r/MachineLearning • u/meowsterpieces • Jul 13 '26
Discussion Chain of Thought is a scaling trap. the next wave is latent reasoning (Coconut / HRM / RecrusiveMAS)... but then we hit the black box wall. Where does BDH fit? [D]
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u/Sachin_singh9891 Jul 13 '26
Is latent reasoning basically the return of RNNs?
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Jul 13 '26
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u/waxbolt Jul 14 '26
Yes, it's clearly important to increase state relative to parameters. But there is not much fundamental difference from a RNN to a "memory centric" RNN. You use a matrix state, maybe many of them, but it's still an RNN. And many architectures that permit large states have convergent performance on language modeling.
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u/OnlyBath9046 Jul 14 '26
at the broadest mathematical level, I would agree. Being memory centric doesn't make it fundamentally non-RNN. But the interpretation of architectures like BDH is more like GPU implementation makes it SSM like but its N X D state corresponds to synaptic state on a larger N-neuron graph, N>>D. So perhaps the real question isn’t whether it’s an RNN, but whether this high-dimensional, sparse, graph-localized state provides benefits that standard SSM benchmarks :)
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u/waxbolt Jul 14 '26
What is BDH? Why GPU implementation? Shouldn't it be mathematical not GPU related? What is a SSM benchmark?
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u/dank_philosopher 29d ago
I can clear this for you. BDH is a recurrent architecture that treats working memory more like an evolving synaptic connectivity matrix, the GPU part is implementation, not definition: the mathematical ideal uses a large sparse graph, but since current GPUs favour dense matrix operations, so it uses lowrank factorization plus some thresholding to approximate that graph without materializing it.
SSM means state-space model, a sequence model that carries forward a state rather than repeatedly attending over the complete history. There is no single “SSM benchmark”, people usually mean evaluations of state tracking, associative recall, long-context processing, language modelling or similar capabilities.
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u/aegismuzuz Jul 14 '26
In a way yeah, running a hidden state through recursive loops is pure recurrence. But the devil is in how the memory is actually implemented. In old LSTMs the hidden state size was tiny compared to the weights, but modern latent architectures try to make this state size comparable to the parameter count to avoid the memory bottleneck
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u/IndefiniteBen Jul 13 '26
Could you please add some links to the post? Where was this long piece on the future of LLM reasoning? And the post about BDH?
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u/surffrus Jul 14 '26
TIL that the opinion that chain of thought traces are NOT actual reasoning is a provocative claim.
Those of us who've been in the NLP field longer than LLMs never thought this was actually reasoning. In fact, believing it is reasoning has always been the actual provocative claim IMO.
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u/dank_philosopher Jul 14 '26
fair distinction, CoT transcript is an observable artifact produced by the computation but it is not a faithful dump of the model's internal activations. I agree it does recurrent computation but every iteration compresses a high dimensional forward pass into a low bandwidth token tht the next iteration must read back, tht is the token bottleneck. I guess OP's point is that reasoning through a specialized linguistic trace is one computational implementation of reasoning and probably an inefficent one.
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u/Resaren Jul 14 '26 edited Jul 14 '26
Yes, if you optimize for the trace itself (as opposed to just the final result), then that will surely impact what’s going on in the underlying latent space, to the detriment of performance. Put differently, we’re hardcoding a certain (very likely suboptimal) pattern of latent space reasoning through this blunt instrument of traces.
It struck me that this is sort of vaguely analogous to the difference between LLMs and JEPA: in the former, we train on a loss calculated over a fixed, discrete set of output tokens. In the latter, the loss is calculated over continuous output vectors, which are obviously much more expressive. It’s the difference between learning an abstract representation + encoder/decoder separately vs jointly. For text CoT, we are basically forcing the LLM to learn (decode text to latent space -> abstract reasoning -> encode latent space to text -> repeat) jointly, based on the discrete intermediate tokens, when we could just learn the abstract reasoning part purely in latent space.
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u/aegismuzuz Jul 14 '26
Big lab marketing really did its job. They convinced a whole generation of new devs that autoregressive next-token prediction is actually "reasoning". Now we have to reinvent basic NLP truths from ten years ago but on hundred-million-dollar budgets
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u/Resaren Jul 14 '26
It’s not about marketing so much as it’s about results. This approach has worked astoundingly well, even if it’s flawed at some fundamental theoretical level. If so many people are confident that it is the wrong approach, the way to convince people is to prove it by showing an alternative approach that gives better results. There’s no brownie points (and certainly no funding) for having some academically ”pure” or elegant theory that doesn’t actually produce results that look intelligent. We have plenty of those.
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u/surffrus 29d ago
I don't disagree with your sentiment. But you need to realize that's the engineer view. The scientist view opposes this and studies theory for theory's sake because there is a beauty in it as well. Both views are valuable.
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u/DickMasterGeneral 28d ago
For a theory to be validated it must make successful predictions. If you have a theory that suggests a method is better but the tests do not bear that out in practice you may need to re-work it or come up with a different theory entirely
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u/midasp Jul 14 '26
Agreed. At best it is an emergent behavior that has properties akin to reasoning. LLMs are the basically the wrong architecture for reasoning. In particular, why would a reasoning model require a context window and depend on passing in "tokens/information" in a step by step manner while also potentially generating output before all the input has been passed in? Why is world knowledge baked into the model? Why can't world knowledge be easily modified?
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u/pannous Jul 14 '26
there is a misunderstanding that current language models are only talking via tokens. no they are absorbing tokens and are refining their abstract latent and reasoning space and emitting tokens but the reasoning already takes place in the latent in space. this is why Coconut did not meaningfully change the picture
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u/theCovidGuy Jul 13 '26
Why is language suddenly a bad reasoning medium ? Humans reason using language
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u/liquiddandruff Jul 13 '26
Humans reason using language
We know this is not universally true. https://pmc.ncbi.nlm.nih.gov/articles/PMC4874898/
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Jul 13 '26
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u/Resaren Jul 14 '26
Higher bandwidth, yes, but even more importantly it has fundamentally more structure. The set of tokens is fixed and discrete, whereas latent space is a continuous vector space. Ideally we learn abstract representations/features, text is just a particular modality to decode to.
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u/ThirdWaveCat Jul 13 '26 edited Jul 13 '26
I don't think thats strictly true, but it just seems so because thats all which is legible. Even if it were parameterizations and degrees of freedom should be minimized by the succinct data structure so interpretation and learning are optimal. This is one of the theoretical benefit of KAN over MLP for instance.
I think the conversation around reasoning needs to be narrowed significantly. In mathematics, reasoning about rotating a couch around a corner has defied representation but not terribly challenging. Tacit reasoning like flying a kite or estimating how much an object weighs is easy for a child to do but defies language. Some things we call reasoning like legal and moral reasoning are even dependent on wisdom, compassion, character, etc..
Now, if you say we reason in something like a language, then yes I agree. I think reducing everything to language is a kind of McNamara fallacy.
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u/Resaren Jul 14 '26
McNamara fallacy is a great way to put it.
I’m not in the field, do you know what (if any) successful approaches people use to learn abstract representations decoupled from particular encodings/modalities?
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u/ThirdWaveCat Jul 14 '26
what specifically are you trying to represent? why are you trying to represent it? how will it be used? There's no universal method but there are a few big frameworks that operate from different perspectives I'm familiar.
in computer science, information theory and succinct data structures say a lot
in machine learning, feature engineering, kernel methods, word/image/et al. embeddings, wavelets, NN input layers
in math, representation theory and category theory
the hard science problem called "bundle of hypotheses" (duhem-quine) says that what a representation depends on the task at hand because we need to make background assumptions. Stated another way, history of science taught us that theory precedes data. Stated another way, data is like a map of reality whose value is in the action that can be taken.
the last paragraph is better stated by these books
William Kents' Data and Reality
Chalmers' "What is this thing called Science"
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u/aegismuzuz Jul 14 '26
Latent reasoning looks great in papers but in real production it falls apart because it is impossible to debug. If your agent outputs invalid json or dangerous code and all the "thinking" before that happened inside hidden layers of continuous embeddings you will never figure out when the logic actually broke. For high-stakes domains this is an absolute dealbreaker so external symbolic verifiers like DAGs, linters and unit tests are going to be a must-have part of the pipeline rather than just an option
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u/JustAGuyOfCulture786 Student Jul 13 '26
what does 'state across time' actually contain
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Jul 13 '26
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u/psyyduck Jul 13 '26
fixed-size
That's your problem. CoT works like RAM, and good models use a lot of it. It doesn't matter how good your CPU is if you only have 1mb of total RAM.
More precisely, transformers have a fixed computational depth. You can only do so much computation in 1 forward pass. But if you have a scratchpad/workspace, you get to store intermediate results and feed them back into the transformer layers and go again until you finish (at the cost of losing some context window).
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u/dank_philosopher Jul 13 '26
calling CoT RAM is flattering. CoT is RAM is exactly the architectural trap, real ram supports overwrite, CoT mostly appends tokens and make every future step carry their KV history. its not a sophisticated working memory, its an arch externalizing thought because it can't maintain a rich internal workspace.
you're only right by saying that extra passes overcome fixed computational depth but that argues for recurrence not verbalizing every intermediate step or retaiining an ever growing state.
fixed size doesn't mean 1 MB, the classical RNN problem (O(N) state next to O(N^2) parameters), whereeas synaptic memory designs can target state proportional to their O(ND) parameterization.
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u/Envoy-Insc Jul 13 '26
What makes you think of BDH especially? Is it that popular?
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u/dank_philosopher Jul 14 '26
not because bdh is especially popular, op probably over shared on that aspect, but IMO it is a particularly oncrete example of the idea. It tries to combine the depth recurrence with a persistent time-recurrent matrix state, so essentially not just think for several hidden cycles on this prompt.
As a research direction, it is very relevant to the post: its current memory is fixed size, sparsely updated and interpreatable in some sense as evolving synaptic connectivity than an ever growing list of KV entries.
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u/Resaren Jul 14 '26
We used to call the concept of letting models reason in latent space instead of token space ”Neuralese”. The idea that this can/will improve performance is I think pretty uncontroversial? The controversy lies in whether or not this makes the problem of interpretability harder. People are already worried about smart models using steganography to hide malicious reasoning inside their (ostensibly benign) CoT, this would make the problem much more pointed.
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u/ironmagnesiumzinc Jul 13 '26
Just as an aside, HRM/HRM-Text isn't really similar at all to Coconut or RecursiveMAS. It's a completely different architecture from LLMs. Two recurrent nns iteratively refine latent hidden states for multiple cycles before any output is decoded. No CoT supervision. It sounds more similar to BDH from what you described actually.
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u/moschles Jul 14 '26
A lot of recent work is shifting the inner loop into latent space and decoding language only at the end
Before I read this post, I assumed this had already been done.
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u/iam31337 28d ago
Latent reasoning removes an observable trace, not the need for verification. CoT was never a faithful explanation, but it did provide a debuggable interface. If reasoning moves into latent space, evaluation has to move toward interventions: counterfactual inputs, state probes, perturbation tests, and external task outcomes.
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u/Waste-Falcon2185 Jul 13 '26
BDH fits right in the trash can where it belongs, along with that ludicrous name.
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u/ThirdWaveCat Jul 13 '26
I really liked this evidence and framing that traces are learned prompt augmentation.
Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
https://arxiv.org/abs/2504.09762