Study design for comparisons across model instances [ftip-00LS]
✍️source

This subsection specifies how a conditional architecture comparison may be instantiated without changing the architecture-neutral formulation. It is a study design, not a universal ranking of architectures.

Definition 1. Matched model-instance record [ftip-00LT]

A model-instance record names the architecture, parameterization or checkpoint, training and post-training recipe, optimizer, execution stack, inference budget, task family, evaluation law, and recorded cost vector. Two records are comparable only after the fields held fixed and the fields allowed to vary are declared.

Definition 2. Pair identity and comparison unit [ftip-00LU]

A comparison unit is a pair of model-instance records evaluated on the same task family and evaluation law, together with a declared cost scalarization. A Transformer reference and a Kimi Delta Attention instance may form such a pair only when the remaining coordinates are documented.

Remark 3. Paired responses to a common recipe [ftip-00LV]

For a pair of matched model-instance records, the common-recipe arm measures how the permitted architecture packages respond to one recipe. A score advantage in this comparison need not persist after either package is retuned.

Remark 4. Different recipes under equal tuning effort [ftip-00LW]

Under the equal-tuning-budget arm, the two selected model instances may use different recipes. That difference is compatible with equal predeclared tuning effort; forcing the selected recipes to match would answer a different comparison question.

Remark 5. How an allowed class changes its envelope [ftip-00LX]

In the restricted-envelope arm, enlarging an allowed intervention class at fixed evaluation law, cost accounting, and budget can raise its envelope and cannot lower it. A finite search over feasible interventions supplies a lower bound from the scores it attains. It certifies the supremum only if it covers every feasible intervention or is accompanied by a matching upper-bound argument.

Remark 6. Coordinate-change rule [ftip-00LY]

Changing the optimizer, post-training method, hardware or kernel, precision, context policy, or evaluation law changes a comparison coordinate. A score difference after such a change belongs to the new intervention, unless the study explicitly estimates the interaction.

Definition 7. Architecture-family transfer matrix [ftip-00LZ]

A transfer matrix records which interfaces, intervention arms, and budgets permit a comparison. Each row is a study record: its source, estimand, matched controls, evaluation coordinates, and declared scope boundary are explicit. A blank or incompatible cell is an incomparability finding, not a missing score to be imputed. The vector and evaluation coordinates are reported together under the conditional frontier of Definition [ftip-00JM].

Comparison cell	Source	Estimand	Matched controls	Cost / stability / monitorability	Status and scope boundary
Transformer--KDA/full attention	Remark [ftip-00KF], Definition 2, Example [ftip-00LQ]	Difference in utility, cost, and $K_{\rm state}$ between KDA and full attention	Same task, model scale, recipe, context, seeds, and evaluation law	Report measured cost vector, training/inference stability, and state/failure observability	Open; a finite KDA--Transformer result does not establish an architecture-wide ceiling
Transformer--Mamba	Mamba, Sections 3.1--3.3; § [ftip-00OQ]	Difference in retrieval utility and $K_{\rm state}$ at the declared budget	Task, scale, recipe, context, and inference budget	Report cost vector, recurrence stability, and state/failure observability	Open; selective compression does not transfer a retrieval result across tasks
Transformer--linear attention	Linear attention, Sections 3.2--3.3; § [ftip-00OQ]	Difference in retrieval utility and $K_{\rm state}$ for associative summaries	Task family, feature map, context budget, and evaluation law	Report prefix cost, numerical stability, and summary-state observability	Open; associative summaries do not imply full-attention equivalence
Transformer--CTM or looped model	Continuous Thought Machine, Sections 3.1--3.5; § [ftip-00KG]; § [ftip-00OQ]	Difference in utility and $K_{\rm adapt}$ per charged tick or loop	Input, base model, stopping rule, recipe, and charged internal compute	Report tick/loop cost, training and inference stability, and stopping-signal monitorability	Open; extra passes are charged and do not imply continuous-time behavior
Transformer--BDH	BDH, Section 3.2; § [ftip-00OQ]	Difference in task utility and $K_{\rm state}$ under the reduced state interface	Task, state parameterization, credit horizon, systems stack, and cost	Report memory and compute cost, optimization stability, and state observability	Open; a reduced state-space proposal is not a production transfer result
Transformer-Recurrent Looped Transformer	§ [ftip-00OQ]; [zhang2026rlt, Sections 2-3 and 5-8]	Paired change in finite-task utility and $K_{\rm state}$ under recurrent decoder feedback	Same task, model family, parameter count, training tokens, seeds, and feedback interface; causal no-feedback and chunking arms	Report encoder/decoder FLOPs, recurrent state, chunk latency, memory, stability, and state monitorability	Open and finite-only until a declared deployment arm and transfer record exist
Residual baseline: Deep Delta rewrite	§ [ftip-00OQ]; [zhang2026deepdelta, Sections 2-4 and 6]	Paired change in utility and rewrite-sensitive state behavior after the rank-one read/compare/target/write intervention	Matched residual capacity, optimizer, tokens, seeds, and model family; no-write, write-only, and capacity controls	Report inference FLOPs, memory, residual/cache state, numerical stability, and rewrite monitorability	Open; finite GPT-2 evidence does not establish a general capability or frontier claim
Core architecture--JEPA adjunct	I-JEPA, Section 3; § [ftip-00OQ]	Conditional change in $K_{\rm repr}$ and $K_{\rm plast}$ after the objective intervention	Same base artifact, target task, predictor budget, data, and optimizer arm	Report intervention cost, training stability, and representation/probe monitorability	Conditional; an objective intervention is not a replacement architecture
I-JEPA--SplitJEPA	SplitJEPA, Sections 3.1--3.4; § [ftip-00OQ]	Subspace identifiability and conditional change in $K_{\rm repr}$	Matched invariant correspondences, variant-rank premise, normalization, and encoder budget	Report probe cost, numerical and optimization stability, and subspace monitorability	Conditional; identifiability does not become a general I-JEPA guarantee
Core architecture--liquid dynamics	Liquid time-constant networks, Sections 1--2; § [ftip-00OQ]	Difference in utility and $K_{\rm adapt}$ under the solver intervention	Input law, solver, step budget, tolerance, task, and cost vector	Report solver cost, numerical stability, and trajectory/failure monitorability	Open; continuous dynamics and solver choices remain intervention coordinates
Core architecture--Neural Operator	Neural Operators, Section 3; § [ftip-00OQ]	Difference in utility and $K_{\rm repr}$ across discretizations	Function-space task, discretization family, operator layer, and evaluation law	Report training/inference cost, discretization stability, and field-observation monitorability	Open; discretization invariance under one source formulation is not task transfer

A completed row must report its paired observations through § [ftip-00L4] and the common-recipe, equal-tuning, and restricted-envelope arms of § [ftip-00JU]. Until those records are supplied, its status remains open or incompatible.

Remark 8. Applying a finite model to an architecture [ftip-00M0]

A toy representation or recurrence result may motivate a hypothesis, but it is not transferred to a model instance until its interface, task, cost accounting, numerical regime, and evaluation protocol are instantiated and checked. If any required assumption is unverified, applicability to the model instance remains unestablished.

Definition 9. Negative-result and stopping report [ftip-00M1]

A completed comparison reports the tested cost grid, excluded configurations, stopping rule, uncertainty, and negative results. Stopping without a detected difference is evidence about the declared study, not proof that the architectures have equal potential.

Remark 10. Open comparison questions [ftip-00M2]

The matrix in Definition 7 is a protocol map, not a result table. The following probes keep the five capability coordinates separate and leave an unknown cell visible until the required records exist. Each probe names its source, estimand, controls, and evaluation coordinates so that a missing record cannot be mistaken for a zero score.

State access. Sources: Mamba, Sections 3.1--3.3 and linear attention, Sections 3.2--3.3. Estimand: the paired difference in retrieval utility and $K_{\rm state}$. Controls: Transformer, Mamba, and linear-attention systems on a predeclared retrieval and continuation suite at matched parameter, context, recipe, and cost budgets. Evaluation: report cost, recurrence or summary stability, and state observability. Scope boundary: a long-context score does not transfer to another task family without its task and transport record.
Recurrent feedback. Source: [zhang2026rlt, Sections 2-3 and 5-8]. Estimand: the paired change in finite-task utility and $K_{\rm state}$ from decoder feedback. Controls: a common causal encoder, model size, training tokens, seeds, and matched per-token budget, with no-feedback and declared chunk-size arms. Evaluation: report recurrent-state bytes, encoder/decoder FLOPs, chunk latency, numerical/training stability, and feedback monitorability. Scope boundary: parity, permutation tracking, and modular arithmetic remain finite evidence; without a deployment and transfer record, the cell is finite-only.
Residual rewrite. Source: [zhang2026deepdelta, Sections 2-4 and 6]. Estimand: the paired change in utility and rewrite-sensitive state behavior from a gated rank-one read/compare/target/write edit. Controls: residual capacity, optimizer, tokens, seeds, and model family, with no-write, write-only, and capacity-matched arms. Evaluation: report rewrite and residual-state monitorability, inference FLOPs, memory, numerical stability, and a held-out transfer task. Scope boundary: finite GPT-2 observations do not identify a general capability gain or architecture-wide law.
Adaptive computation. Source: Continuous Thought Machine, Sections 3.1--3.5 and the looped-model contract in § [ftip-00KG]. Estimand: the paired change in utility and $K_{\rm adapt}$ per charged internal tick or loop. Controls: common input, base model, recipe, stopping rule, and charged internal compute. Evaluation: report tick/loop cost, training and inference stability, and stopping-signal monitorability. Scope boundary: an observed adaptive gain remains conditional on that stopping law.
Representation and abstraction. Sources: I-JEPA, Section 3 and SplitJEPA, Sections 3.1--3.4. Estimand: subspace identifiability and the conditional change in $K_{\rm repr}$. Controls: fixed correspondences, full-rank variant differences, normalization, encoder budget, and matched downstream probes. Evaluation: report probe cost, optimization stability, and subspace monitorability. Scope boundary: the conditional subspace result is separate from downstream task transfer; failure of a premise is an incompatible cell, not a zero score.
Plasticity. Source: the objective and optimizer interfaces in Definition [ftip-00JM] and the I-JEPA intervention description in § [ftip-00OQ]. Estimand: the paired change in $K_{\rm plast}$ after a declared post-training intervention. Controls: common recipe and equal-tuning arms with data, optimizer, trial count, stopping, and feedback fixed or explicitly varied. Evaluation: report intervention cost, training stability, and update-signal monitorability. Scope boundary: an intervention effect is limited to the declared base artifact, task, and objective; it does not transfer across architectures without a new paired record.
Portability. Sources: the Mamba, BDH, and Neural Operator method descriptions in § [ftip-00OQ]: Mamba, BDH, and Neural Operators. Estimand: survival of the declared utility and $K_{\rm port}$ across one transfer edge. Controls: source model, task family, model scale, deployment regime, evaluation law, and cost vector retained as paired fields. Evaluation: report migration cost, stability after the shift, and failure monitorability. Scope boundary: evidence for one transfer edge does not order unmeasured edges; blank edges remain unmeasured.
Adjunct interactions. Sources: liquid time-constant networks, Sections 1--2, Neural Operators, Section 3, and I-JEPA, Section 3. Estimand: the joint change in the declared capability coordinates after an explicit intervention. Controls: declared core model, objective, temporal law, function-space bias, matched controls, and separate single-intervention arms. Evaluation: report joint and marginal costs, numerical/training stability, and interaction monitorability. Scope boundary: a joint effect is limited to the declared core, task, temporal law, and function-space regime; it does not transfer across regimes without separate intervention records.

Semantic representation diagnostics along pretraining checkpoints [ftip-00OZ]

This bounded diagnostic records when a declared semantic representation becomes recoverable along a pretraining trajectory. It uses the spatial and temporal probing design of [gurnee2024space, Sections 2-4 and 7] as a measurement template. The result is a representation diagnostic, not a claim about a developmental mechanism, a deployment capability, or a post-training law.

The task family is fixed before inspecting checkpoints: the six coordinate task datasets in the source family (world, United States, and New York City locations; historical figures, cultural works, and news headlines). Each score uses held-out entities. An eligible model family supplies at least three ordered pretraining checkpoints, identical train, validation, and test entity splits and filtering at every checkpoint, and the same layer-wise linear ridge-probe protocol, including a predeclared ridge-regularization grid and validation selection rule. A family or checkpoint missing one of those fields is reported as missing rather than silently pooled.

For model family $m$, checkpoint $c$, task $q$, and layer $\ell $, let $s^{\mathrm {val}}_{m,c,q,\ell }$ and $s^{\mathrm {test}}_{m,c,q,\ell }$ be validation and held-out-test coefficients of determination for the fixed probe. Choose the layer on validation entities, then evaluate once on the test entities:

\[ \ell ^*_{m,c,q}=\arg \max _{\ell \in L_m}s^{\mathrm {val}}_{m,c,q,\ell },\qquad s_{m,c,q}=s^{\mathrm {test}}_{m,c,q,\ell ^*_{m,c,q}}. \]

A predeclared layer may replace the validation choice, but a test score is never used to select $\ell ^*$; this keeps the emergence score from being a layer-selection envelope.

The primary absolute emergence threshold is $\tau =0.50$ in this held-out score, chosen before the trajectory is inspected. The first crossing is

\[ e_{m,q}(\tau )=\min \{c:s_{m,c,q}\geq \tau \}, \]

with $e_{m,q}(\tau )=\bot $ when no checkpoint crosses. A crossing is reported with the checkpoint token count, layer, a bootstrap uncertainty interval over held-out entities, and the probe seed; it is not smoothed into a claim about an unobserved training interval. Sensitivity must include $\tau \in \{0.25,0.50,0.75\}$, model family, parameter scale, and checkpoint spacing.

The structural record contains held-out coordinate prediction, block and cross-entity generalization, prompt variants, and the layer at which the score peaks. These are content and representation probes. A function or deployment record is separate: it must specify an intervention or user task, an outcome metric, matched controls, and its own cost and failure accounting. A probe crossing alone cannot populate that record.

The source reports linear spatial and temporal representations across Llama-2 and Pythia scales, prompt sensitivity, cross-entity robustness, and individual neurons; see [gurnee2024space, Sections 2.1-2.3, 3.1-3.3, 4.1-4.2, and 5]. Those observations motivate the fields above, but every trajectory association reported by this protocol is noncausal. In particular, it makes no claim about which training environment caused the score, whether a representation is used by a deployed function, or what post-training will preserve.

Each trajectory record therefore retains the model family and size, checkpoint token count, task and entity split, probe class and seed, all layer scores, threshold grid, missingness, and the separately reported function or deployment assay. Comparisons are within the declared task family and model family before any cross-family summary. A change in checkpoint spacing or probe protocol is a new arm, not evidence of an earlier or later emergence.

Convention. Identifiability regimes for semantic representation diagnostics [ftip-00P1]

Zhou and Klindt study labels-to-latents and latents-to-labels data-generating directions. Their general assumptions include a fixed continuous invertible map $g:Z\to X$, a continuous learned encoder with $h=f\mathbin \circ g$, a row-centered log-linear posterior $p(y|z)={\rm softmax}(W^*z)$, effective dimension $n'={\rm rank}(W^*)$, and a global population cross-entropy optimum with linear readout $U$. The source boundary for this note is the DGP construction and assumptions in Sections 3.1 to 3.2, Theorems 4.1 to 4.4, Sections 5.1 to 5.4, and Section 6, with Supplementary Assumptions 8.1 to 8.3, of [zhou2026complete, Theorems 4.1 to 4.4, Sections 3 to 5, and Section 6].

Source observation. When $m\geq n'$, Theorem 4.1 makes the classification-relevant part of every optimum linear, $h(z)=Az+\nu (z)$ with $UA=W^*$; the null-readout term satisfies $\nu (z)\in N(U)$ and may remain unconstrained. When $m<n'$, Theorem 4.2 gives generic nonlinear, non-identifiable optima when $\operatorname {supp}(p_Z)$ has nonempty interior $\Omega $, $p_Z$ is continuous and strictly positive on $\Omega $, and every candidate $A$ has $N(A)\cap \Omega \ne \varnothing $. These are different capacity regimes, not two scores for one representation.

The stronger conclusions require additional structure. In the generative case, Assumption 8.2 takes $Z=\mathbb R_+^n$ and adds a factorized exponential conditional, constant conditional entropy, a uniform class prior, exact capacity $m=n=n'$, non-negative activations, fixed within-class variance, and an energy-minimizing global optimum. Theorem 4.3 then gives the scaled permutation $h(z)=DP_\sigma z$. In the discriminative case, Assumption 8.3 uses $A^*\in \mathbb R^{n\prime \times n}$ with full row rank and $U^*\in \mathbb R^{|Y|\times n\prime }$, adds $W^*=U^*A^*$, $k$-sparse latents, and $\operatorname {spark}(A^*)>2k$. With $m\geq n\prime $, let $P_U$ be the orthogonal projector onto $\operatorname {Row}(U)$; the classifier-visible encoder $P_Uh(z)=Az$ has $N(A)=N(A^*)$, so the unique $k$-sparse solution can recover $z$. Axis alignment and sparse recovery are distinct routes through distinct assumptions.

Section 5 reports finite experiments rather than a universal law. Linear alignment uses held-out coefficient of determination for a linear decoder; axis alignment uses the mean correlation coefficient (MCC) after Hungarian matching recovered axes to true latents. Sparse recovery compares supervised Lasso, dense OLS, and unsupervised sparse dictionary learning. Cross-system correspondence uses the Maximum Matching Unit (MMU): for each source unit, the maximum absolute Pearson correlation with any unit in a declared comparison model and layer on a declared stimulus set. The synthetic experiments use ten random seeds; the real-data cross-system analysis does not inherit that seed count. Section 6 records limits from constant conditional entropy, non-negativity, class priors, and the assumed sparse data-generating process; signed latents do not inherit Theorem 4.3 directly.

Finite identifiability record. For a fixed model family, checkpoint, task split, and probe seed, record

\[ \mathcal D=\bigl (r,m,n,n',\mathcal A,[h]_{\sim _r},R^2_{\rm lin}, R^2_{\rm sparse},\operatorname {MCC}_{\rm axis}, \operatorname {MCC}_{\rm sparse},\operatorname {MMU}_{\rm cross},\mathsf E\bigr ). \]

Here $r$ is one declared regime: sufficient-capacity linear, undercomplete nonlinear, generative axis-aligned, or discriminative sparse recovery. $\mathcal A$ lists the causal direction, DGP, capacity, sparsity, and assumptions actually held. $R^2_{\rm lin}$ is the held-out coefficient for linear latent alignment; $R^2_{\rm sparse}$ names the decoder and target, with supervised Lasso and dense OLS kept as separate arms. $\operatorname {MCC}_{\rm axis}$ records the matching and correlation convention for axis recovery, while $\operatorname {MCC}_{\rm sparse}$ records the matching convention, sparsity level, and whether the decoder is supervised or an unsupervised dictionary. $\operatorname {MMU}_{\rm cross}$ records the directed source and comparison model, layers, unit sets, stimulus set, and correlation rule. An unavailable or inapplicable field is $\bot $.

The equivalence class identifies only the ambiguity allowed by the declared regime: $(A,\nu )$ with $UA=W^*$, $\operatorname {rank}(A)=n'$, and $\nu (z)\in N(U)$ in the linear regime; no universal quotient, recorded as non-identifiable under the stated assumptions, in the undercomplete regime; positive diagonal scaling and permutation $(D,P_\sigma )$ in the axis-aligned regime; and the classifier-visible null-space class $N(A)=N(A^*)$ in the sparse regime. A sparse decoder has permutation-and-scaling ambiguity only when that convention is declared and measured. No scalar summary is defined, and none of these fields alone establishes monosemanticity.

Source-grounded finite instance. In the compressed-sensing arm, the paper fixes $n=16$, $n'=m=8$, $|Y|=64$, draws a Gaussian measurement matrix with normalized columns, and tests $k\in \{1,2,3\}$ over ten seeds. For the $k=1$ arm, the record declares discriminative sparse recovery and the $W^*=U^*A^*$ and spark conditions; it reports $R^2_{\rm sparse}\approx 1$ for supervised Lasso, $R^2_{\rm sparse}\approx n'/n=0.5$ for OLS, and $\operatorname {MCC}_{\rm sparse}\approx 0.86$ for unsupervised Lasso, with $\operatorname {MCC}_{\rm axis}=\bot $ and $\operatorname {MMU}_{\rm cross}=\bot $. The ten-seed estimate, decoder arm, and missing fields are part of $\mathsf E$; the values are finite synthetic observations, not a deployment or capability claim.

FTIP deduction. This record makes the representation question conditional on capacity, causal direction, and an explicit equivalence class. It lets the existing checkpoint route report linear recoverability, axis alignment, sparse recovery, and cross-system matching without treating any one readout as a capability or deployment result. A finite observation can support a regime-specific comparison; it cannot identify a developmental mechanism or transfer beyond the declared task and assumptions.

Proposed protocol. Before inspecting a checkpoint, freeze the task family, latent or proxy labels, split, width ratio, causal regime, assumptions, decoder class, comparison model and layer for MMU, stimulus set, and held-out evaluator. Select layers or decoder settings on validation data only. Report the full record, seed repetitions, missingness, and assumption-violation arms; keep each $R^2$, MCC, and MMU in its native units. For real model activations, treat latent recovery as a declared proxy assay and report the proxy construction with the same boundary.

A scoped local formalization search of Forest, formal-math, TauCetiReview, and TauCetiWorker found no declaration that packages this four-regime record with its separately typed readouts. Existing representation and evaluation declarations cover neighboring fields only, so this finite identifiability record remains a protocol proposal.

This protocol links the conceptual-discovery question in § [ftip-00MH] to the capability coordinates in Definition [ftip-00JM]; the panel of open comparison questions in Remark 10 transcludes this child.

These probes do not establish a common ordering of architecture families. They specify what would count as evidence for a declared bottleneck change, while cost, numerical stability, monitorability, and transfer remain separate evaluation coordinates. Paired observations should use § [ftip-00L4], the study arms of § [ftip-00LS], and the capability vector of Definition [ftip-00JM]. Recurrent feedback and residual rewrite remain separate arms in every record; a finite result without a deployment arm stays finite-only.

References

Reference. Complete or Sparse: A Tale of Two Identifiabilities [zhou2026complete]

2026
Junze Zhou, David Klindt
https://arxiv.org/abs/2610.20851

@misc{zhou2026complete,
  title = {Complete or Sparse: A Tale of Two Identifiabilities},
  author = {Zhou, Junze and Klindt, David},
  year = {2026},
  eprint = {2610.20851},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.20851}
}

Reference. Deep Delta Learning [zhang2026deepdelta]

2026
Yifan Zhang, Yifeng Liu, Mengdi Wang, Quanquan Gu
https://arxiv.org/abs/2601.00417

@misc{zhang2026deepdelta,
  title = {Deep Delta Learning},
  author = {Zhang, Yifan and Liu, Yifeng and Wang, Mengdi and Gu, Quanquan},
  year = {2026},
  eprint = {2601.00417},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2601.00417}
}

Reference. Recurrent Looped Transformer [zhang2026rlt]

2026
Yifan Zhang, Jichen Feng, Shihan Qin
https://arxiv.org/abs/2610.07591

@misc{zhang2026rlt,
  title = {Recurrent Looped Transformer},
  author = {Zhang, Yifan and Feng, Jichen and Qin, Shihan},
  year = {2026},
  eprint = {2610.07591},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.07591}
}

Reference. Language Models Represent Space and Time [gurnee2024space]

2024
Wes Gurnee, Max Tegmark
https://arxiv.org/abs/2310.02207v3

@misc{gurnee2024space,
  title = {Language Models Represent Space and Time},
  author = {Wes Gurnee and Max Tegmark},
  year = {2024},
  eprint = {2310.02207},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2310.02207v3}
}

Context

Architecture-indexed ceilings and matched compute [ftip-00JF]

This optional section refines the architecture-neutral capability framework only when the question names an architecture, optimizer, or systems implementation. The general model/interface formulation remains available when these fields are unnecessary.

Architecture comparisons below are conditional on a declared task family, evaluation law, intervention class, and scalar cost. Toy bounds are finite results under their displayed assumptions and do not transfer to concrete model instances without verified interface, task, and cost assumptions.

Architecture, optimizer, and systems variables [ftip-00JG]

Architecture, optimizer, and systems fields refine the model/interface description. A comparison depends only on the fields named in its assumptions and conclusion.

Definition. Optional architecture refinement [ftip-00JH]

Let $M$ denote an existing model intervention. An architecture refinement is a record $A=(\mathcal X,\mathcal Y,\operatorname {Map}_A)$ whose map realizes the same declared input and output interface. Forgetting $A$ returns $M$; no architecture claim is made when the field is omitted.

Definition. Optimizer and systems records [ftip-00JI]

When needed, extend the record by $O$ for optimizer and state-update rules, and $S$ for kernels, precision, memory, cache, and scheduling. The triplet $(A,O,S)$ is descriptive; it is not a scalar intelligence score.

Definition. Architecture-indexed evaluation functional [ftip-00JJ]

Fix an architecture $A$, its base artifact, a declared intervention set $\mathfrak I_A$, and the common evaluation interface of Convention [ftip-005D]. Each $\eta \in \mathfrak I_A$ specifies a protocol $P_{A,\eta }=\operatorname {PostTrain}(A,\eta )$ in that measurable and absolutely integrable evaluation domain, with real performance $J_{\rm ev}(P_{A,\eta })$. Let $c_A(\eta )\in [0,+\infty ]$ be its scalar cost under the study's declared accounting rule: a fixed measured cost or an expected nonnegative cost, as specified. For a finite budget $C\in \mathbb R_{\geq 0}$, define \[ V_A(C)=\sup \left \{ J_{\rm ev}(P_{A,\eta }): \eta \in \mathfrak I_A,\ c_A(\eta )\leq C \right \}\in \overline {\mathbb R}. \] Infinite-cost interventions are infeasible at every such budget.

As in Definition [ftip-005M], the empty feasible set has value $-\infty $; an unbounded-above feasible score set has value $+\infty $. The value is real exactly when that set is nonempty and bounded above. Increasing $C$ enlarges the feasible set, so $V_A$ is nondecreasing. A finite supremum need not be attained by any intervention.

Frontier differences and derivatives are ordinary real operations only where the relevant values are finite, with differentiability additionally required for a derivative. Cost ratios use the domain in Definition [ftip-00JP]. The allowed intervention set, evaluation law, and accounting rule are part of the definition, so $V_A$ is not a universal intelligence function.

Remark. Architecture-neutral projection and gradual complexity [ftip-00JK]

A statement that depends only on the model/interface map is invariant under changes to architecture $A$, optimizer $O$, or systems $S$ that leave that map fixed. A conclusion about one of those fields instead requires assumptions that distinguish its possible values.

Definition. Three ceiling layers [ftip-00JL]

For fixed task and evaluation laws, distinguish the representational ceiling of $A$, the optimizer-reachable ceiling of $(A,O)$, and the systems-feasible frontier of $(A,O,S,C)$. Each layer is conditional on the objects named; none implies that the next layer attains it.

Definition. Capability vector and conditional frontier [ftip-00JM]

For an architecture record $A$, optimizer record $O$, systems record $S$, evaluation configuration $\mathfrak e$ from Definition [ftip-00CE], and declared intervention $\eta $ at cost cap $C$, define the capability vector

\[ \mathbf K(A,O,S,\mathfrak e,\eta ,C) =\bigl (K_{\rm state},K_{\rm adapt},K_{\rm repr}, K_{\rm plast},K_{\rm port}\bigr ). \]

Its coordinates respectively record state access, adaptive computation, representation and abstraction, plasticity, and portability under the named task, intervention, evaluation law, and cost accounting. A coordinate may be a descriptive field, an estimand, or $\bot $ when the record is missing, incompatible, or not measured. The tuple is therefore not a common scalar and does not order architectures without a declared comparison rule.

The five coordinates attach to the existing ceiling layers conditionally: representation claims first require a map from $A$; optimizer-reachable plasticity requires $(A,O)$; state, adaptive computation, and portability claims require the relevant $(A,O,S)$ interface, task family, and deployment regime. A systems-feasible claim also carries $C$, the cost vector and its declared scalarization. The records in Definition [ftip-00JH], Definition [ftip-00JI], Definition [ftip-00JL], and Definition [ftip-00CE] remain authoritative.

The scalar frontier $V_A(C)$ of Definition [ftip-00JJ] is a projection onto the declared utility $J_{\rm ev}$ for one architecture and intervention envelope; it is not a capability coordinate. For a fixed evaluation configuration, a vector-valued study may instead retain the conditional set

\[ \mathcal F_{\mathfrak e}(C) =\operatorname {Pareto}\left \{ \bigl (\mathbf K(A,O,S,\mathfrak e,\eta ,C), J_{\rm ev}(P_{A,\eta }),\mathbf c(P_{A,\eta })\bigr ): c_A(\eta )\leq C \right \}, \]

where Pareto dominance is declared for the reported coordinates. The set is conditional on the task, intervention class, evaluation law, cost vector, and missingness rule; it is not a universal intelligence frontier.

The semantic trajectory diagnostic in semantic representation diagnostics supplies a conditional representation record for this vector; its held-out probe score does not fill a function or deployment coordinate.

Matched-compute arms in § [ftip-00JU] compare entries only after stating which coordinates are held fixed, which are allowed to vary, and how the cost vector is charged. Cost, numerical stability, and monitorability remain evaluation coordinates alongside the vector, so a lower scalar cost or a stable implementation is not silently counted as a capability increase.

Depth reuse and latent recurrence [ftip-00KG]

Repeated depth, latent feedback, and recirculation change the computation performed at inference. Relative to the fixed reference autoregressive Transformer, their costs include any extra passes, state, and prefill work.

Definition. Computation-axis record [ftip-00KH]

For an architecture record $A$, distinguish token steps, feed-forward depth passes, recurrent passes, and latent-state updates. Let $d(A,r)$ be the declared number of depth passes in run $r$; it is a computation coordinate, not a synonym for parameter count or reasoning quality.

Remark. Looped Transformers as a source result [ftip-00KI]

Giannou et al. show that a fixed shallow Transformer placed in a loop can execute programmed iterative computations, including conditional branches and in-context algorithms, in Looped Transformers as Programmable Computers[giannou2023looped]. This is a source result under its program, state, and precision assumptions; it is not a universal claim about language-model capability.

Remark. Parameter sharing and width tradeoffs [ftip-00KJ]

Xue et al. report that depth sharing can reduce trainable parameters while limiting modeling capacity, and study width and mixture-of-experts remedies in Go Wider Instead of Deeper[xue2021wider]. The comparison is empirical and task-specific; it does not identify a depth-independent ceiling.

Remark. Virtual logical depth and scaling [ftip-00KK]

Zhu et al. vary virtual logical depth by reusing weights and report reasoning gains at nearly fixed parameter count in Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws[zhu2025virtualdepth]. Their results motivate a depth coordinate in a scaling study, while leaving knowledge capacity, optimization, and transfer dependent on the declared training and evaluation protocol.

Remark. Recirculation as an inference intervention [ftip-00KL]

Mozer et al. introduce inference-time recirculation that feeds latent states back through an off-the-shelf model and report task improvements in Recirculation[mozer2026recirculation]. The added serial prefill and adaptive tuning belong in the systems and inference records; the observation is not a proof of a new representational ceiling.

Remark. Latent feedback with a preserved Transformer interface [ftip-00KM]

Wang et al. widen the feedback channel between decoding steps with latent feedback while retaining a Transformer and language-modeling interface in Full-bandwidth transformer[wang2026fullbandwidth]. Any reported token or accuracy savings are conditional on the scheduled multi-pass training and measured decoding cost.

Remark. Latent prediction as a training intervention [ftip-00KN]

Teoh et al. add next-latent prediction to next-token training and report compact predictive states without changing the Transformer interface in Next-Latent Prediction Transformers Learn Compact World Models[teoh2025nextlatent]. This belongs to the training intervention record, not to an architecture-only comparison.

Example. Equal parameters do not fix effective depth [ftip-00KO]

Two runs can share parameter count while using different numbers of recurrent or latent-feedback passes. A matched study must therefore report the computation-axis record and cannot infer an architecture ceiling from parameters alone.

Definition. Depth-reuse model-instance record [ftip-00KP]

For a depth-reuse comparison, record shared weights, pass count, stopping rule, recurrent state, training objective, token and pass FLOPs, prefill and decode latency, memory, and evaluation seeds. A pass-count change is an intervention even when the parameter tensor is unchanged.

Example. Fixed-recipe depth comparison [ftip-00KQ]

In a fixed-recipe arm, hold data, optimizer, feedback, stopping rule, and evaluation law fixed while varying only the declared depth-reuse intervention. The resulting difference is an estimand for that recipe, not a best-achievable comparison.

Remark. Scaling surfaces rather than a single law [ftip-00KR]

A depth-reuse study may fit a surface over parameters, tokens, passes, and cost. The fit is an empirical summary over its measured range; it does not establish an asymptotic law or a universal saturation point.

Remark. Architecture and training are separable records [ftip-00KS]

Weight sharing, latent objectives, and recirculation can alter optimization without changing the declared external interface. The comparison must retain architecture, training intervention, and systems coordinates separately.

Remark. Depth reuse across passes, parameters, and tasks [ftip-00KT]

The cited looped, virtual-depth, latent-feedback, and recirculation results do not show that more passes always improve capability, that parameter sharing dominates added parameters, or that inference-time gains transfer to training or to another task family. Each transfer requires a matched model-instance record.

Ceilings, frontiers, and saturation [ftip-00JN]

Definition. Fixed-recipe and tuned frontiers [ftip-00JO]

The fixed-recipe frontier evaluates one common $\eta $. The equal-tuning frontier permits architecture-specific choices from a predeclared trial set with the same tuning data and budget. The restricted envelope permits a predeclared class of interventions under $C$. These are distinct estimands.

Definition. Iso-quality cost ratio [ftip-00JP]

For a finite real target $q\in \mathbb R$ and the frontier of Definition [ftip-00JJ], define the inverse cost \[ C_A(q)=\inf \{C\in \mathbb R_{\geq 0}:V_A(C)\geq q\} \in [0,+\infty ], \qquad \inf \varnothing =+\infty . \] The iso-quality ratio is defined only on the domain \[ \rho _{A/B}(q)=\frac {C_A(q)}{C_B(q)}, \qquad 0\leq C_A(q)<+\infty , \quad 0<C_B(q)<+\infty . \] The architectures must use the same target, evaluation interface, cost units, and comparison arm. The ratio compares efficiency under these choices.

An inverse cost is a threshold infimum, not an executable minimum. The infimum over budgets may be unattained; even if a budget satisfies $V_A(C)\geq q$, its performance supremum may be unattained at $q$. An actual target-achieving intervention requires a separate witness.

Positive individual costs do not guarantee a positive inverse cost. For interventions $\eta _n$, $n\geq 1$, with score $1$ and cost $1/n$, the target $q=1$ has $C_A(1)=0$, although every intervention costs more than zero. If both architectures have this family, the putative ratio is $0/0$ and is excluded by the displayed domain.

Definition. Operational cost vector and scalarization [ftip-00JQ]

Record training FLOPs, inference FLOPs, wall time, memory, energy, and hardware separately as $\mathbf c$. A scalar cost $C=w\cdot \mathbf c$ is a declared study choice with nonnegative units $w$; changing $w$ changes the frontier and must not be hidden as architecture quality.

Theorem. Finite-window representational obstruction [ftip-00JR]

Consider a causal system whose state after each prefix has at most $K$ distinct values, and a task with $K+1$ prefixes requiring pairwise distinct continuation labels. By the pigeonhole principle two prefixes share a state, so at least one continuation label is wrong. This is a finite toy obstruction only; it does not bound a concrete Transformer or KDA instance without a proved reduction to this state model.

Theorem. A conditional saturation certificate [ftip-00JS]

Let $V_A$ be the nondecreasing frontier of Definition [ftip-00JJ], bounded above by a finite $U\in \mathbb R$. If an intervention reaches a real target $q<U$ at a finite cost $C_q\geq 0$, then at every finite $C\geq C_q$, $q\leq V_A(C)\leq U$. In particular $V_A(C)$ is real and $0\leq U-V_A(C)\leq U-q$. This elementary certificate is conditional on the bound $U$; it does not assert that real intelligence saturates.

Example. Equal scalar cost does not identify architecture [ftip-00JT]

Two systems can have the same scalar $C$ while differing in memory, latency, and training allocation. A cost-weight change can reverse their ordering without changing either system. Thus a one-factor comparison needs a fixed scalarization and a reported cost vector; no architecture effect follows from equal $C$ alone.

Matched-compute comparison protocol [ftip-00JU]

Definition. Common-recipe comparison arm [ftip-00JV]

A common-recipe arm fixes training data, optimizer family, schedule, post-training procedure and feedback, training and post-training budgets, inference budget, and evaluation protocol and draws. Interface-compatible parameter choices are declared in advance as part of each permitted architecture package. The comparison varies that package under the common recipe; any further deviation is recorded as a separate comparison coordinate. Its score difference measures performance under this recipe, not the best attainable result for either architecture.

Definition. Equal-tuning-budget comparison arm [ftip-00JW]

An equal-tuning-budget arm permits architecture-specific tuning under a predeclared protocol that fixes the tuning data and its volume, trial count, selection rule, stopping rule, and tuning cost for both architectures. The cost uses the same declared accounting rule. The resulting comparison measures practical performance under this equal optimization effort; it does not determine best possible training or a representation-only limit.

Definition. Restricted-envelope comparison arm [ftip-00JX]

A restricted-envelope arm declares an allowed intervention class $\mathcal E_A\subseteq \mathfrak I_A$ for each architecture, specifying the permitted training, post-training, and inference procedures. The classes expose exclusions, search budgets, seeds, and stopping rules. Restrict the evaluation functional of Definition [ftip-00JJ] to $\mathcal E_A$ and compare the resulting suprema under the same finite cost cap $C\geq 0$, common evaluation law, and common declared cost accounting.

The measurable, integrable evaluation domain and extended-real conventions of Definition [ftip-00JJ] apply: an empty feasible class has value $-\infty $, an unbounded-above feasible score set has value $+\infty $, and a finite supremum need not be attained. The allowed class is part of the comparison, so its envelope is a restriction-dependent quantity, not an unrestricted architectural ceiling.

Definition. Model-instance comparison evidence record [ftip-00JY]

A model-instance record contains architecture/checkpoint identity, parameter count, data and post-training recipe, optimizer, precision, hardware, cache policy, training and inference cost vectors, task suite, and independent evaluation seed. A missing field makes an architecture attribution unknown.

Remark. Systems optimizations are measured interventions [ftip-00JZ]

FlashAttention, KV-cache layouts, quantization, batching, and kernels can change feasible cost. If they approximate, truncate, or alter precision of the mathematical computation, the record must mark that as a systems intervention rather than as a pure architecture comparison.

Theorem. Matched-protocol difference is an estimand [ftip-00K0]

Given $n\geq 1$ evaluation draws, let $\widehat V_A(C)$ be the sample mean of finite real evaluation scores for architecture $A$ at a finite cost cap $C\geq 0$. Under a fixed evaluation law and common-recipe arm, the finite difference $\widehat V_A(C)-\widehat V_B(C)$ is a well-defined empirical estimand.

It does not identify a causal architecture effect when data, tuning, or systems coordinates differ. The conclusion follows directly from the declared record.

Remark. Conditions for applying a toy model [ftip-00K1]

A toy theorem applies to a concrete model only if an explicit map from its state and interface to the model preserves the theorem's assumptions, including its cost accounting. Without such a map, the conclusion concerns only the abstract model; its validity for the concrete system is unknown.

Kimi Delta Attention versus Transformer [ftip-00K2]

Definition. KDA model-instance record [ftip-00K3]

For a Kimi Delta Attention (KDA) versus full-attention baseline study, record the exact Kimi Linear checkpoint, layer mix, context length, hardware, kernel, precision, batch, and decoding workload. The paper describes KDA as a fine-grained gated delta-rule module in a hybrid architecture; those are source observations from Kimi Linear: An Expressive, Efficient Attention Architecture[kimi2025linear], not universal theorems.

If the baseline is called a Transformer, record whether it is the paper's MLA baseline or another full-attention implementation; the label alone does not identify a common architecture or cost.

Example. Matched-cost KDA and Transformer comparison [ftip-00K4]

A matched comparison uses the same task prompts, post-training data and feedback, optimizer family, evaluation protocol, and quality target. The corresponding measurements include parameter count, training FLOPs, inference FLOPs, wall time, memory, KV-cache bytes, and context length for each architecture. A reported speed or quality difference is conditional on these experimental conditions and measurements.

Remark. What the Kimi Linear paper establishes [ftip-00K5]

Kimi Linear reports a hybrid KDA/MLA model and fair-comparison experiments in Sections 3--5, with setup in Section 5.4 and efficiency comparisons in Sections 5.5--5.6 of Kimi Linear: An Expressive, Efficient Attention Architecture[kimi2025linear]. These include long-context efficiency.

The reported throughput and quality are empirical under its training recipe and hardware; they are lower bounds on demonstrated performance, not an architecture-independent ceiling.

Example. Parameter matching is not cost matching [ftip-00K6]

Two models with equal parameter count can have different attention FLOPs, KV-cache memory, kernel utilization, and attainable context. Conversely, equal wall time can hide different hardware and precision. Therefore a parameter-matched result cannot by itself identify a compute frontier.

Remark. Cache, throughput, and quality in KDA comparisons [ftip-00K7]

The KDA example does not prove that every linear-attention model beats every Transformer, that cache savings imply equal quality, or that a measured throughput gain is a capability gain. Any such statement requires a new matched study with the evidence record of Definition [ftip-00JY].

Definition. Model-instance comparison decision rule [ftip-00K8]

Call architecture A more cost-efficient than B at quality $q$ only when the compared threshold costs use the same declared protocol, the confidence procedure and stopping rule are fixed, and all cost-vector coordinates needed by the claim are present. A ratio additionally requires finite costs and a strictly positive denominator as in Definition [ftip-00JP]. State whether the costs are measured target-achieving witnesses or justified inverse-frontier values $C_A(q)$ and $C_B(q)$; observing one successful run does not identify those infima. Otherwise the comparison is descriptive or unknown.

Interface boundaries for alternative architectures [ftip-00K9]

Definition. JEPA comparability interface [ftip-00KA]

A JEPA-like architecture is comparable to an autoregressive model only after fixing the predictor, target representation, decoder or downstream interface, training objective, inference procedure, and evaluation law. A parameter-count match alone is not a typed comparison.

Remark. Representation and task interface are separate [ftip-00KB]

A latent predictor can be excellent under one decoder and unusable under another. The evaluation record must therefore include the interface that maps the representation to the declared task output.

Theorem. Interface-preserving architecture reduction [ftip-00KC]

If two architecture records induce the same conditional output law on a fixed task and evaluation interface, every evaluation functional $J$ gives the same value. This is an immediate pushforward identity under the stated output-law and interface assumptions. It does not imply that distinct architectures are generally equivalent.

Example. Equal benchmark score can hide different ceilings [ftip-00KD]

Two architectures may tie on a finite benchmark while differing on an unmeasured task slice or longer context. Thus a single score cannot establish equal representational ceilings; the task family and evaluation coverage must be declared.

Remark. Architecture-independent model interfaces [ftip-00KE]

A result stated solely in terms of a model/interface map applies to every architecture realizing that map and satisfying its hypotheses. An architecture-specific conclusion can require further assumptions, as in Remark [ftip-00JK].

Remark. Open model-instance questions [ftip-00KF]

The relative capability and cost of KDA, full attention, and JEPA candidates remain empirical questions. A reproducible comparison requires identified checkpoints or reproducible training, fixed evaluation seeds, and measured cost vectors. Architecture-specific ceilings remain estimands and hypotheses rather than established constants.

Published hybrid-attention comparison [ftip-00KU]

Kimi Linear combines recurrent KDA layers with periodic full MLA attention. Its published comparison shows task-dependent score changes and lower long-context decoding times. The selected scores in Example [ftip-00LC], the memory estimate in Example [ftip-00KY], and the discovery calculation in Example [ftip-00L3] connect these observations to a post-training question: when can a cheaper attempt compensate for a possible lower per-attempt success probability?

Example. Published Kimi Linear operating point [ftip-00KV]

The Kimi Linear report compares experimental models pretrained on 1.4 trillion tokens, each with 48 billion total and 3 billion active parameters. Kimi Linear uses three Kimi Delta Attention layers for each full Multi-head Latent Attention layer, with no positional encoding (NoPE) in its MLA layers; the reference uses full MLA attention [kimi2025linear, Sections 4 and 5.4]. These are properties of the reported hybrid architecture.

These experimental models are distinct from the released Kimi Linear model, pretrained on 5.7 trillion tokens and supporting up to one million tokens of context. Appendix D compares that release with Moonlight, which has 16 billion total and 3 billion active parameters [kimi2025linear, Section 5.4.1 and Appendix D].

Example. Published efficiency observations [ftip-00KW]

For batch size one at one million tokens, Figure 7 labels a $2.9\times $ prefill speedup and a $2.2\times $ decoding speedup for Kimi Linear against MLA. Section 6.3 gives $2.3\times $ for the latter point, so the graphic and prose differ slightly [kimi2025linear, Figure 7 and Sections 5.6 and 6.3].

The larger-batch comparison in Figure 1(b) gives decoding time per output token of 1.84 milliseconds for Kimi Linear and 11.48 milliseconds for MLA, with a reported $6.3\times $ speedup. Section 6.3 describes this as a theoretical speedup from reallocating saved KV-cache memory to larger batches. It illustrates a different use of the memory saving from the batch-one latency result [kimi2025linear, Figure 1(b) and Section 6.3].

Figure 1(a) reports RULER scores of 84.3 for Kimi Linear and 81.3 for MLA at 128k context, with $3.98\times $ decoding acceleration [kimi2025linear, Figure 1(a)]. The report also gives a reduction of up to 75 percent in KV-cache use [kimi2025linear, Abstract]. Together these observations motivate studying context length and batch size as separate cost coordinates. The unequal RULER scores and different timing regimes are retained when interpreting those points.

Definition. Quality target and cost measurement [ftip-00KX]

For a reported benchmark score q, use the same task, prompt law, scoring rule, and post-training condition for both systems. The comparison records the smallest measured cost at which each system reaches q, when such a measurement exists. A throughput or cache ratio is not substituted for this iso-quality cost.

Example. A cache-memory estimate for hybrid attention [ftip-00KY]

The cited FLA KDA implementation at commit 6b6f546f stores one active recurrent state per sequence, with dimensions determined by the value heads and key/value dimensions rather than accumulated context length. Its newly allocated state uses float32 [⧉]. The optional short-convolution state has one fixed-width kernel buffer per channel and sequence [⧉]. This supplies a concrete implementation model for the recurrent part of the 3:1 mixture in Example [ftip-00KV].

Consider a stylized comparison with $4\ell $ layers, batch size $b$, and context length $T$, where $\ell ,b,T$ are positive integers. Assume each MLA layer stores $\kappa T$ bytes per sequence, with the same $\kappa >0$ in both designs, and each KDA layer stores one terminal recurrent-plus-convolution state of $\sigma >0$ bytes. The active attention-state memories are

\[ M_{\rm MLA}=4b\ell \kappa T,\qquad M_{\rm hybrid}=b\ell (\kappa T+3\sigma ),\qquad \frac {M_{\rm hybrid}}{M_{\rm MLA}} =\frac {1}{4}+\frac {3\sigma }{4\kappa T}. \]

The hybrid uses less active state exactly when $T>\frac {\sigma }{\kappa }$, and the ratio tends to $1/4$ as context grows. This explains why replacing three quarters of the growing caches can approach a 75-percent saving, while fixed recurrent-state overhead can erase the advantage at short contexts. The retained MLA layers keep the hybrid's state memory linear in $T$; the whole model is not a fixed-memory recurrent system.

Weights, training activations, workspaces, allocator padding and saved prefix states are outside this active-state calculation. At a fixed device memory budget, the estimate predicts more room for simultaneous or longer rollouts when attention state dominates. It does not predict a fourfold speedup. Training and decoding also use different execution paths: FLA selects chunk kernels for gradient-enabled computation and fused recurrence for short inference calls [⧉]. The fixed-state estimate is therefore a useful serving hypothesis, not a training-memory or training-speed formula.

Example. Common-recipe reading of the Kimi result [ftip-00KZ]

The report holds its stated pretraining and SFT recipes common across the experimental models [kimi2025linear, Sections 5.4--5.5 and Table 4]. The selected observations in Example [ftip-00LC] compare the resulting trained designs, including the hybrid's 3:1 KDA/MLA pattern and NoPE choice. The mixed score directions suggest a task-dependent tradeoff rather than one ordering of the two architectures.

The theoretical mechanism is a change in the cost of maintaining and using the prefix: recurrent layers compress it into state, while the retained MLA layers preserve growing attention caches. The model in Example [ftip-00KY] predicts the largest memory advantage at long contexts. If this advantage reduces the cost of generating useful candidates, Example [ftip-00L3] predicts when more attempts can offset a reduction in per-attempt quality. This is a conditional explanation to test through rollout costs and verified success rates; it does not require attributing the whole result to KDA alone.

Example. Measured iso-quality cost ratio [ftip-00L0]

Let $c_K(q)$ and $c_T(q)$ be measured inference costs for Kimi Linear and the Transformer reference at the same finite real score $q$ and context length, under one accounting rule. If $0\leq c_K(q)<+\infty $ and $0<c_T(q)<+\infty $, the measured ratio $\widehat \rho _{K/T}(q)=c_K(q)/c_T(q)$ is reported together with the full cost vectors. These measured costs need not be the inverse frontier infima of Definition [ftip-00JP]. If either threshold is unmeasured or the denominator is zero, the ratio is left undefined rather than inferred from the paper's speedup plot.

Remark. Hybrid attention is not pure linear attention [ftip-00L1]

Kimi Linear combines KDA with periodic full-attention layers. A measured advantage therefore identifies the reported hybrid intervention, not a claim that a purely linear recurrent architecture has the same quality or ceiling. The layer pattern belongs in the architecture record.

Remark. Demonstrated performance is a lower bound [ftip-00L2]

The published benchmark scores demonstrate attainable points for the reported checkpoints and protocol. They do not establish a representational ceiling, a universal scaling law, or a claim that additional cost cannot improve either architecture.

Example. When cheaper attempts compensate for lower success [ftip-00L3]

Smaller caches can support more concurrent requests or avoid preemption and recomputation. These are actual serving mechanisms in vLLM's memory and scheduling guidance [⧉]. For post-training, the useful prediction is therefore about completed candidate attempts under a budget, including verification and optimization costs, rather than decoding speed alone.

Use the following cost model on the same hardware. One baseline attempt costs $c>0$; generation accounts for a fraction $f\in [0,1]$. Suppose generation becomes $s_{\rm gen}>0$ times as fast while all other cost per attempt stays fixed. The new cost and total attempt-throughput factor are

\[ c_K=c\left (1-f+\frac {f}{s_{\rm gen}}\right ),\qquad S=\frac {c}{c_K} =\frac {1}{1-f+f/s_{\rm gen}}. \]

For a fixed budget $B\geq 0$, this model permits $N_M=\lfloor B/c\rfloor $ baseline attempts and $N_K=\lfloor B/c_K\rfloor $ hybrid attempts. Assume $N_M,N_K\geq 1$ and verified-success events independent within each set of attempts, with respective probabilities $p_M,p_K\in [0,1]$. Then Theorem [ftip-0078] gives discovery probabilities $1-(1-p_M)^{N_M}$ and $1-(1-p_K)^{N_K}$. The hybrid matches or exceeds baseline discovery exactly when

\[ p_K\geq 1-(1-p_M)^{N_M/N_K}. \]

This follows by comparing the two failure probabilities and taking the nonnegative $N_K$-th root.

For an illustrative estimate, suppose generation consumes 80 percent of the baseline cost and use $s_{\rm gen}=2.2$, motivated by the batch-one decode figure in Example [ftip-00KW]. Applying that decode factor to the whole generation stage is a working approximation, not a measured rollout result. Then $S\approx 1.774$; a budget of $B=10c$ buys 10 baseline attempts or 17 hybrid attempts. If $p_M=0.10$ and $p_K=0.08$, the discovery probabilities are approximately $0.651$ and $0.758$. The break-even hybrid probability is about $0.0601$. Thus this model predicts that more attempts can outweigh a moderate per-attempt quality loss. The assumed probabilities are illustrative verified-success rates, not conversions of the benchmark scores in Example [ftip-00LC].

The prediction is strongest for long-context, generation-heavy work whose saved memory becomes usable rollout capacity. As verification or optimization dominates, $f$ shrinks and $S$ approaches one. Correlated attempts require the joint-law analysis of Convention [ftip-0077] instead of the independent product. Measure completed attempts, verified-success rates and total cost under a fixed prompt, decoding and verifier protocol to test the tradeoff. In the RLVR round of Example [ftip-004M], additional successful candidates can improve the available feedback; whether they produce useful accepted updates is a further optimizer-and-evaluation question, as Example [ftip-0085] demonstrates.

Paired measurements for model comparison [ftip-00L4]

A paired measurement specifies both model instances and their task, intervention, evaluation, and cost coordinates. These conditions determine the meaning of each point in the measured frontier.

Definition. Comparison pair identity [ftip-00L5]

A paired comparison identifies the two systems, exact checkpoint or training commit, architecture variant, parameter counts, tokenizer, and context limit. A label such as ``Transformer'' is insufficient when attention, cache, or layer patterns differ.

Definition. Common task and evaluation conditions [ftip-00L6]

Both systems use the same task family, prompt and data law, scoring rule, sampling seeds, stopping rule, and evaluator version. Any exception is a separate comparison arm, not an unrecorded architecture effect.

Definition. Training and post-training coordinates [ftip-00L7]

Record data volume, token order policy, optimizer and schedule, supervised tuning, feedback or reinforcement procedure, update count, and selection rule. The common-recipe arm fixes these coordinates; the equal-tuning arm allows predeclared alternatives with equal search resources.

Definition. Inference and systems coordinates [ftip-00L8]

Record hardware, software and kernel versions, precision, batch, cache policy, sequence lengths, warm-up procedure, concurrency, and decoding settings. A kernel or cache change that alters numerical computation is marked as an intervention rather than hidden as an implementation detail.

Definition. Per-system cost vector [ftip-00L9]

For each system, the cost vector $\mathbf c$ contains training FLOPs, inference FLOPs, wall time, memory, energy when available, and context length. The primary scalar $C$ and its weights are declared beside the vector; omitted coordinates are marked unknown.

Definition. Repeated measurement rule [ftip-00LA]

For each fixed workload and system, record warm-up runs, repetition count, random seeds, and the aggregation rule. Report mean and dispersion for time, memory, and score; a single fastest run cannot define a cost frontier.

Definition. Paired uncertainty estimates [ftip-00LB]

When the same evaluation items are used, report paired score differences and an uncertainty procedure fixed before inspecting the result. Confidence intervals quantify sampling variation; they do not repair unmatched training, systems, or task coordinates.

Example. Published Kimi Linear comparison [ftip-00LC]

Table 4 of the Kimi Linear report compares the experimental models pretrained on 1.4 trillion tokens in Example [ftip-00KV], after the same supervised fine-tuning (SFT) recipe. Both have 48 billion total and 3 billion active parameters. Three selected benchmark scores are [kimi2025linear, Sections 4 and 5.4 and Table 4]:

Arm	MMLU-Pro	LiveBench	EvalPlus
Kimi Linear	67.4	45.2	61.0
Full MLA	65.7	45.7	62.6

The entries use the paper's score units, with larger values better on each selected benchmark; LiveBench is reported as Pass@1. Kimi Linear is higher on MMLU-Pro and lower on LiveBench and EvalPlus in these observations. These are reported point estimates on different tasks, not a single capability score; the table supplies no uncertainty estimate.

The common recipe uses the K2 pretraining corpus, MuonClip and a shared training schedule. SFT proceeds from broad instruction data to targeted reasoning tasks; evaluation uses temperature 1.0 and an internal framework derived from LM-Harness [kimi2025linear, Section 5.4]. Thus the scores provide a concrete example of how the trained hybrid and full-MLA designs respond to the same recipe. The cost mechanism and its possible post-training consequences are developed in Example [ftip-00KZ].

Remark. Measured and assumed costs [ftip-00LD]

A useful cost model can combine measurements with assumptions justified by the architecture and its implementation. In Example [ftip-00L3], the generation share $f$ and speed factor $s_{\rm gen}$ determine a predicted attempt-throughput factor. Their assumed values can be varied without pretending they were measured in the original experiment.

For fixed $f\in [0,1]$, write $S(s)=1/(1-f+f/s)$ for $s>0$. This function is nondecreasing. An interval $s_{\rm gen}\in [s_-,s_+]$, with $0<s_-\leq s_+$, therefore gives the conditional range $S\in [S(s_-),S(s_+)]$. This is sensitivity to a chosen assumption range; it is a statistical confidence interval only if a sampling argument supplies that interpretation.

Definition. Measured contrasts and conditional predictions [ftip-00LE]

A measured paired contrast evaluates the same declared quantity for two observed model instances under a specified task, protocol and cost accounting. The common-recipe scores in Example [ftip-00LC] are one example. A confirmatory comparison fixes its matching and selection rules before examining the scores.

A conditional paired prediction instead uses an explicit model to supply one or more cost or response quantities, then derives the comparison under those assumptions. The discovery probabilities in Example [ftip-00L3] are an example. Such a prediction can be motivated by existing observations and guide a later experiment; its unmeasured inputs remain assumptions, and agreement with new matched measurements tests the model.

Example. How generation share changes the predicted gain [ftip-00LF]

Keep the illustrative generation factor $s_{\rm gen}=2.2$ from Example [ftip-00L3] and vary the baseline generation share $f$. Its cost model gives the following total attempt-throughput factors:

Generation share	Predicted throughput factor
20 percent	1.122
50 percent	1.375
80 percent	1.774
100 percent	2.200

These calculated values show why the same decoding improvement can matter much more for generation-heavy search than for a verifier- or optimizer-dominated workload. They suggest measuring the time spent in generation, verification and updates before selecting where to invest systems effort. A change in the bottleneck changes the useful intervention, even when the attention architecture stays fixed.

Remark. Testing the predicted architecture tradeoff [ftip-00LG]

The memory and discovery models predict the strongest benefit when long prefixes make attention state expensive, saved capacity improves useful rollout throughput, and per-attempt success remains above the break-even threshold. Short contexts, expensive verification, or strongly correlated attempts can weaken that benefit. These are distinct mechanisms to test, rather than a universal ranking of KDA and full attention.

A comparison across implementations can retain this reasoning while re-estimating state sizes, generation share and success probabilities for the new system. Report both the prediction and the matched measurements: their agreement or disagreement identifies which mechanism or assumption needs revision.

Conditional frontier and saturation analysis [ftip-00LH]

A finite matched study measures capability at selected values of one declared cost. Its frontier and saturation target are conditional on that protocol; they do not establish a universal intelligence law.

Definition. Finite cost grid [ftip-00LI]

Choose a finite ordered grid $0<C_1<\cdots <C_m$ and evaluate each architecture at every declared grid point under the same task and protocol. The resulting values $\widehat V_A(C_i)$ are observations of the restricted frontier, not its values between grid points.

Definition. Observed upper envelope [ftip-00LJ]

For an observed score $s_A(C_i)$ define the discrete upper envelope $U_A(C_i)=\max _{j\leq i}s_A(C_j)$. It is a descriptive monotone summary of the measured points; it is not evidence that unmeasured costs attain the envelope or that extra cost cannot reduce score.

Definition. Uncertainty bands on the frontier [ftip-00LK]

Attach a predeclared uncertainty interval $I_A(C_i)$ to each score and carry those intervals through score differences and threshold crossings. A finite band describes sampling and measurement variation; it does not cover unmeasured training procedures or architectures.

Definition. Declared saturation target [ftip-00LL]

Fix a quality target $q\in \mathbb R$, tolerance and cost increment $\varepsilon ,\delta \in (0,+\infty )$, and a budget $C\in \mathbb R_{\geq 0}$. Require both $V_A(C)$ and $V_A(C+\delta )$ to be finite real values. A study calls an architecture $\varepsilon $-saturated at $C$ only relative to its allowed intervention class when the restricted frontier has certified gain $V_A(C+\delta )-V_A(C)\leq \varepsilon $.

Theorem. Conditional saturation certificate [ftip-00LM]

Take finite real $U$, $C\geq 0$, $\delta \geq 0$, and $\varepsilon \geq 0$, with the nondecreasing frontier of Definition [ftip-00JJ]. If a justified upper bound gives $V_A(C+\delta )\leq U$ and a justified lower bound gives $V_A(C)\geq U-\varepsilon $, then $U-\varepsilon \leq V_A(C)\leq V_A(C+\delta )\leq U$. Both endpoint values are therefore finite real, and $0\leq V_A(C+\delta )-V_A(C)\leq \varepsilon $. This is a conditional bound for the named frontier; it proves no universal saturation of intelligence.

For the finite controller class with hard resource admission, Corollary [ftip-00MC] obtains the required bounds from a feasible controller and a uniform Bellman certificate.

Example. Finite observations need not identify a frontier [ftip-00LN]

Let $S\subset \mathbb R_{\geq 0}$ be a finite set of measured costs, and choose $C_0>0$ larger than every element of $S$. Suppose the exact frontier value observed at each cost in $S$ is zero. For finite budgets $C\geq 0$, suppose the declared class of possible frontiers permits both \[ V_0(C)=0, \qquad V_1(C)= \begin {cases} 0,&0\leq C<C_0,\\ 1,&C\geq C_0. \end {cases} \] These nondecreasing frontiers with scores in $[0,1]$ agree on every observed cost and differ at $C_0$. The observations alone do not distinguish them.

Both possibilities have finite realizations in the framework of Definition [ftip-00JJ]: allow two interventions with costs $0$ and $C_0$. Give the first score zero and the second score $\theta \in \{0,1\}$. For example, on a single deterministic evaluation task with utility equal to the output bit, let the two resulting protocols return $0$ and $\theta $. The two possible choices of $\theta $ give $V_0$ and $V_1$, respectively, under the same intervention and cost specification. Every score is finite, and the feasible score maximum is attained at each budget.

This example does not supply a strictly better agreeing frontier for every possible data set or every admissible class. If a proved global score bound is $1$ and an intervention attains it at a finite cost $C_*\geq 0$, monotonicity forces $V_A(C)=1$ for every $C\geq C_*$; no higher value is admissible. A singleton class of possible frontiers can also identify the frontier without such an alternative. A conditional certificate such as Theorem [ftip-00LM] therefore requires its stated upper-bound evidence; that evidence does not follow merely from the absence of an observed improvement.

Definition. One-factor cost interpretation [ftip-00LO]

The primary cost $C$ is a declared scalarization of the recorded cost vector. A frontier statement is conditional on its units and weights; the same paired measurements may produce a different ordering under a different scalarization.

Example. Crossing architecture frontiers [ftip-00LP]

Two architectures may alternate in the observed ordering across costs: one can score higher at small $C$ while the other catches up at larger $C$. Thus a single comparison point cannot establish a global ordering or a common ceiling.

Example. Frontier report for the KDA study [ftip-00LQ]

A Kimi Delta Attention versus Transformer study should publish the cost grid, paired scores, uncertainty intervals, scalarization weights, and the exact identities and configurations of the compared models. The report may then state which measured points are Pareto or iso-quality comparisons under that protocol.

Remark. Frontiers depend on the intervention and evaluation [ftip-00LR]

The conditional frontier is indexed by architecture, intervention class, task family, evaluation law, and cost scalarization. Changing any of these coordinates creates a new estimand; no ordering transfers automatically to a new checkpoint, optimizer, hardware stack, or task family.

Study design for comparisons across model instances [ftip-00LS]
✍️source

Definition. Matched model-instance record [ftip-00LT]

Definition. Pair identity and comparison unit [ftip-00LU]

Remark. Paired responses to a common recipe [ftip-00LV]

Remark. Different recipes under equal tuning effort [ftip-00LW]

Remark. How an allowed class changes its envelope [ftip-00LX]

Remark. Coordinate-change rule [ftip-00LY]

Definition. Architecture-family transfer matrix [ftip-00LZ]

Comparison cell	Source	Estimand	Matched controls	Cost / stability / monitorability	Status and scope boundary
Transformer--KDA/full attention	Remark [ftip-00KF], Definition [ftip-00LU], Example [ftip-00LQ]	Difference in utility, cost, and $K_{\rm state}$ between KDA and full attention	Same task, model scale, recipe, context, seeds, and evaluation law	Report measured cost vector, training/inference stability, and state/failure observability	Open; a finite KDA--Transformer result does not establish an architecture-wide ceiling
Transformer--Mamba	Mamba, Sections 3.1--3.3; § [ftip-00OQ]	Difference in retrieval utility and $K_{\rm state}$ at the declared budget	Task, scale, recipe, context, and inference budget	Report cost vector, recurrence stability, and state/failure observability	Open; selective compression does not transfer a retrieval result across tasks
Transformer--linear attention	Linear attention, Sections 3.2--3.3; § [ftip-00OQ]	Difference in retrieval utility and $K_{\rm state}$ for associative summaries	Task family, feature map, context budget, and evaluation law	Report prefix cost, numerical stability, and summary-state observability	Open; associative summaries do not imply full-attention equivalence
Transformer--CTM or looped model	Continuous Thought Machine, Sections 3.1--3.5; § [ftip-00KG]; § [ftip-00OQ]	Difference in utility and $K_{\rm adapt}$ per charged tick or loop	Input, base model, stopping rule, recipe, and charged internal compute	Report tick/loop cost, training and inference stability, and stopping-signal monitorability	Open; extra passes are charged and do not imply continuous-time behavior
Transformer--BDH	BDH, Section 3.2; § [ftip-00OQ]	Difference in task utility and $K_{\rm state}$ under the reduced state interface	Task, state parameterization, credit horizon, systems stack, and cost	Report memory and compute cost, optimization stability, and state observability	Open; a reduced state-space proposal is not a production transfer result
Transformer-Recurrent Looped Transformer	§ [ftip-00OQ]; [zhang2026rlt, Sections 2-3 and 5-8]	Paired change in finite-task utility and $K_{\rm state}$ under recurrent decoder feedback	Same task, model family, parameter count, training tokens, seeds, and feedback interface; causal no-feedback and chunking arms	Report encoder/decoder FLOPs, recurrent state, chunk latency, memory, stability, and state monitorability	Open and finite-only until a declared deployment arm and transfer record exist
Residual baseline: Deep Delta rewrite	§ [ftip-00OQ]; [zhang2026deepdelta, Sections 2-4 and 6]	Paired change in utility and rewrite-sensitive state behavior after the rank-one read/compare/target/write intervention	Matched residual capacity, optimizer, tokens, seeds, and model family; no-write, write-only, and capacity controls	Report inference FLOPs, memory, residual/cache state, numerical stability, and rewrite monitorability	Open; finite GPT-2 evidence does not establish a general capability or frontier claim
Core architecture--JEPA adjunct	I-JEPA, Section 3; § [ftip-00OQ]	Conditional change in $K_{\rm repr}$ and $K_{\rm plast}$ after the objective intervention	Same base artifact, target task, predictor budget, data, and optimizer arm	Report intervention cost, training stability, and representation/probe monitorability	Conditional; an objective intervention is not a replacement architecture
I-JEPA--SplitJEPA	SplitJEPA, Sections 3.1--3.4; § [ftip-00OQ]	Subspace identifiability and conditional change in $K_{\rm repr}$	Matched invariant correspondences, variant-rank premise, normalization, and encoder budget	Report probe cost, numerical and optimization stability, and subspace monitorability	Conditional; identifiability does not become a general I-JEPA guarantee
Core architecture--liquid dynamics	Liquid time-constant networks, Sections 1--2; § [ftip-00OQ]	Difference in utility and $K_{\rm adapt}$ under the solver intervention	Input law, solver, step budget, tolerance, task, and cost vector	Report solver cost, numerical stability, and trajectory/failure monitorability	Open; continuous dynamics and solver choices remain intervention coordinates
Core architecture--Neural Operator	Neural Operators, Section 3; § [ftip-00OQ]	Difference in utility and $K_{\rm repr}$ across discretizations	Function-space task, discretization family, operator layer, and evaluation law	Report training/inference cost, discretization stability, and field-observation monitorability	Open; discretization invariance under one source formulation is not task transfer

Remark. Applying a finite model to an architecture [ftip-00M0]

Definition. Negative-result and stopping report [ftip-00M1]

Remark. Open comparison questions [ftip-00M2]

The matrix in Definition [ftip-00LZ] is a protocol map, not a result table. The following probes keep the five capability coordinates separate and leave an unknown cell visible until the required records exist. Each probe names its source, estimand, controls, and evaluation coordinates so that a missing record cannot be mistaken for a zero score.

State access. Sources: Mamba, Sections 3.1--3.3 and linear attention, Sections 3.2--3.3. Estimand: the paired difference in retrieval utility and $K_{\rm state}$. Controls: Transformer, Mamba, and linear-attention systems on a predeclared retrieval and continuation suite at matched parameter, context, recipe, and cost budgets. Evaluation: report cost, recurrence or summary stability, and state observability. Scope boundary: a long-context score does not transfer to another task family without its task and transport record.
Recurrent feedback. Source: [zhang2026rlt, Sections 2-3 and 5-8]. Estimand: the paired change in finite-task utility and $K_{\rm state}$ from decoder feedback. Controls: a common causal encoder, model size, training tokens, seeds, and matched per-token budget, with no-feedback and declared chunk-size arms. Evaluation: report recurrent-state bytes, encoder/decoder FLOPs, chunk latency, numerical/training stability, and feedback monitorability. Scope boundary: parity, permutation tracking, and modular arithmetic remain finite evidence; without a deployment and transfer record, the cell is finite-only.
Residual rewrite. Source: [zhang2026deepdelta, Sections 2-4 and 6]. Estimand: the paired change in utility and rewrite-sensitive state behavior from a gated rank-one read/compare/target/write edit. Controls: residual capacity, optimizer, tokens, seeds, and model family, with no-write, write-only, and capacity-matched arms. Evaluation: report rewrite and residual-state monitorability, inference FLOPs, memory, numerical stability, and a held-out transfer task. Scope boundary: finite GPT-2 observations do not identify a general capability gain or architecture-wide law.
Adaptive computation. Source: Continuous Thought Machine, Sections 3.1--3.5 and the looped-model contract in § [ftip-00KG]. Estimand: the paired change in utility and $K_{\rm adapt}$ per charged internal tick or loop. Controls: common input, base model, recipe, stopping rule, and charged internal compute. Evaluation: report tick/loop cost, training and inference stability, and stopping-signal monitorability. Scope boundary: an observed adaptive gain remains conditional on that stopping law.
Representation and abstraction. Sources: I-JEPA, Section 3 and SplitJEPA, Sections 3.1--3.4. Estimand: subspace identifiability and the conditional change in $K_{\rm repr}$. Controls: fixed correspondences, full-rank variant differences, normalization, encoder budget, and matched downstream probes. Evaluation: report probe cost, optimization stability, and subspace monitorability. Scope boundary: the conditional subspace result is separate from downstream task transfer; failure of a premise is an incompatible cell, not a zero score.
Plasticity. Source: the objective and optimizer interfaces in Definition [ftip-00JM] and the I-JEPA intervention description in § [ftip-00OQ]. Estimand: the paired change in $K_{\rm plast}$ after a declared post-training intervention. Controls: common recipe and equal-tuning arms with data, optimizer, trial count, stopping, and feedback fixed or explicitly varied. Evaluation: report intervention cost, training stability, and update-signal monitorability. Scope boundary: an intervention effect is limited to the declared base artifact, task, and objective; it does not transfer across architectures without a new paired record.
Portability. Sources: the Mamba, BDH, and Neural Operator method descriptions in § [ftip-00OQ]: Mamba, BDH, and Neural Operators. Estimand: survival of the declared utility and $K_{\rm port}$ across one transfer edge. Controls: source model, task family, model scale, deployment regime, evaluation law, and cost vector retained as paired fields. Evaluation: report migration cost, stability after the shift, and failure monitorability. Scope boundary: evidence for one transfer edge does not order unmeasured edges; blank edges remain unmeasured.
Adjunct interactions. Sources: liquid time-constant networks, Sections 1--2, Neural Operators, Section 3, and I-JEPA, Section 3. Estimand: the joint change in the declared capability coordinates after an explicit intervention. Controls: declared core model, objective, temporal law, function-space bias, matched controls, and separate single-intervention arms. Evaluation: report joint and marginal costs, numerical/training stability, and interaction monitorability. Scope boundary: a joint effect is limited to the declared core, task, temporal law, and function-space regime; it does not transfer across regimes without separate intervention records.

Semantic representation diagnostics along pretraining checkpoints [ftip-00OZ]

\[ \ell ^*_{m,c,q}=\arg \max _{\ell \in L_m}s^{\mathrm {val}}_{m,c,q,\ell },\qquad s_{m,c,q}=s^{\mathrm {test}}_{m,c,q,\ell ^*_{m,c,q}}. \]

A predeclared layer may replace the validation choice, but a test score is never used to select $\ell ^*$; this keeps the emergence score from being a layer-selection envelope.

The primary absolute emergence threshold is $\tau =0.50$ in this held-out score, chosen before the trajectory is inspected. The first crossing is

\[ e_{m,q}(\tau )=\min \{c:s_{m,c,q}\geq \tau \}, \]

Convention. Identifiability regimes for semantic representation diagnostics [ftip-00P1]

Finite identifiability record. For a fixed model family, checkpoint, task split, and probe seed, record

Backlinks

Remark. How to read these notes [ftip-00NW]

The seven chapters connect learning mechanisms, computational constraints, agent reliability, evaluation, capability growth and economic conditions. The diagram shows their main logical relationships. Solid arrows indicate concepts used by another analysis. The dashed branch supplies architecture-specific detail for comparisons involving a particular model implementation or optimizer. Within that branch, the mechanism map identifies which capability bottleneck changes and what evidence would show the change; the second dashed edge marks its possible consequences for representations and discovery across model generations.

$\text{[math]}$

Post-training and agent state are complementary sources of capability change: one changes learned parameters, while the other manages interaction, computation and retained artifacts. Evaluation studies the evidence for those changes and their limits. Architecture analysis connects the comparison to the model's computation and optimization. The lineage analysis extends it to discovery and learning across generations. Economic analysis then asks how productive capacity, knowledge renewal and financing make the required resources available, and how learning changes those conditions.

Foundations and interfaces introduces capability claims, token probabilities, pretraining, tasks and environments. These interfaces allow the later analysis to specify a model without requiring a particular neural architecture.
Post-training, alignment, and feedback develops fine-tuning, preference learning, RLHF, DPO, RLVR and agentic training, with concrete objectives, feedback mechanisms and update procedures.
Agent state, computation, and reliability studies persistent state, retained context, recursive execution, failure modes and reliability. It separates changes to agent behavior from changes to model weights while examining their interaction.
Evaluation, evidence, and finite limits fixes the comparison law and costs, studies transport to other evaluations, and develops finite results about discovery, support, feedback and proxy error. Its closing synthesis collects the consequences and counterexamples.
Architecture-conditioned potential and computation begins with a decoder-only Transformer, then uses the mechanism-family map to ask which capability bottleneck a design changes. Its optimization-geometry and matched-compute sections use the evaluation framework to make implementation-specific claims. The core panel is the default comparison path; the adjunct panel supplies optional objective, temporal-dynamics and function-space lenses.
Conceptual discovery across model generations studies discovery and learning across successive artifacts. It develops the complete lineage, independently developed contributions, representation learning, curricula and mathematical research settings, alongside the conjecture and arguments that could establish or defeat it. Its research route records the capability profile and tests retrieval, transfer, remix, construction, consequence, verification and acquisition as separate outcomes.
Civilization, economic reproduction and capability conditions learning on feasible economic paths. It develops knowledge renewal and financing bounds, reliable acquisition from contributors, shared preparation costs, and alternative models with different implications for continued learning.

The research question and scope introduces the common distinctions between elicitation, acquisition, computation and evaluation. Questions about fine-tuning, preferences, rewards and interaction lead to the post-training chapter. Questions about memory, tools, recursive execution and reliability lead to the agent chapter. Both use the model, task and environment interfaces in the foundations.

Questions about a particular model implementation or optimizer are developed in the architecture chapter. Begin with its mechanism-family map when the question is which bottleneck a mechanism changes and what evidence could establish the change. Then follow the frontier and protocol route through architecture-indexed ceilings and matched compute, published paired measurements, paired model measurements, conditional frontiers and study design. The shared evaluation framework supplies the task laws and resource comparisons used in that analysis.

A mechanism-first route reads the core panel before the frontier sections. Its adjunct panel is optional: it changes an objective, temporal law or function-space bias and should be compared under the same interfaces rather than treated as another architecture family. Questions about representations, curricula and discovery continue through conceptual discovery; questions about costs, stability, monitorability and transfer return to evaluation and finite limits and its comparison controls.

Claims about a capability gain or limit are examined in independent evaluation and post-training potential and the following finite results and counterexamples. These sections distinguish training reward from evaluation evidence, examine transport to other task laws, and make the assumptions behind a bound explicit. Training, search and deployment costs have distinct roles in those comparisons.

Questions about reusable concepts and capability growth across generations are developed in conceptual discovery. Read its complete lineage and acquisition criterion first; then use the capability profile, semantic discovery records and research assays inside the mathematical research setting. The conjecture and its arguments remain the place to assess the proposed separation, while contributor development, representations and curricula provide distinct settings for it.

Questions about the origin of a learning budget begin with economic state and admissible paths and the uniform resource envelope. The knowledge-renewal model and financing account give distinct constraints; the automation game studies an equilibrium effect under a narrower institutional assumption. The contribution protocol and portfolio comparison specify possible cost advantages. Read these with the alternative mathematical models: they show which maintenance, investment, substitution and simulation assumptions can prevent a proposed separation. Each proved implication is conditional; none alone establishes a universal training ceiling or the open conceptual-discovery lower bound.

Mechanism families and capability bottlenecks [ftip-00OQ]

This section compares mechanism families by the capability bottlenecks they change and by the evidence needed to detect those changes. The shared model, task, intervention, cost and evaluation interfaces remain fixed conditions for every claim, while source descriptions, measured results and transfer claims remain separate.

The comparison uses five capability dimensions: state access, the information that can remain available across tokens, steps or external memory operations; adaptive computation, conditional allocation of depth, recurrence, search or stopping together with its charged cost; representation and abstraction, the available representation, composition or predictive state; plasticity, the changes reachable by a declared objective, optimizer, parameter tying and post-training recipe; and portability, survival of a result when the model family, task, scale or deployment regime changes. This is distinct from transfer, which evaluates a frozen artifact on new problems under a common deployment budget. They are coordinates for the capability vector, not a single intelligence scalar.

A bottleneck is the declared resource or interface condition that limits one of these dimensions in a comparison. Cost, numerical stability and monitorability remain evaluation coordinates: the ability to observe the relevant state, compute, update or failure signals under the study protocol. A matched-compute comparison fixes the cost accounting before comparing outcomes; its protocol and the architecture frontier remain authoritative elsewhere in this chapter.

The default core panel contains mechanism families that alter the sequence computation or its state interface. Existing Transformer notation and the decoder-only reference are in § [ftip-000K]; the entries below identify the question each family makes testable.

Transformer reference. Attention exposes token-indexed content access through the declared context and serves as the baseline for the matched-compute comparison and interface comparisons.
Selective state-space models. The Mamba construction, Sections 3.1--3.3 makes the state-space parameters input-dependent and computes the resulting time-varying recurrence with a hardware-aware scan. The testable bottleneck is how selective compression trades long-context access and throughput against retrieval and portability boundaries; a source mechanism does not settle that tradeoff.
Linear attention. The linear-attention formulation, Sections 3.2--3.3 uses a feature-map factorization and associative prefix summaries rather than the full softmax attention matrix. The comparison tests which retrieval and memory tasks preserve their evaluation under the changed state interface; it does not identify every hybrid or recurrent model with pure linear attention.
Internal-tick and looped models. The Continuous Thought Machine, Sections 3.1--3.5 uses a discrete internal tick dimension, private neuron-level models that process activation histories, synchronization as a latent representation, and standard cross-attention to the input features. Its loss selects minimum-loss and maximum-certainty ticks, making variable internal compute a testable mechanism. The looped computations in § [ftip-00KG] are a separate family; neither is a continuous-time claim, and every extra tick or loop must be charged in the comparison.
Recurrent or compressed-state mechanisms. The Dragon Hatchling / BDH proposal, Section 3.2 defines a tensor-friendly BDH-GPU state-space system whose attention state admits a reduced representation, alongside local graph dynamics. Its open comparison is whether this state interface supports the target tasks and stable credit assignment at a declared budget; the proposal is not a production result.
Recurrent feedback. The [zhang2026rlt, Sections 2-3 and 5-8] splits a causal parallel encoder from a recurrent decoder whose previous-token state is fed back into the next token. On finite parity, permutation-tracking, and modular-arithmetic tasks, its reported length extrapolation depends on the feedback ablation and on chunk size. This is evidence for a state-access intervention in a bounded synthetic setting; it does not establish a frontier reasoning gain or a general recurrence law.
Residual rewrite. [zhang2026deepdelta, Sections 2-4 and 6] replaces an additive residual shortcut with a gated rank-one read/compare/target/write edit. Reported GPT-2 experiments are a finite mechanism observation whose effect remains confounded with residual capacity, compression, and rewrite behavior. A write-only control, capacity-matched control, multiple seeds, and a charged transfer task are needed before attributing a capability change to the rewrite interface.

The recurrent-feedback and residual-rewrite entries are separate intervention arms. Their paired records must expose the changed state or write interface, causal ablations, parameter and token budgets, inference FLOPs, memory, KV/cache or recurrent-state costs, chunking overhead, and monitorability. If the available evidence remains finite-only, the result stays finite-only and does not support ceiling or frontier language.

The adjunct panel changes an objective, temporal law or function-space bias without being interchangeable with the core architecture families. I-JEPA, Section 3 is an encoder/predictor training architecture as well as an embedding-space prediction objective: its context, target and predictor use ViT backbones, the target encoder is updated by exponential moving average, and masked target blocks are predicted from a context block. That predictive objective leaves a specific representation limit: retaining or recovering the latent state does not by itself identify which directions are invariant and which vary across matched observations. SplitJEPA, Sections 3.1--3.4 (the project overview) makes the boundary explicit—predictive recovery can leave a global orthogonal mixing—and resolves it under stationary Gaussian predictive dynamics, a full-rank second moment of variant differences across invariant correspondences, and representation normalization, by identifying invariant and variant subspaces up to within-block isometries without an observation decoder. Here SplitJEPA is a structured JEPA adjunct and a conditional identifiability result, not a general guarantee for I-JEPA. Liquid time-constant networks, Sections 1--2 supply input-dependent continuous dynamics and a numerical ODE-solver interface; the Neural Operator formulation, Sections 1 and 3 supplies a function-space lens with discretization-invariant operator layers. The Zoology study, Sections 3--5 provides controlled recall analyses of sequence mixers. These sources motivate hypotheses and controls; they do not establish a common capability ordering or make an adjunct a replacement architecture. The JEPA comparability interface specifies fields that must be fixed for a JEPA-like comparison; it is not the source of I-JEPA's architecture. Combining CTM's internal dynamics with I-JEPA's encoder/predictor package is a proposed joint intervention, not an established result, and would require separate attribution of both components.

For each panel row, a study should name the changed state or compute interface, the bottleneck dimension, the task and intervention, the evidence type, and the transport boundary. Existing architecture-indexed ceilings and matched compute, published paired measurements (§ [ftip-00KU] and § [ftip-00L4]), conditional frontiers (§ [ftip-00LH]) and study design (§ [ftip-00LS]) provide those controls. Blank, incompatible and unmeasured cells remain findings rather than scores to be filled by intuition.

Remark. Open comparison questions [ftip-00M2]

State access. Sources: Mamba, Sections 3.1--3.3 and linear attention, Sections 3.2--3.3. Estimand: the paired difference in retrieval utility and $K_{\rm state}$. Controls: Transformer, Mamba, and linear-attention systems on a predeclared retrieval and continuation suite at matched parameter, context, recipe, and cost budgets. Evaluation: report cost, recurrence or summary stability, and state observability. Scope boundary: a long-context score does not transfer to another task family without its task and transport record.
Recurrent feedback. Source: [zhang2026rlt, Sections 2-3 and 5-8]. Estimand: the paired change in finite-task utility and $K_{\rm state}$ from decoder feedback. Controls: a common causal encoder, model size, training tokens, seeds, and matched per-token budget, with no-feedback and declared chunk-size arms. Evaluation: report recurrent-state bytes, encoder/decoder FLOPs, chunk latency, numerical/training stability, and feedback monitorability. Scope boundary: parity, permutation tracking, and modular arithmetic remain finite evidence; without a deployment and transfer record, the cell is finite-only.
Residual rewrite. Source: [zhang2026deepdelta, Sections 2-4 and 6]. Estimand: the paired change in utility and rewrite-sensitive state behavior from a gated rank-one read/compare/target/write edit. Controls: residual capacity, optimizer, tokens, seeds, and model family, with no-write, write-only, and capacity-matched arms. Evaluation: report rewrite and residual-state monitorability, inference FLOPs, memory, numerical stability, and a held-out transfer task. Scope boundary: finite GPT-2 observations do not identify a general capability gain or architecture-wide law.
Adaptive computation. Source: Continuous Thought Machine, Sections 3.1--3.5 and the looped-model contract in § [ftip-00KG]. Estimand: the paired change in utility and $K_{\rm adapt}$ per charged internal tick or loop. Controls: common input, base model, recipe, stopping rule, and charged internal compute. Evaluation: report tick/loop cost, training and inference stability, and stopping-signal monitorability. Scope boundary: an observed adaptive gain remains conditional on that stopping law.
Representation and abstraction. Sources: I-JEPA, Section 3 and SplitJEPA, Sections 3.1--3.4. Estimand: subspace identifiability and the conditional change in $K_{\rm repr}$. Controls: fixed correspondences, full-rank variant differences, normalization, encoder budget, and matched downstream probes. Evaluation: report probe cost, optimization stability, and subspace monitorability. Scope boundary: the conditional subspace result is separate from downstream task transfer; failure of a premise is an incompatible cell, not a zero score.
Plasticity. Source: the objective and optimizer interfaces in Definition [ftip-00JM] and the I-JEPA intervention description in § [ftip-00OQ]. Estimand: the paired change in $K_{\rm plast}$ after a declared post-training intervention. Controls: common recipe and equal-tuning arms with data, optimizer, trial count, stopping, and feedback fixed or explicitly varied. Evaluation: report intervention cost, training stability, and update-signal monitorability. Scope boundary: an intervention effect is limited to the declared base artifact, task, and objective; it does not transfer across architectures without a new paired record.
Portability. Sources: the Mamba, BDH, and Neural Operator method descriptions in § [ftip-00OQ]: Mamba, BDH, and Neural Operators. Estimand: survival of the declared utility and $K_{\rm port}$ across one transfer edge. Controls: source model, task family, model scale, deployment regime, evaluation law, and cost vector retained as paired fields. Evaluation: report migration cost, stability after the shift, and failure monitorability. Scope boundary: evidence for one transfer edge does not order unmeasured edges; blank edges remain unmeasured.
Adjunct interactions. Sources: liquid time-constant networks, Sections 1--2, Neural Operators, Section 3, and I-JEPA, Section 3. Estimand: the joint change in the declared capability coordinates after an explicit intervention. Controls: declared core model, objective, temporal law, function-space bias, matched controls, and separate single-intervention arms. Evaluation: report joint and marginal costs, numerical/training stability, and interaction monitorability. Scope boundary: a joint effect is limited to the declared core, task, temporal law, and function-space regime; it does not transfer across regimes without separate intervention records.

Semantic representation diagnostics along pretraining checkpoints [ftip-00OZ]

\[ \ell ^*_{m,c,q}=\arg \max _{\ell \in L_m}s^{\mathrm {val}}_{m,c,q,\ell },\qquad s_{m,c,q}=s^{\mathrm {test}}_{m,c,q,\ell ^*_{m,c,q}}. \]

A predeclared layer may replace the validation choice, but a test score is never used to select $\ell ^*$; this keeps the emergence score from being a layer-selection envelope.

The primary absolute emergence threshold is $\tau =0.50$ in this held-out score, chosen before the trajectory is inspected. The first crossing is

\[ e_{m,q}(\tau )=\min \{c:s_{m,c,q}\geq \tau \}, \]

Convention. Identifiability regimes for semantic representation diagnostics [ftip-00P1]

Finite identifiability record. For a fixed model family, checkpoint, task split, and probe seed, record

Comparison cell	Source	Estimand	Matched controls	Cost / stability / monitorability	Status and scope boundary
Transformer--KDA/full attention	Remark [ftip-00KF], Definition 2, Example [ftip-00LQ]	Difference in utility, cost, and \(K_{\rm state}\) between KDA and full attention	Same task, model scale, recipe, context, seeds, and evaluation law	Report measured cost vector, training/inference stability, and state/failure observability	Open; a finite KDA--Transformer result does not establish an architecture-wide ceiling
Transformer--Mamba	Mamba, Sections 3.1--3.3; § [ftip-00OQ]	Difference in retrieval utility and \(K_{\rm state}\) at the declared budget	Task, scale, recipe, context, and inference budget	Report cost vector, recurrence stability, and state/failure observability	Open; selective compression does not transfer a retrieval result across tasks
Transformer--linear attention	Linear attention, Sections 3.2--3.3; § [ftip-00OQ]	Difference in retrieval utility and \(K_{\rm state}\) for associative summaries	Task family, feature map, context budget, and evaluation law	Report prefix cost, numerical stability, and summary-state observability	Open; associative summaries do not imply full-attention equivalence
Transformer--CTM or looped model	Continuous Thought Machine, Sections 3.1--3.5; § [ftip-00KG]; § [ftip-00OQ]	Difference in utility and \(K_{\rm adapt}\) per charged tick or loop	Input, base model, stopping rule, recipe, and charged internal compute	Report tick/loop cost, training and inference stability, and stopping-signal monitorability	Open; extra passes are charged and do not imply continuous-time behavior
Transformer--BDH	BDH, Section 3.2; § [ftip-00OQ]	Difference in task utility and \(K_{\rm state}\) under the reduced state interface	Task, state parameterization, credit horizon, systems stack, and cost	Report memory and compute cost, optimization stability, and state observability	Open; a reduced state-space proposal is not a production transfer result
Transformer-Recurrent Looped Transformer	§ [ftip-00OQ]; [zhang2026rlt, Sections 2-3 and 5-8]	Paired change in finite-task utility and \(K_{\rm state}\) under recurrent decoder feedback	Same task, model family, parameter count, training tokens, seeds, and feedback interface; causal no-feedback and chunking arms	Report encoder/decoder FLOPs, recurrent state, chunk latency, memory, stability, and state monitorability	Open and finite-only until a declared deployment arm and transfer record exist
Residual baseline: Deep Delta rewrite	§ [ftip-00OQ]; [zhang2026deepdelta, Sections 2-4 and 6]	Paired change in utility and rewrite-sensitive state behavior after the rank-one read/compare/target/write intervention	Matched residual capacity, optimizer, tokens, seeds, and model family; no-write, write-only, and capacity controls	Report inference FLOPs, memory, residual/cache state, numerical stability, and rewrite monitorability	Open; finite GPT-2 evidence does not establish a general capability or frontier claim
Core architecture--JEPA adjunct	I-JEPA, Section 3; § [ftip-00OQ]	Conditional change in \(K_{\rm repr}\) and \(K_{\rm plast}\) after the objective intervention	Same base artifact, target task, predictor budget, data, and optimizer arm	Report intervention cost, training stability, and representation/probe monitorability	Conditional; an objective intervention is not a replacement architecture
I-JEPA--SplitJEPA	SplitJEPA, Sections 3.1--3.4; § [ftip-00OQ]	Subspace identifiability and conditional change in \(K_{\rm repr}\)	Matched invariant correspondences, variant-rank premise, normalization, and encoder budget	Report probe cost, numerical and optimization stability, and subspace monitorability	Conditional; identifiability does not become a general I-JEPA guarantee
Core architecture--liquid dynamics	Liquid time-constant networks, Sections 1--2; § [ftip-00OQ]	Difference in utility and \(K_{\rm adapt}\) under the solver intervention	Input law, solver, step budget, tolerance, task, and cost vector	Report solver cost, numerical stability, and trajectory/failure monitorability	Open; continuous dynamics and solver choices remain intervention coordinates
Core architecture--Neural Operator	Neural Operators, Section 3; § [ftip-00OQ]	Difference in utility and \(K_{\rm repr}\) across discretizations	Function-space task, discretization family, operator layer, and evaluation law	Report training/inference cost, discretization stability, and field-observation monitorability	Open; discretization invariance under one source formulation is not task transfer