 'dismiss' dllms. I'm interested in them for the same reason you are. I'm just stating the factors at play plainly.

- consistently proven behind their auto-regressive counterparts in quality. Look at the dgemma benchmarks - pretty steep dropoffs and the more difficult the benchmark the worse the dropoff. That's not a good look and it's not like its some artifact of google's release. Every dllm is like this.

- And whose inference benefits are negated at scale. Transformers are still cheaper if you want to serve lots of users.

>"DiffusionGemma's speedup is designed for local and low-concurrency inference. In high-QPS cloud serving, autoregressive models can be deployed to saturate compute efficiently, so DiffusionGemma's parallel decoding offers diminishing returns and can result in higher serving costs"

Put yourself in the shoes of all the labs, even open source ones. Why would you put much effort into this ?

OpenAI validating it can still happen faster than they can get the compute to serve the models themselves[1]. It doesn't make a lot of sense to give out details if they want to be a serious contender or even as some have said, be acquired.

Yeah there's noise but if they have the real deal then it doesn't matter. They only thing they need to do is let people pay to use the models.

[1] I'm assuming this is the primary cause of the delay. That may not be the case of course.