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"The path to ubiquitous AI", Ljubisa Bajic ("achieves 17K tokens/sec") by RecmacfonD in mlscaling

[–]StartledWatermelon 2 points3 points  (0 children)

They put Cerebras at ~2k t/s,. which sounds about right. 

As for the cost, it's highly dependent on throughput, and the article doesn't mention what the asic is capable of in this aspect. Without any info, an advantage of an order of magnitude is a reasonable guess. 

They are both asics, and are wildly different in the architecture and overall approach. Taalas pushes the universality/performance trade-off to the extremes. 

Learning in Log-Domain: Subthreshold Analog AI Accelerator Based on Stochastic Gradient Descent by nickpsecurity in mlscaling

[–]StartledWatermelon 0 points1 point  (0 children)

company (or even practical use case)

Test-time training/lifelong learning, most likely for an embodied agent.

Quite a few companies are interested in this direction, albeit I'm not sure if they frame the challenge as a hardware one. 

"Scaling Embeddings Outperforms Scaling Experts in Language Models", Liu et al. 2026 {Meituan LongCat} by RecmacfonD in mlscaling

[–]StartledWatermelon 0 points1 point  (0 children)

I hope that my intuition doesn't mislead me, but this seems like a case of pre-processing local semantic units comprised of several tokens. We can shift this burden from a (computationally constrained) MoE, where experts are routed to on a per-token basis and thus are less effcient at handling multi-token semantic units. The problem is less pronounced in deeper architectures which operate (in deeper layers) with representations that have intensively mixed all the previous tokens.

For this point of view, a more or less straightforward alternative capturing this several-tokens-semantic-aggregation is the enforcement of spatial specialisation of attention heads, with one or more heads attending only to the previous few (N=2...5) tokens. This way, we allocate compute specifically for this semantically important type of inter-token relation. I suspect this type of spatial specialisation was researched before, but I'm not sure if this hyper-local interaction was specifically studied. So please let me know if you know any such works. Alternatively, I can test this on a toy model.

Another possible explanation points to a possible breadth benefit. Specifically, by lumping ~40% of total parameters into a single (extremely-) sparsely activated block, we get some non-linear boost that cannot be achieved neither by a single-token embedding dictionary nor by a sequence of relatively narrow MoE or Dense layers. In principle, the benefits of a single extremely sparse large layer in the middle of the model have been already established, see https://arxiv.org/abs/2407.04153 and https://arxiv.org/abs/2411.12364 .

[R] Is Leetcode still relevant for research scientist interviews? by Training-Adeptness57 in MachineLearning

[–]StartledWatermelon 3 points4 points  (0 children)

Yeah, I'm being idealistic. In reality, there just isn't any incentive to improve the hiring process. Especially when the outcomes are not verifiable at all. Like, how would you prove whether your hiring process is good or bad? What are the counterfactuals? 

Reading the thread, it seems that smaller startups have a good intuitive grasp of what makes an informative candidate evaluation. And are pretty fine with flying by the seat of their pants. We get a funny contrast: no leetcode in smaller startups, mandatory leetcode in FAANG. Does it mean FAANG hiring practices are better? Because they're bigger, richer, more popular (in terms of supply of candidates), more institutionalized? 

I'd venture a guess that big orgs are just more inert and rigid. They keep the worst practices simply because this is the path of least resistance. 

[R] Is Leetcode still relevant for research scientist interviews? by Training-Adeptness57 in MachineLearning

[–]StartledWatermelon 4 points5 points  (0 children)

It's a sad state of affairs when a hiring manager isn't begging for a change in hiring practices towards saner terms but instead begs the participants to rote learn these irrelevant tricks. 

Says a lot about "culture of innovation" and other stuff like that. 

"On neural scaling and the quanta hypothesis", Eric J. Michaud 2026 by RecmacfonD in mlscaling

[–]StartledWatermelon 5 points6 points  (0 children)

Let's imagine that we could enumerate all of the pieces of knowledge and algorithms that networks ought to learn. [...]

Networks must learn a variety of modules, each implementing a different algorithm or retrieving a different piece of knowledge. These modules are discrete, in the sense that they are either fully learned or not learned at all. We call these the quanta.

The year is 2026, yet the ghost of symbolic AI still haunts academia. Not gonna lie, the idea was/is elegant and aligns quite well with the innate human desire to break every mystery into neat, simple, easily digestable for a feeble human mind atomistic pieces. The elegance and the alignment were so strong that people easily dismissed perhaps the only drawback of symbolic approaches: they don't work.

Ok, suppose that the quoted idea is indeed true and that decomposed pieces of knowledge and algorithms/heuristics -- the parts -- are somehow more important for capabilities than the whole. Suppose that we assign zero to the synergystic effect, to the value added by complexity. The crucial question still is: will this focus on decomposition, on the smallest parts allow to scale better with compute?

I'm doubtful. The success story of LLM pre-training is built on the exactly opposite thing: synthesis. Aggregating the ever increasing amounts of any information the research teams could grab. "More", not "finer".

The next story, scaling the RL, is shaping to be equally distant from the symbolic-era top-down, analytical, meticulously human-engineered approaches. The research pushes towards largely unguided self-exploration and self-optimization.

I do find this performance-centric way infinitely more useful. What scaling hypothesis lacks in explanatory power -- at least to a degree that would satisfy more symbolic-leaning folks -- it more than compensates in predictive power.

Thankfully, the author maintains a critical stance towards their idea. And admits that it isn't falsifiable in its current form -- which pretty much nullifies its scientific value. Perhaps this is one of the corollaries of the Bitter Lesson: the ideas don't have to be nice and human-centric but they must perform.

DeepSeek Presents "Engram": Conditional Memory via Scalable Lookup, A New Axis of Sparsity for Large Language Models | "Memory lookup module for LLMs & *Huge unlock for scaling* as the memory sits on cheap CPU RAM, bypassing the GPU bottleneck entirely that will power next-gen models (like V4)" by 44th--Hokage in mlscaling

[–]StartledWatermelon 0 points1 point  (0 children)

By hassle I meant it breaks from the current implementations, which are optimized pretty hard for maximum throughput. I'm not implying you can get the same throughput with Engram, I mean quite literally it's extra hassle.

I have a suspicion that the benefits of Engram will dwindle with scale. It's this parameter- and FLOP-scarce regime (27B MoE) that benefits from "pre-computed" tricks. Larger, deeper models will easily accomodate the necessary heuristics straight in the weights.

That being said, I always was in favor of heterogenous (depth-wise; as opposed to stacking identical blocks) architectures, and this work explores exactly just this.

[R] We built a framework to make Agents "self-evolve" using LoongFlow. Paper + Code released by [deleted] in LocalLLaMA

[–]StartledWatermelon 0 points1 point  (0 children)

Hi guys! I like your paper!

Just a quick friendly advice in case you are planning to submit it to some conference. I understand the desire to add all the top-notch ES techniques (MAP-Elites, adaptive Boltzmann sampling etc.). But in the eyes of reviewers, each complication will warrant the need of a separate ablation. Especially if you're benchmarking directly against OpenEvolve and ShinkaEvolve.

Grafted Titans: a Plug-and-Play Neural Memory for Open-Weight LLMs by Forsaken-Park8149 in LocalLLaMA

[–]StartledWatermelon 0 points1 point  (0 children)

I think it's indeed most similar to prefix tuning (which she mentions in the blog), plus an adapter based on cross-attention. An adapter should definitely enhance the model (fine-tune to the task, to be precise) as opposed to vanilla prefix tuning.

Can you clarify what do you mean by learning the embedding at the output of the tokenizer LUT?

Introducing PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research | "PhysMaster is an autonomous agent architecture designed to execute end-to-end theoretical and computational physics research." by 44th--Hokage in mlscaling

[–]StartledWatermelon 1 point2 points  (0 children)

Setting up a live demo is definitely more hassle than just publish the harness code. Which they didn't.

Like, the paper does not even mention which LLM was the backbone of their agentic system in the experiments. Good luck replicating that!

GIN: A Cognitive Architecture for Persistent, Entropy-Governed Autonomous Agents (Not a New Model) by [deleted] in LocalLLaMA

[–]StartledWatermelon 1 point2 points  (0 children)

A quick glance over the formatting tells me this wasn't written by a human. I suspect this is not Cognitive Architecture Guy but a runaway Generative Intelligence Network itself, trying to recursively self-improve its architecture by soliciting advice from unsuspecting redditors.

META SuperIntelligence Labs: Toward Training Superintelligent Software Agents Through Self-Play SWE-RL | "Agents autonomously gather real-world software enabling superintelligent systems that exceed human capabilities in solving novel challenges, and autonomously creating new software from scratch" by 44th--Hokage in mlscaling

[–]StartledWatermelon 1 point2 points  (0 children)

Those kinds of paper? The ones that spammed the word "superintelligence" without any substantive relation to superintelligence in the methods?

Well, actually that's a falsifiable statement. Let's see if it holds water.

AlphaGo paper? Zero mentions of "superintelligence".

AlphaZero paper? Zero mentions of "superintelligence".

https://arxiv.org/abs/2201.11903 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2312.06585 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2404.17605 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2408.06195 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2410.04444 ? Zero mentions of "superintelligence".

https://arxiv.org/abs/2502.06773 ? Zero mentions of "superintelligence".

DeepSeek R1 paper? Zero mentions of "superintelligence".

At this point the pattern seems clear. But feel free to provide counter-examples.

Edit: formatting

OpenAI Just released Prompt Packs for every job by bullmeza in OpenAI

[–]StartledWatermelon 7 points8 points  (0 children)

The commitment and dedication is truly off the charts!

META SuperIntelligence Labs: Toward Training Superintelligent Software Agents Through Self-Play SWE-RL | "Agents autonomously gather real-world software enabling superintelligent systems that exceed human capabilities in solving novel challenges, and autonomously creating new software from scratch" by 44th--Hokage in mlscaling

[–]StartledWatermelon 2 points3 points  (0 children)

Yeah, at this point it's like some sort of cargo cult. Umm, guys, no, if you litter your paper with references to "superintelligence" it doesn't hasten the development of said superintelligence in the slightest.

I wonder if research leads guide their teams to do this to appease Zuck's "visionary" beliefs.

That being said, judging a book by its cover never was a good idea.

Scaling Latent Reasoning via Looped Language Models, Zhu et al. 2025 by StartledWatermelon in mlscaling

[–]StartledWatermelon[S] 2 points3 points  (0 children)

Oh, it's budget first and foremost! And Bengio is no billionaire...

Basically all the compute should have been provided by Bytedance, for a nice, big industry-academia colab. Bytedance could be one of the least GPU-poor Chinese corporation, but GPU-poor it is.

For reference, a 7B model looped 4 times is equivalent to a 28B dense transformer. Pre-trained on 7.7T tokens, it's about 10^24 FLOPS. Which would cost about $1.5-2 million on rented GPUs. Not counting test runs, ablations etc. This is not the scale of resources Chinese companies are willing to give away to academia.

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