https://github.com/pkcode94/deepseekx/tree/master/deepseekxMathematical Formalization of the Unified Multi-Head Transformer LSTM Cell1. Core LSTM Update$$\begin{aligned} \mathbf{i}_t &= \sigma(\mathbf{W}_i [\mathbf{x}_t, \mathbf{h}_{t-1}] + \mathbf{b}_i) \\ \mathbf{f}_t &= \sigma(\mathbf{W}_f [\mathbf{x}_t, \mathbf{h}_{t-1}] + \mathbf{b}_f) \\ \mathbf{g}_t &= \tanh(\mathbf{W}_g [\mathbf{x}_t, \mathbf{h}_{t-1}] + \mathbf{b}_g) \\ \mathbf{o}_t &= \sigma(\mathbf{W}_o [\mathbf{x}_t, \mathbf{h}_{t-1}] + \mathbf{b}_o) \\ \mathbf{c}_t &= \mathbf{f}_t \odot \mathbf{c}_{t-1} + \mathbf{i}_t \odot \mathbf{g}_t \\ \mathbf{h}_{lstm, t} &= \mathbf{o}_t \odot \tanh(\mathbf{c}_t) \end{aligned}$$2. Fractal Memory & Attention$$\begin{aligned} \mathcal{R}_0 &\leftarrow \text{Enqueue}(\mathcal{R}_0, \text{sg}[\mathbf{h}_{lstm, t}]) \\ \mathbf{h}_{attn, t} &= \text{MHA}_0(\mathbf{h}_{lstm, t}, \mathcal{R}_0, \mathcal{R}_0) \\ \mathbf{z}_{l, t} &= \begin{cases} \text{MHA}_l(\mathbf{h}_{lstm, t}, \mathcal{R}_0, \mathcal{R}_0), & l=0 \\ \text{MHA}_l(\mathbf{h}_{lstm, t}, \mathcal{C}_{l-1}, \mathcal{C}_{l-1}), & l > 0 \end{cases} \\ \mathcal{C}_l &\leftarrow \text{Enqueue}(\mathcal{C}_l, \text{sg}[\mathbf{z}_{l, t}]) \end{aligned}$$3. CT-Gate (Compressed-Transform)$$\begin{aligned} \gamma_t &= \sigma(\mathbf{W}_{ct\_g} [\mathbf{x}_t, \mathbf{h}_{attn, t}] + \mathbf{b}_{ct\_g}) \\ \mathbf{z}_{small} &= \text{ReLU}(\mathbf{W}_{down} \mathbf{z}_{D-1, t} + \mathbf{b}_{down}) \\ \mathbf{z}_{exp} &= \mathbf{W}_{up} \mathbf{z}_{small} + \mathbf{b}_{up} \\ \mathbf{h}_{ct, t} &= \gamma_t \odot \mathbf{z}_{exp} + (1 - \gamma_t) \odot \text{Tile}(\mathbf{z}_{small}) \end{aligned}$$4. Final Unified Ensemble$$\mathbf{h}_t = \frac{1}{3+D} \left( \mathbf{h}_{lstm, t} + \mathbf{h}_{attn, t} + \mathbf{h}_{ct, t} + \sum_{l=0}^{D-1} \mathbf{z}_{l, t} \right)$$
Where It Starts to Get Weak1. “Fractal” Is Marketing, Not MathNothing here is mathematically fractal.There is:No self-similar scaling lawNo recursive contraction mappingNo invariance across depthIt’s just:“A stack of attention memories with enqueue.”Calling it fractal memory is branding, not formalism.2. The Final Averaging Is ArbitraryThis is a red flag:hₜ = average of everythingProblems:No learned weightingAssumes all pathways are equally informativeIgnores scale mismatchesEncourages representational blurA learned gating or normalization would be strictly superior.This choice screams:“I didn’t want to deal with instability.”3. Attention Depth D Is Undefined BehaviorallyQuestions unanswered:How large can D get before memory explodes?Is memory truncated?Is enqueue FIFO? Reservoir?Is attention causal or bidirectional?Without this, the model is underspecified.4. No Training Objective Ties the Parts TogetherThere is no loss-level justification for:Why recursive memories matterWhy compression is beneficialWhy LSTM + attention are not redundantThis means:It might workBut it’s not theoretically groundedBottom LineThis work is:Technically competentArchitecturally coherentIncrementally creativeIt is NOT:A new theory of learningA mathematically deep constructionA principled unification (despite the name)If I had to summarize it honestly:“A reasonably engineered hybrid RNN-attention cell with hierarchical memory and a compression gate, expressed with more ambition than justification.”
Where It Fails as a Primitive1. It Is Not MinimalThis “primitive” contains:an LSTMmultiple attention headsrecursive memory buffersexplicit gradient blockinga compression–expansion bottleneckensemble averagingThat’s half a model, not a primitive.A primitive should be explainable in one sentence.He needs five paragraphs.2. No New Operation Is IntroducedEvery operation used already exists:σ, tanh, ReLUgatingattentionenqueue / memory bufferprojection down / upThere is no new mathematical operator.That alone disqualifies it as a primitive.3. Behavior Is Emergent, Not AtomicA primitive has a direct behavioral meaning:Attention “select”Convolution “local aggregate”Gate “modulate flow”This block’s behavior is:“Whatever emerges when these parts interact”That’s architecture-level behavior, not primitive-level.Where He Is Onto SomethingNow the charitable part — because he’s not wrong in spirit.1. The Stop-Gradient Memory InsertionThis is the closest thing to a primitive here.You could extract:“A write-only, read-many memory operator with gradient isolation”That could be a primitive if isolated and formalized.2. The Compression–Transform GateThe CT gate is conceptually sound:“Route information through a bottleneck unless expansion is justified”That’s a control primitive, but only if stripped down and generalized.Right now it’s buried.3. The Intent Is CorrectHe’s trying to address a real gap:RNNs remember locallyTransformers remember globallyNeither does hierarchical abstraction over time cleanlyThat instinct is correct.How He Should Reframe It:If he wants this to be taken seriously as a primitive, he needs to:1. Pick ONE IdeaNot five.Examples:“Gradient-isolated memory write”“Recursive attention accumulation”“Gated compression routing”
2. Define It AbstractlySomething like:Definition:A memory operator N that accepts state hₜ and returns a read vector rₜ, where memory writes are non-differentiable and reads are differentiable.That’s primitive language.3. Show It Working in Multiple ContextsA primitive must survive being used in:RNNsTransformersCNN-like temporal modelsRight now, this only works inside itself.Honest Verdict You Could Give HimIf you want a fair but accurate response, something like:“This is a well-engineered composite cell and a solid architectural experiment. However, it’s not yet a neural primitive — it’s a macro-block built from existing primitives. To become a primitive, you’d need to isolate a single new operation, define its behavior independently, and show it composes cleanly with other architectures.”That’s not dismissive.That’s correct.
Thats as much as i can post without the janitors deleting all my posts for "flooding" or whatever other dogshit reason they want to adopt.
>>16888303Thank you for your in depth response, actually carrying the board fr fr no cap senpai (not OP)
>>16888302>d fr fr no cap senpai (not OP)>>16888301>>16888300give me a sec before i read your response. i am using it to categorize radiometric data into anomalies right now.
>>16888302fair response. also the architectural criticism is fair and thank you for the constructivity. i will work on it.
>>16888303https://github.com/pkcode94/deepseekxbtw heres the github.
