We also introduce an agentic streaming inference framework that supports thousand-second-scale generation while mitigating drift.

As GLM-5.2 extends the maximum context length from 200K to 1M tokens, coding workloads are expected to shift substantially toward longer prompts. This shifts the primary inference bottleneck from computation to KV-cache capacity, long-context kernel overhead, and CPU-side overhead.

Instead, with IndexShare, the KV cache of $h_5$ includes only $kv_{1:4}$, all from the hidden states of the target model. For training, we reuse both kv cache and topk indices of the first mtp step.

the lack of KV sharing across requests leads to redundant prefill computation and wasted memory.

the KV cache has emerged as a primary performance and cost bottleneck because its size grows rapidly with context length and batch size. Limited GPU memory forces frequent recomputation, cache eviction, or spilling to storage

inference is not just a compute problem; it's increasingly a memory scaling problem.

Every time you ask ChatGPT a question, your request triggers a data relay race. Information leaves memory, passes through a CPU for preprocessing, travels to a GPU for heavy computation, and then makes its way back and that entire journey repeats for every single word the AI generates.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining. A small model, by reading itself, can iterate toward answers that neither it nor any of its workers could reach in a single pass.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining. A small model, by reading itself, can iterate toward answers that neither it nor any of its workers could reach in a single pass.

The question it forces is not which model is best. It is who owns the inference layer your organization depends on, what happens when the economics of that layer stop being subsidized, and whether the thing in your pocket turns out to matter more than the thing in the datacenter.

a lightweight surrogate trained on them can absorb a significant portion of future traffic at near-zero marginal inference cost

TPU 8i is designed with more memory bandwidth to serve the most latency-sensitive inference workloads, which is critical because interactions between agents at scale magnify even small inefficiencies.

We see continued gains from inference scaling on larger projects, suggesting they may be solvable given enough tokens.

Training on fields themselves forces the model to learn the physics that produces S-parameters, rather than learning to approximate the mapping directly.

19世紀の経済学者ジェヴォンズは、蒸気機関の効率向上によって石炭の消費効率が上がると、かえって全体の消費量が増えることを見出しました。

during the inference phase, the framework invokes both memory mechanisms synchronously

Using vLLM high-throughput LLM serving on DGX Spark provides a high-performance platform for the largest Gemma 4 models

I'm using this logic as as to build spacetime. But I think it's going to give an even more powerful approach. I don't have to minimize some free energy principle. I I have a more direct computational way

that's sort of the the approach that Fristristen is taking to and his company is taking toward toward this. Um intelligence is somehow about minimizing surprise

Carl Fristen and a new company where they're using something called active inference

Inter-node communication stalls: high batching is crucial to profitably serve millions of users, and in the context of SOTA reasoning models, many nodes are often required. Inference workloads then resemble more training.

What is heavy should be local, and what is light should be global and shared.

What’s missing obviously is a viable third option that would disrupt and transform the status quo by leaning into and operating from an awareness of the emerging future

An Active Inference Model of Collective Intelligence

Kevin Mitchell says in one of his books free agents he talks about I 00:27:10 move therefore I am is that yeah yeah no that's that's that's that's exactly right and all the work on um uh uh active inference

The thing most obvious about the type systems of Java, C, C++, Pascal, and many other widely-used “industry” languages is not that they are statically typed, but that they are explicitly typed.In other words, they require lots of type declarations. (In the world of less explicitly typed languages, where these declarations are optional, they are often called “type annotations”.) This has nothing to do with static types. continued

Performing optimization in the latent space can more flexibly model underlying data distributions than mechanistic approaches in the original hypothesis space. However, extrapolative prediction in sparsely explored regions of the hypothesis space can be poor. In many scientific disciplines, hypothesis spaces can be vastly larger than what can be examined through experimentation. For instance, it is estimated that there are approximately 1060 molecules, whereas even the largest chemical libraries contain fewer than 1010 molecules12,159. Therefore, there is a pressing need for methods to efficiently search through and identify high-quality candidate solutions in these largely unexplored regions.

Causal Deep Learning Authors:Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

To avoid such bias, a fundamental aspect in the research design of studies of causalinference is the identification strategy: a clear definition of the sources of variation in the datathat can be used to estimate the causal effect of interest.

Matching: This approach seeks to replicate a balanced experimental design usingobservational data by finding close matches between pairs or groups of units andseparating out the ones that received a specified treatment from those that did not, thusdefining the control groups.

Emphasizing lifetime-polymorphism can also make type inference untenable, a design choice that wouldn’t fit OCaml.

Nooria never goes for water, nor does Mother.Maryam doesn't, either. She doesn't have to doanything!

The Delta Method, from the field of nonlinear regression. The Bayesian Method, from Bayesian modeling and statistics. The Mean-Variance Estimation Method, using estimated statistics. The Bootstrap Method, using data resampling and developing an ensemble of models.

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

collecting and checking the content of declarations of private interests, of personal data that are liable to disclose indirectly the political opinions, trade union membership or sexual orientation of a natural person constitutes processing of special categories of personal data, for the purpose of those provisions.

those provisions cannot be interpreted as meaning that the processing of personal data that are liable indirectly to reveal sensitive information concerning a natural person is excluded from the strengthened protection regime prescribed by those provisions, if the effectiveness of that regime and the protection of the fundamental rights and freedoms of natural persons that it is intended to ensure are not to be compromised.

Hermeneutic circle   In traditional humanities scholarship, the hermeneutic circle refers to the way in which we understand some part of a text in terms of our ideas about its overall structure and meaning -- but that we also, in a cyclic fashion, update our beliefs about the overall structure and meaning of a text in response to particular moments.

As I think today microservice can do much more than just gives predictions using a single model, like:

And I declare, on my word of honour, that what I am now about to write is, strictly and literally, the truth.

so now finally we get to active inference all this discussion and we're finally getting to the point here right for his lab so um i had and i had already touched on 01:47:35 some of this before but um it would you know today if you're going to develop a really good ai system you're and you're going to have a you have a robot saying the robot has to act 01:47:47 in some environment it is pretty well understood that that if you program that robot to you give it a you give it a i mean traditionally you'll give it a a a fitness function or some kind of 01:47:59 valuation function and it's for example it's good if it it you know you lose points if you fall through a trap door and is and you get points if you uh you know whatever 01:48:11 find find the piece of cake or something well that's uh that's fine for extremely simple universes that your robot might work in but as soon as you get beyond you know as soon as you get to any kind of more realistic uh 01:48:24 universe that your robot has to work in that pre-programming pre-programming concept just kind of falls apart it is you you it would require the the the practitioner to think ahead of all the 01:48:37 things that the robot might encounter and then how to value certain you know value those situations in certain ways uh and that is really uh what active inference 01:48:49 offers is a is a kind of a cognitive understanding or a mechanism by which an organism will uh uh where its 01:49:00 fitness score is in a sense involves both uh you know achieving goals and exploring its world to for for for epistemic gain so 01:49:16 um that's what we would like the that's how we would like to program the robot in a sense so that it can learn from it can learn on the fly from its experiences it can it can alter its actions and 01:49:30 goals as it be as it becomes clear as it gathers more information from its universe as it as it meets new situations that were never never conceived of by the by the 01:49:42 programmer that it through through an active inference or an active inference like uh you know mechanism it can learn and explore and and critically balance exploration with 01:49:54 exploitation and then we come right back to that whole concept of criticality so you know what you would really like your robot to do is remain at that critical uh phase between 01:50:06 exploring what's out there and making use and gold directed behavior of what's in front of it and um and uh you know that's how you could program this world this robot to act in the world and be pretty good at 01:50:20 it you know if you if you build it well so that's what the systems of a society can help a society to do you you don't you it's worth talking about building new systems i think it would not be wise to say 01:50:32 this checklist of like we wanted this level of education we want to want this you know to react this way in this situation react this way in this situation and this level of uh you know whatever money and this level of this and this 01:50:45 level of that while those kinds of preferences can be a useful start society has to be alive in its moment you know in the moment as society is alive it's cognating it's 01:50:57 it's it's it's actively uh you know comparing what it's the result of its actions to the model that is in its head and uh so active inference offers this way 01:51:09 to uh to balance uh exploration and and uh and uh exploitation and remain critical and remain optimally cognitive right so that's part of it 01:51:24 uh and then part of it i mean and for me this the the the idea of the embodied uh you know the three four e's uh this is what i really am attracted to in 01:51:46 active inference is in a sense it's kind of a simple concept it's not really very complicated you know if you've studied bayesian uh theory it all it's kind of straight you know in a way it's kind of straightforward 01:51:58 but the the you know the way fristen has connected the dots and and and and uh extended that into the bigger picture of life kind of it it to me it is uh it is rich 01:52:11 there's a there's a lot yet to be learned and gained and explored in this umbrella of active inference

inference to increase the coverage of Wikidata data considerably

assumes that the function identifier f has a particular type

While no serious climate scientist doubts the fact that human activities are causing climate change, this can’t be proved through experimentation on another Earth.

“provenance” — broadly, where did data arise, what inferences were drawn from the data, and how relevant are those inferences to the present situation? While a trained human might be able to work all of this out on a case-by-case basis, the issue was that of designing a planetary-scale medical system that could do this without the need for such detailed human oversight.

They reflect an area of science known as biological taxonomy, the classification of organisms into different groups.

discover how these mountain people identified relatives and friends

Rather than studying people, ethnography means learning from people

without Brady‟ s knowledge and approval

“You good Jonny boy? ”; “You doing good?

speaking by telephone three times in the hours after the game for a total of 37 minutes and 11 seconds

McNally‟s knowledg e that Brady prefers footballs inflat ed at the low end of the permissible range and his express request that the referee set the balls at a 12.5 psi level

Brady and Jastremski shortly after suspicions of ball tampering became public on January 1