analysis-and-evaluation.
138 of 378 NOTE documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| NOTE-004 | Scale Efficiently | Model shape, not only model size, determines downstream fine-tuning quality — and the widely adopted T5-Base and T5-Large configurations are Pareto-inefficient. Read in full for #114 after its LIT note was found to describe a different paper. | Read |
| NOTE-008 | Visualizing the Loss Landscape of Neural Nets | Filter normalization makes loss-surface plots comparable across architectures, and under it sharpness correlates with generalization error. Deep networks transition from nearly convex to chaotic; skip connections prevent that transition, which is why they are needed at depth. | Read |
| NOTE-027 | Simple Open-Vocabulary Object Detection with Vision Transformers | Transfers a contrastively pretrained image-text model to open-vocabulary detection with minimal architectural change. Its transferable finding is about fine-tuning: the text encoder needs a learning rate about 100× lower than the image encoder, freezing it entirely is also wrong, and the recipe that maximises zero-shot transfer is not the one that maximises in-distribution performance. | Read |
| NOTE-051 | Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models | 204 tasks from 450 authors, evaluated across dense and sparse models from millions to hundreds of billions of parameters, with expert human raters as the baseline. Its most useful finding is about measurement: tasks showing “breakthrough” behaviour at a critical scale tend to involve multiple steps or brittle metrics, while smooth ones are knowledge-heavy. | Read |
| NOTE-052 v2 | Progress measures for grokking via mechanistic interpretability | Reverse-engineers a one-layer transformer trained on modular addition and finds it computes a Fourier multiplication algorithm — embed inputs as rotations, combine with trigonometric identities. Uses that to define continuous progress measures showing grokking is three phases, and finds grokking vanishes entirely above about 60% data. | Read |
| NOTE-057 | What Makes for Good Views for Contrastive Learning? | Argues for an InfoMin principle: views should share as little mutual information as possible while keeping task-relevant information intact. Too much shared information makes the contrastive task trivial, too little destroys the signal, so there is a sweet spot — and augmentation design is the search for it. | Read |
| NOTE-060 | Improving Language Models by Retrieving from Trillions of Tokens | Matches GPT-3 on the Pile with 25× fewer parameters by cross-attending to chunks retrieved from a 2-trillion-token database, using a frozen BERT retriever that never needs training. The gain is constant from 150M to 7B, and the model improves at evaluation time by enlarging the database — capability added after training ends. | Read |
| NOTE-061 | Pruned Neural Networks are Surprisingly Modular | Imports modularity from graph clustering — a module is neurons with strong internal and weak external connectivity — and measures it by spectral clustering on MLP weights. Trained and pruned networks are more modular than random ones, and than random networks with the same sparse weight distribution, which is the control that makes the result mean something. | Read |
| NOTE-070 | Understanding Contrastive Learning Requires Incorporating Inductive Biases | Shows that theoretical guarantees for contrastive learning that depend only on the augmentations and the loss value cannot explain why it works, and are provably vacuous in some settings. Different function classes and algorithms behave very differently downstream given identical augmentations and identical contrastive loss. | Read |
| NOTE-076 | Understanding Evolution Strategies for LLM Reasoning | Three questions about what ES post-training does rather than whether it wins: it raises pass@1 and pass@k together where GRPO trades one for the other, its 40x larger parameter drift is functionally sparse and does not produce broad forgetting, and the population size needed for stable training falls as the model grows. | Read |
| NOTE-078 | Neural Thickets | The neighbourhood of a pretrained weight vector is dense with task-improving Gaussian perturbations, the density rises monotonically with model scale, and the perturbations that help are specialists rather than uniform improvements — which together are why a single parallel round of random guessing plus majority voting can match PPO, GRPO and ES at equal training FLOPs. | Read |
| NOTE-083 | Matching Accuracy, Different Geometry | ES and GRPO reach the same accuracy by travelling in nearly orthogonal directions two orders of magnitude apart in length, and land in the same basin with no loss barrier between them — because most of the ES displacement is a loss-invariant random walk whose squared norm grows as sigma^2 d T / N. | Read |
| NOTE-084 | The Blessing of Dimensionality in LLM Fine-tuning | One geometric property — a fine-tuning landscape whose curvature lives in a few stiff directions amid a near-zero bulk — accounts for both why a population of thirty keeps working as models grow and why the training reward rises, peaks and decays under fixed hyperparameters, in GRPO as well as ES. | Read |
| NOTE-090 | Topics in propagation of chaos | When N particles interact only through their empirical distribution (mean- field interaction), the system decouples in the large-N limit: each particle evolves independently according to a nonlinear SDE whose drift depends on its own marginal law, the McKean-Vlasov SDE dX_t = b(X_t, μ_t)dt + σ dW_t where μ_t = Law(X_t). | Read |
| NOTE-091 | The Kuramoto model: A simple paradigm for synchronization phenomena | The Kuramoto model — N oscillators coupled through sin(θ_j − θ_i) interactions scaled by 1/N — undergoes a sharp phase transition at a critical coupling K_c that depends only on the spread of the natural frequency distribution g(ω) at its center. Below K_c the time-averaged order parameter r = |(1/N)Σ e^{iθ_j}| → 0 (incoherence); above K_c a macroscopic fraction of oscillators lock to a common frequency and r > 0 (synchrony). | Read |
| NOTE-092 v2 | Products of indecomposable, aperiodic, stochastic matrices | A finite collection of stochastic matrices all being primitive (indecomposable + aperiodic) guarantees that their products eventually “scramble” — any two rows become increasingly similar — regardless of the order of multiplication, as long as each matrix is used infinitely often. The key mechanism: a single primitive matrix P has P^k → 1v^T (rank-1) for large k. | Read |
| NOTE-093 | Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach | When the width n is large, the n hidden units behave as exchangeable particles whose empirical distribution μ_t evolves by gradient descent in 2-Wasserstein metric on a convex energy functional E[μ] — even though the finite-n loss in parameter space is non-convex. | Read |
| NOTE-094 | Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks | For kernel/NTK regression with eigenvalue decay λ_k ∝ k^{-β}: ε(n) ∝ n^{-α}, where α = β / (1 + β) The exponent α is determined entirely by the data spectrum β, not by architecture beyond the NTK. | Read |
| NOTE-098 | A Mean Field View of the Landscape of Two-Layers Neural Networks | When the number of hidden units N is large (N ≫ D), the population risk of a two-layer net becomes convex as a functional of the distribution ρ of hidden-unit parameters, and SGD corresponds to a Wasserstein gradient flow of this functional. | Read |
| NOTE-101 | Reconciling modern machine learning practice and the bias-variance trade-off | Classical statistics predicts a U-shaped bias-variance tradeoff: more capacity → lower bias but higher variance → optimal capacity somewhere in between. Modern ML practice (overparameterized neural networks, random forests) violates this: after the interpolation threshold, adding more capacity reduces test error again. | Read |
| NOTE-102 | An effective theory of collective deep learning | A competition between local learning dynamics and diffusive inter-network coupling produces a genuine phase transition to collective learning: below a critical coupling strength each network only knows its own private class, while above it the ensemble generalizes to all classes, and this transition is depth-delayed and first-order for deep networks due to the exotic phi^{2D+2} Ginzburg-Landau structure. | Read |
| NOTE-104 | The mean field analysis for the Kuramoto model on graphs I. The mean field equation and transition point formulas | Network topology enters the synchronization threshold only through the spectrum of the kernel operator W defined by the graph limit (graphon). The incoherent (uniform) state of the mean field equation is neutrally stable precisely on K ∈ [K_c^-, K_c^+], where K_c^± = 2/(π g(0) ζ_{max/min}(W)), generalizing Kuramoto’s complete-graph formula (ζ_max = 1) and exposing graph structure as a spectral multiplier on the critical coupling. | Read |
| NOTE-108 | Neural Tangent Kernel: Convergence and Generalization in Neural Networks | At infinite width, gradient descent on neural networks behaves identically to kernel gradient descent with the NTK. The Jacobian (∂f/∂θ) becomes deterministic and constant, so the NTK Θ = J^T J is a fixed PSD matrix. Loss decays as e^{-λ_min(Θ) t}. This is the “lazy training” or “kernel regime”: parameters barely move from initialization, and the network functions as a linear model in parameter space. | Read |
| NOTE-110 | Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup | In the teacher-student two-layer ReLU setup, the high-dimensional weight dynamics compress exactly onto a small set of scalar ODEs. | Read |
| NOTE-113 | The mean field analysis of the Kuramoto model on graphs II. Asymptotic stability of the incoherent state, center manifold reduction, and bifurcations | The network’s structural imprint on synchronization onset is fully captured by the spectrum of the graphon kernel operator W: the critical coupling is 2/(π g(0) μ_max) and the bifurcating partially synchronized state is a scalar multiple of the eigenfunction w_max of the largest eigenvalue μ_max, with √(K-K_c) amplitude (pitchfork). | Read |
| NOTE-114 | A Necessary and Sufficient Condition for Consensus Over Random Networks | For gossip-style algorithms with IID random mixing matrices, almost sure consensus is governed entirely by the EXPECTED topology, not by any individual realization. As long as the expected weight matrix E[W] is “connected enough” (directed spanning tree in its graph, unique unit eigenvalue), consensus happens almost surely even when individual rounds may fail to connect all nodes. | Read |
| NOTE-118 | The Variational Formulation of the Fokker-Planck Equation | Langevin dynamics — and hence SGLD — is not merely a diffusion process but gradient descent on the free energy functional F(ρ) = KL(ρ ‖ π) in the infinite-dimensional Riemannian manifold of probability measures equipped with the W₂ metric. The unique stationary point is the Gibbs measure π ∝ exp(−V), which minimizes F. | Read |
| NOTE-121 | On-line learning in soft committee machines | The full weight dynamics compress onto two scalar order parameters per student- teacher neuron pair. The plateau phase — where R is nearly static despite ongoing gradient descent — arises because all students point in nearly the same direction, making the two-layer network effectively linear. Only once perturbations grow does the specialization phase begin and R accelerates toward 1. | Read |
| NOTE-126 | Consensus Problems in Networks of Agents With Switching Topology and Time-Delays | The consensus problem is exactly a question about the null space of the graph Laplacian. For first-order dynamics ẋ = -Lx (L = graph Laplacian), the system converges to the subspace spanned by the all-ones vector (consensus) iff L has exactly one zero eigenvalue — iff the graph is connected. The second-smallest eigenvalue λ_2(L) (algebraic connectivity, Fiedler value) governs the convergence rate: D_x(t) ≤ D_x(0)·e^{-λ_2·t}. | Read |
| NOTE-134 | Consensus Based Sampling | By replacing the empirical covariance in a CBO-like consensus update with a Gibbs-weighted covariance and scaling the noise by a parameter λ, the same interacting particle dynamics can converge to a Gaussian approximation of the posterior (λ = 1/(1+β), sampling mode) or collapse to the MAP estimator (λ = 1, optimization mode), while preserving affine invariance. | Read |
| NOTE-136 v2 | Coordination of groups of mobile autonomous agents using nearest neighbor rules | Even if a network is disconnected at every individual time step — no single round of communication reaches everyone — the agents can still reach consensus as long as the union of communication graphs over any fixed- length sliding window has a spanning tree. | Read |
| NOTE-138 | Convex Analysis of the Mean Field Langevin Dynamics | The proximal Gibbs distribution p_q(θ) ∝ exp(-(1/λ) δF/δq (q)(θ)) plays the role of a “gradient” and “proximal point” in the space of measures: KL(q || p_q) simultaneously serves as a Polyak-Łojasiewicz-style upper bound on the optimality gap L(q) - L(q*) and, in ERM settings, equals exactly the duality gap. | Read |
| NOTE-139 | On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport | Over-parameterized particle gradient descent on F_m(u_1,…,u_m) can be viewed as a discretization of a Wasserstein gradient flow on measures. Although the non-convex landscape has many local minima, homogeneity of Phi and V plus a “separation” property of the initialization support — preserved along the flow — force any limit point to be a global minimizer. | Read |
| NOTE-140 | Mean Field Analysis of Neural Networks: A Law of Large Numbers | With 1/N scaling of the output layer and a time rescaling t = k/N (so that one unit of continuous time corresponds to N SGD steps), the N hidden units behave like an interacting particle system whose empirical measure μ^N_t converges to a deterministic measure-valued process μ̄_t satisfying a McKean-Vlasov / Fokker-Planck PDE. | Read |
| NOTE-143 | Exact Solution for On-Line Learning in Multilayer Neural Networks | The closed-form solution is: R(t) = R_∞ - (R_∞ - R_0) exp(-t / τ_sp) with specialization timescale τ_sp ∝ 1/η. Generalization error follows: ε_gen(t) ∝ (1 - R(t))² = C_∞ + A · exp(-t/τ_sp) + B · exp(-2t/τ_sp) This is a SUM OF EXPONENTIALS in ε, not a single exponential, because of the square. | Read |
| NOTE-146 | Phase transitions, logarithmic Sobolev inequalities, and uniform-in-time propagation of chaos for weakly interacting diffusions | Both the N-particle Fokker-Planck equation and its McKean-Vlasov mean-field limit are 2-Wasserstein gradient flows of related free energies (E^N and E^MF), and E^N Gamma-converges to E^MF. | Read |
| NOTE-147 | Emergent Behavior in Flocks | The CS model places the “consensus” dynamics on VELOCITIES rather than positions. All particles converge to the same velocity (heading), but their positions may continue to diverge — they flock together, traveling as a group. | Read |
| NOTE-149 | Emergence of stochastic flocking for the discrete Cucker-Smale model with randomly switching topologies | The Cucker-Smale velocity update is a stochastic matrix multiplication on velocities: V[t+1] = (Id - (h/N)·L_{σ[t]}[t]) · V[t], where each factor is a row-stochastic matrix with ergodicity coefficient determined by the communication weight φ and position diameter. Flocking = the velocity diameter D(V[t]) → 0 a.s. | Read |
| NOTE-158 | Broken Neural Scaling Laws | A single power law ε(t) = C_∞ + b·t^{-c} fails when the curve has two regimes (steep early, shallow late — or vice versa). | Read |
| NOTE-165 | Large Language Models Cannot Self-Correct Reasoning Yet | Asked to revise its own reasoning with no external feedback, a model does not reliably improve and sometimes degrades. The durable part is the definition rather than the number: INTRINSIC self-correction, meaning no oracle, no verifier, no second model — a line that a lot of contemporary self-correction claims were quietly on the other side of. | Read |
| NOTE-167 | The Surprising Effectiveness of Test-Time Training for Few-Shot Learning | Turn the in-context examples into a loss and take gradient steps on them at inference, then discard the update. Up to 6x a fine-tuned baseline on ARC and +7.3 points on BIG-Bench Hard at 10-shot. The finding underneath is about in-context learning: the same examples are worth several times more as gradient than as context. | Read |
| NOTE-174 | Evaluating Large Language Models Trained on Code | Read for the metric rather than the benchmark. The paper defines pass@k and the unbiased estimator 1 - C(n-c,k)/C(n,k), and argues explicitly why the naive repeated-trials measurement is biased downward — which is the convention SOTA-210 already recommends while calling it “the standard one” and citing nobody. | Read |
| NOTE-178 | Studying Large Language Model Generalization with Influence Functions | Scales influence functions to 52B-parameter language models with an EK-FAC approximation to the IHVP and query batching over the gradient bottleneck, then uses them as an instrument. Generalization gets more abstract with scale; influence is heavy-tailed but not concentrated; and a training sequence only counts when the prompt-related phrase comes first. | Read |
| NOTE-179 | If Influence Functions are the Answer, Then What is the Question? | Decomposes the influence-function / leave-one-out gap into five terms and finds that the three largest are not approximation errors but a change of question. Influence estimates on neural networks closely track the proximal Bregman response function, which supports the same use cases — so the method is not fragile, it was mislabelled. | Read |
| NOTE-180 | Estimating Training Data Influence by Tracing Gradient Descent | Defines a training example’s influence as the total change it caused in a test point’s loss over the iterations where it was used, and approximates that by step-size-weighted gradient dot products at saved checkpoints. No Hessian, no convexity. Self-influence ranks mislabelled examples to the top. | Read |
| NOTE-181 | Understanding Black-box Predictions via Influence Functions | Traces a prediction back to the training points responsible for it by upweighting each point infinitesimally and differentiating, which needs only gradients and Hessian-vector products rather than retraining. Establishes the four uses of training-data attribution — explanation, poisoning, domain-mismatch debugging, and finding mislabelled data. | Read |
| NOTE-183 | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric | Collect human similarity judgements first, then ask which metric agrees with them. Deep features beat every hand-designed metric by a wide margin, and the surprise is the insensitivity — architecture barely matters and supervision barely matters, but training on something matters a great deal, because a randomly initialized network does not work. Perceptual similarity looks like an emergent property of learned visual representations rather than of any particular training signal. | Read |
| NOTE-190 | How much do language models memorize? | Defines memorization as compression rate against the model, separates it from generalization by training on uniform random bitstrings, and measures GPT-style transformer capacity at ~3.6 bits per parameter, linear in parameter count and nearly indifferent to precision. Double descent begins exactly where the data’s information content crosses that capacity. | Read |
| NOTE-200 | Scaling Laws for Fact Memorization | Fact capacity is linear in model size and saturates in epochs along a negative exponential; all of Wikidata would want ~1000B non-embedding parameters at 100 epochs. Redundant facts cost full price unless they share direction and structure. Generalisation to unseen facts exists and scales like ordinary pretraining. | Read |
| NOTE-204 | Hidden Breakthroughs in Language Model Training | A smooth loss curve is what many differently-timed abrupt transitions look like when averaged. POLCA decomposes loss change per example and along a curvature-derived low-rank basis; on synthetic arithmetic it recovers the carrying skill, which clustering the exact loss curves does not. | Read |
| NOTE-206 | The Platonic Representation Hypothesis | Vision and language models measure distance between datapoints more alike as they get larger, across architectures and objectives. The conjecture is that they converge on a model of what generated the data. The proof holds for bijective observations only, and the caption-density experiment tests that boundary rather than asserting past it. | Read |
| NOTE-212 | The Illusion of Thinking | Controllable puzzles replace contaminated benchmarks; reasoning models collapse to zero past a threshold and reduce thinking tokens as they do. Reading it: the three-regime result is solid, and the collapse reading is contested on scoring grounds the paper does not address. | Read |
| NOTE-213 | Comment on the Illusion of Thinking | Argues the reported collapse is evaluation design. Reading it: the unsolvable-instance claim is a checkable fact and lands; the rest is a reinterpretation of a measurement the two papers agree on, from a preprint that is underpowered and has already been corrected once. | Read |
| NOTE-214 | Limitations of multi-layer Transformers | First unconditional lower bound for a decoder-only transformer of more than one layer. Reading it: the bound comes from causal masking rather than from circuit complexity, and it buys an exponential depth-width tradeoff, an encoder/decoder separation, and a provable benefit of chain of thought. | Read |
| NOTE-215 | Auxiliary Task Demands | Four capacities, each measured two ways; the higher-demand way scores lower and the gap shrinks with model size and training time. Reading it: the interaction is what matters, because it means cross-scale comparisons under a demanding evaluation overstate the difference between models. | Read |
| NOTE-219 | Emergent Abilities | The paper that named the phenomenon, read for what it actually argues rather than what it is cited for. It raises the metric explanation itself, in §5.1, and declines it on two grounds — one of which was later answered and one of which still stands. | Read |
| NOTE-220 | Emergence as a Mirage | Reading it: the strongest evidence is not the rebuttal of GPT-3’s arithmetic curve but the vision experiments, where emergence is manufactured in models that had never shown it. The weakest part is what “mirage” gets read as — the paper explicitly declines the claim that emergence is impossible. | Read |
| NOTE-223 | The Coverage Principle | Reading it: the strongest thing here is not the theorem but the Bernoulli example, which shows KL going to infinity while coverage stays fine on a two-outcome model. That is the whole argument in four lines, and it explains why a metric everybody watches can be anti-correlated with what they want. | Read |
| NOTE-225 | Local Volume | Reading it: the useful result is the audit, not the hypothesis. A network trained to generalize badly is detected on clean held-out data where its behaviour matches a clean model’s, in a small number of forward passes. The volume hypothesis itself is left where it was found — “broadly consistent”, by the authors’ own conclusion. | Read |
| NOTE-229 | Auditing Political Exposure Bias | Reading it to find out whether deployment-and-society earns its place: it does, and not because of the finding. The methods section is a sequence of stated trades about measuring a system you cannot inspect, and the sharpest result is that the arm following nobody gets the most diverse timeline. | Read |
| NOTE-230 | Informative and Actionable Social Media Research | Reading it: the power-grid example is the whole paper and it is worth the space. Treat too few substations and the grid absorbs it; treat enough and the controls black out too. Both give “no effect”, both are wrong, and nothing about that argument is specific to social media. | Read |
| NOTE-231 | Industry Influence in Social Media Research | Reading it: the number that matters is not 49% of papers but 21% of authors. A field where half the high-profile output has industry ties held by a fifth of its people is not a field engaging broadly with industry — it is a small group with durable relationships, and that is a different diagnosis. | Read |
| NOTE-233 | TinyStories | Read as the missing trunk under eight tiny-models practices. The result that matters is a separation, not a record: what stops a 125M model speaking coherent English is the breadth of the corpus, not the complexity of language — established by holding the architecture and shrinking the corpus. | Read |
| NOTE-234 | SimpleStories | Read to see what, if anything, it establishes that its trunk did not. The dataset comparison is direct and well instrumented; the model comparison is confounded three ways and the authors say so. The practice this record takes from it is about the generation procedure, which was measured. | Read |
| NOTE-235 | ParScale | Read as a candidate third scaling axis. The scaling law is fitted honestly and the inference-cost analysis is better instrumented than most; the claim that carries furthest is the asymmetry — code benefits more than general text, on two independent measurements — and it is the claim the paper labels a conjecture. | Read |
| NOTE-236 | AI on Change.org | Read to test it against SOTA-291’s promotion condition, which it satisfies. The platform-level difference-in-differences is the strong evidence; the repeat-writer analysis reads stronger than it is; and the finding with the longest reach — that the text features stopped predicting outcomes — is in the appendix. | Read |
| NOTE-238 | PHi: prediction of hidden states | Read for the instrument. The diagnosis of next-token loss is clean and the boring/interesting separation is convincing on two architectures. The reasoning-selection result is weaker than the abstract implies, and the paper says so itself in a sentence most readers will pass over: picking by next-token loss alone already scores 71%. | Read |
| NOTE-239 | Denoising-corrected gradient guidance | Read for a practice the record’s diffusion line was missing. The ordering change is well motivated and cheaply adopted; the theory is proved under idealized assumptions the authors name; and the useful experimental result is that the standard method was optimizing the plan rather than the executed outcome. | Read |
| NOTE-241 | LLMs raise the opportunity cost of research time | Read for a mechanism THEORY-051 named and the record could not supply: why effort would fall when a writing tool gets better. The answer is that a perfect time-saving tool raises the value of time, and the interesting part is not the decline but the phase dependence — the model predicts opposite signs for selectivity, and the opposite sign for thoroughness, from properties of the tool you can identify before deploying it. | Read |
| NOTE-242 | Genesys and what survives its own controls | Read from the #180 worklist for the architecture-discovery claim and filed for the code-generation one. Unit-by-unit generation with an execution-based checker takes valid output from 6% to 92% on the same proposals, with a stated mechanism that has nothing to do with architectures. The discovered architectures are a wash against their own seeds, by the paper’s own table. | Read |
| NOTE-244 | SQ hardness for semiautomata, and the regime it needs | Read from the #180 worklist. The theorem is clean and the regime it needs — an alphabet cubic in the number of states — is what a citing reader will drop. Filed for the mental model underneath it, which the record had nothing for: longer random sequences carry less information about a hidden transition structure, not more. | Read |
| NOTE-248 | The FID Lottery: which lottery, and how big | Read from the #180 worklist, taken ahead of its dwell rank because SOTA-305 had just landed on the adjacent question. The finding that matters is not that FID is noisy but which noise dominates: the training run, by 3.2×, in a term no amount of extra sampling touches — and a control that rules out the boring explanation. | Read |
| NOTE-249 | MemGraphRAG: the pilot study is the contribution | Read from the #180 worklist in rank order. The system is a multi-agent GraphRAG pipeline with single-run numbers; the durable part is §3, which measures other people’s systems and finds graph expansion trading relevance for recall at a net loss on the end task. That is the filing. | Read |
| NOTE-250 | MIMI: the objective that needs the knowledge it assumes away | Read from the #180 worklist in rank order. The headline is a task-agnostic interface score; the durable finding is its failure case, where an assistant doing its job drives the metric negative and the fix is a horizon hyperparameter that requires exactly the task knowledge the objective was built to avoid. | Read |
| NOTE-251 | Non-identifiability of mechanistic explanations | Read from the #180 worklist, the strongest of the remaining candidates. Asks whether mechanistic interpretability’s criteria pick out a unique explanation and answers no at all four stages by exhaustive enumeration — no network had exactly one circuit interpretation — with both counts lower bounds by construction. | Read |
| NOTE-252 | RNA-FM, and the control that does not carry the claim | Read to open the biological-sequence trunk, which this record did not hold. The structural results are large and the paper’s one clean input-swap control is not: replacing one-hot sequence with a 640-dimensional pretrained embedding in a published UTR model moves R² from 0.814 to 0.816 on the real-human set, where a 16-dimensional predicted secondary structure moves it to 0.820. No practice filed. | Read |
| NOTE-253 v2 | The information-bottleneck rebuttal, and the assumption it makes visible | Read from the ICLR 2018 version, supplied by the record’s owner; co-author Kolchinsky’s publications page also links it. Three claims tested, none general. The finding worth more than the headline is in Appendix C: the same tanh network, binned evenly in net input instead of evenly in activity, shows no compression — because I(h;X) in a deterministic network is infinite and every finite number is a property of an imposed noise model. | Read |
| NOTE-254 | Adaptive estimators: a counter-rebuttal whose body is weaker than its abstract | Read to check whether it softens LIT-509, as #220 feared. It does on one narrow point and not on the one that matters. Its own Figure 6 shows a ReLU network averaged over 50 initializations has no distinct phase, though its Figure 7 averages do show ELU, Swish and centered softplus compressing; and it agrees with the rebuttal that hidden-layer compression does not correlate with generalization. | Read |
| NOTE-255 | Gemini 1.0: complete disclosure, and an abstract that picks the flattering row | Read from the #180 worklist for the model and filed for the appendices. The headline — first model to exceed human-expert MMLU — holds under one inference procedure out of three the report itself publishes, and under the other two GPT-4 is ahead. Nothing is concealed. The disambiguating numbers are in the same document, which is what makes this the corpus’s cleanest instance of DP-010. | Read |
| NOTE-259 | Refine, then calibrate: the validation-loss minimum is a compromise nobody chose | Read from the #180 worklist and filed because the record had no calibration document at all. A proper loss is calibration error plus refinement error; the two bottom out at different epochs, so stopping on validation loss lands between them. Fit a temperature before reading the loss and the compromise goes away. 196 tabular datasets, three model families, ten runs per vision dataset. | Read |
| NOTE-261 | The singular distribution stops moving long before the weights do | Read for one question: is the spectral shape that three other papers measure a stable property or a snapshot? It is stable, and early. The empirical section was read and the theory that derives the stability bound was not, which is stated rather than implied. | Read |
| NOTE-262 | Eleven checkpoints agree on where the spectrum concentrates, and imitating it does not help | Read for breadth rather than depth of claim. Eleven GPT-2-style checkpoints differing in size, language, tokenizer and corpus share a depth profile in effective-rank entropy, which is the right instrument for a claim about where a matrix’s magnitude sits. The paper’s own headline — that you can initialize from that profile — is a negative result, reported as one. | Read |
| NOTE-263 | An MP bulk plus outliers at both ends, and why magnitude order is not importance order | Read to answer THEORY-059’s open question. It answers it and complicates it: language-transformer weight spectra are steep at the top, as the account needs, but the bottom of the spectrum in non-square matrices holds directions whose removal is catastrophic. Frobenius magnitude and functional importance are ordered differently, and a method that peels the top and quantizes the rest is keeping exactly the part this paper says is fragile. | Read |
| NOTE-268 | A data-free lineage check built on the products that survive permutation and scaling | Read because the grep came back empty: the record holds no document about model provenance. The method is one good idea executed cleanly — compare the spectra of matrix products that functionality-preserving transforms cannot change — and the honest limit is that it is evidence of derivation, not proof against an adversary who has read the paper. | Read |
| NOTE-270 | Concepts live in the quiet directions, and the paper labels its own claims robust or partial | Read last of the batch and the easiest call to make, for a reason that has little to do with the finding: it arrives with a table grading its own six claims, a robust null against the hypothesis it set out to confirm, and partial results labelled partial. The finding itself is that contextual concept directions sit at the bottom of the unembedding spectrum and static vocabulary contrasts sit at the top. | Read |
| NOTE-271 | One metric that separates transformers from every other architecture, and what it buys | Read for whether the record wants an inductive-bias claim. It does, because the claim comes with a computable scalar rather than an adjective, and the scalar does four jobs — separates architectures, predicts robustness, correlates with flatness, and moves during a grokking plateau. The fourth is the one this record has an existing use for. | Read |
| NOTE-272 | An idealization that checks its own assumptions against ALBERT, and clusters within a few layers | Read a second time, in full, after being declined on a skim. The decline said the t → ∞ frozen-weights idealization put it too far from a trained model to be evidence about one, and that it underwrites no practice. Both halves were wrong: the record’s own schema says a theory need underwrite nothing, and the paper checks its hypotheses on ALBERT, gives the discrete-time analogue, and says clustering arrives within a few layers. | Read |
| NOTE-275 | The trunk states the condition its successors were refuted for dropping | Read because the record filed a three-paper information-bottleneck dispute and the paper all three argue about was absent — a gap this session created. Reading it reassigns some blame: the rebuttal’s central objection is that mutual information needs stochasticity, and the founding paper says so in one sentence before anybody measured anything. | Read |
| NOTE-276 | Order-sensitivity refutes it, and the models everyone measured were trained for the task | Read as the challenge from outside the setup. It separates the hypothesis that pretrained models do in-context learning by gradient descent from the hypothesis that transformer weights exist which simulate it, and shows the literature had been testing only the second. Theorem 1 then gives a contradiction from order sensitivity alone, and LLaMA-7B disagrees with fine-tuning on three metrics across four datasets. | Read |
| NOTE-277 | The algorithm changes with depth, and the one it lands on is Bayes-optimal ridge | Read as the second, independent arrival at the mesa-optimization construction. Its title asks which algorithm, and its answer is that there is no single one: shallow learners sit nearest gradient descent, deeper ones nearest ridge, deepest nearest ordinary least squares, and under label noise the best-fitting ridge parameter is the Bayes-optimal σ²/τ² at every setting. The gradient-descent phase is the small-model phase. | Read |
| NOTE-278 | Linear in Newton steps, exponential in gradient steps: a rate is what separates the rivals | Read as the challenge from inside the gradient-descent line’s own setup. Matching each transformer layer to the best number of steps of a candidate algorithm gives a linear trend against Iterative Newton — about 3 iterations per middle layer — and an exponential one against gradient descent; on data with condition number 100 the transformer is unchanged while GD needs 2,000 steps a 12-layer model cannot hold. The method is the contribution: fits cannot separate these rivals and rates can. | Read |
| NOTE-279 | The setup that made the mechanism question answerable | Read because every later claim about what in-context learning is measures in this setup. A 9.5M-parameter GPT-2 trained from scratch on (x, f(x)) sequences matches ordinary least squares on unseen linear functions and Lasso on sparse ones, in a single forward pass. The training objective is the setup’s power and, later, the ground of the main objection to everything built on it. | Read |
| NOTE-280 | The construction is exact, and the trained model it describes is one layer deep | Read as the trunk of the mesa-optimization line. Proposition 1’s weight construction is exact and a single trained linear-self-attention layer approximately finds it. The result that matters most for what came after is four pages later and rarely quoted: deeper trained models do not match gradient descent, they match GD++ — gradient descent on data transformed by I − γXXᵀ — which is the paper’s own evidence that plain GD is the wrong description beyond one layer. | Read |
| NOTE-281 | The founding paper already measured the regime dependence | Read because the record staked a claim on grokking being a data-starved-regime phenomenon while holding only the paper that put a number on it. The founding paper measured the dependence too, and reported it as a headline: converged accuracy is flat across a range of training-set sizes while time-to-generalize explodes as the set shrinks. It also names weight decay as the strongest intervention it found, which is the thread every later mechanism pulls on. | Read |
| NOTE-282 | Induced on images, text and molecules, and eliminated on demand | Read to settle whether grokking is specific to algorithmic data. It is not — but producing it on MNIST, IMDb and QM9 takes two deliberate departures from standard practice: a much smaller training set and an inflated initialization scale. At standard initialization there is no grokking on any of them. The control runs both ways: constraining the weight norm nearly eliminates grokking on algorithmic data. | Read |
| NOTE-283 | Two novel phenomena predicted from the theory and then observed | Read as the mechanistic account with the best evidential shape in this cluster. From “weight decay prefers the circuit with more logit per unit norm, and memorisation gets less efficient as the dataset grows” it derives a critical dataset size, and from that derives ungrokking — a grokked network losing its generalization when trained on less data — and semi-grokking. Both were then observed. The mechanism needs weight decay, which is where the counterexample lands. | Read |
| NOTE-284 | A counterexample with no weight decay and a rising weight norm | Read because it is the reason this cluster cannot be filed as a single account. A two-layer MLP groks on modular arithmetic with no weight decay and a parameter norm that goes up through the transition — which neither the LU mechanism nor circuit efficiency can produce, since both turn on the norm coming down. The replacement offered is a transition out of lazy dynamics, with two measurable knobs; what it does not address is ungrokking. | Read |
| NOTE-285 | A complexity measure that discriminates where the training loss is identically zero | Read as the instrument that makes the school usable. The LLC is the volume scaling exponent of the near-optimal set around one minimum, estimated by SGLD at inverse temperature 1/log n. Validated against known theoretical values on deep linear networks to 100M parameters. The result that makes it a practice: on ResNet18/CIFAR10 it separates learning rate, batch size and momentum settings whose training losses have all collapsed to zero. | Read |
| NOTE-286 | The effective parameter count is a volume exponent, and the Hessian is measuring the wrong thing | Read as the trunk under a school the record was already citing. Its single most consequential sentence for this record is that the RLCT matters more than the curvature of the directions it counts — a published argument that the quantity a sharpness visualisation displays is not the quantity that governs generalisation, which is what SOTA-012 rests on. | Read |
| NOTE-287 | Stage boundaries found without knowing in advance what to look for | Read as the application that makes the LLC matter to this record. Critical points of the LLC curve divide two transformers into five stages each, and the boundaries land on bigrams, n-grams, previous-token heads and the induction circuit — found without a mechanistic hypothesis in hand, which is what LIT-085’s progress measures require. Its in-context regression model acquires in-context learning and then loses it. | Read |
| NOTE-288 | The transition the posterior makes and the transition SGD makes are not the same transition | Read for the distinction in its title, which the record needed and did not have. Bayesian phase transitions are changes in where the posterior concentrates as sample size grows, and are well defined; dynamical transitions are changes in the SGD trajectory, and are not. In the one model where both are computed, the sharpest Bayesian transition — predicted at n_cr ≈ 600 and observed at 600–700 — has no dynamical counterpart at all. | Read |
| NOTE-290 | ReFT | A learned low-rank edit to the residual stream at a few prompt positions, with the model frozen. Up to 65× fewer parameters than LoRA. It leads on commonsense QA and on GPT-4-judged instruction following, is level on GLUE, and loses on arithmetic chain of thought. All baselines are copied from earlier papers. Read §1–6 and Appendix D; the other appendices were not read. | Read |
| NOTE-292 | Tensor Programs IV | Classifies width-scaling parametrizations (abc-parametrizations) and proves that every stable, nontrivial one either learns features in the wide limit or trains as a kernel, not both. The standard parametrization is stable only at an O(1/width) learning rate, which is the kernel side. Introduces µP as the unique member that updates every layer maximally. Read end to end through §10 and Appendices A, C.1 and D. The proofs in Appendix H were not checked line by line. | Read |
| NOTE-296 | Fourier features | A coordinate MLP’s NTK has fast spectral falloff, so it fits high frequencies too slowly to matter. A sinusoidal input mapping makes the composed kernel stationary and sets its bandwidth, and the NTK linear model predicts the trained networks’ loss curves. Gaussian random frequencies are best on seven tasks, and only their scale matters. Read §1–7; the appendices were not read. | Read |
| NOTE-297 | Warm Diffusion | Blur and noise mixed in one forward process, with a two-headed model that denoises to the blurry image and deblurs the residual. A blur-to-noise ratio of 0.5, chosen from image and noise spectra, edges EDM on CIFAR-10 (1.85 against 1.97). High ratios degrade sharply, which is the evidence that noise keeps samples on the manifold. Read in full (8 pages). | Read |
| NOTE-299 | Cold Diffusion | Diffusion generalized to deterministic degradations (blur, masking, downsampling, snow) with a restorer and a sampler that is exact for degradations linear in severity. Conditional restoration works. Noiseless unconditional generation is far worse than noise diffusion, and adding a little noise recovers half the gap. Read §1–5.3; the appendix was not read. | Read |
| NOTE-304 | The Role of ImageNet Classes in FID | FID can be lowered by two-thirds without changing a generator, by resampling its outputs to match ImageNet-class statistics. Non-ImageNet feature spaces barely move. An ImageNet-pretrained discriminator gets the same effect by accident. Read §1–5; the appendices were skimmed. | Read |
| NOTE-307 | Scaling and evaluating sparse autoencoders | TopK SAEs, a dead-latent recipe, and scaling laws, demonstrated on a 16M-latent SAE on GPT-4. Also shows that the field’s usual fidelity metric flatters. Sections 1–4 and 6 read. Section 5 (TopK variants, progressive codes) and the appendices skimmed. | Read |
| NOTE-308 | Tuned Lens | The logit lens decodes intermediate layers with the final unembedding, and fails or misleads on many models. A per-layer affine translator, distilled toward the final logits, fixes this across every family tested. Sections 1–5 read, appendices skimmed. | Read |
| NOTE-309 | Are Sparse Autoencoders Useful? A Case Study in Sparse Probing | On 113 datasets and five regimes, probes on SAE latents do not beat, or usefully add to, probes on raw activations once method selection is honest. Earlier wins, including the authors’ own, came from weaker baselines. Main text read, appendices skimmed. | Read |
| NOTE-311 | FLUX.2 latent-space report | A controlled comparison of four autoencoder latents for flow models on ImageNet, with the timestep schedule swept per latent. The schedule decides the ranking. FLUX.2’s VAE gets near-RAE learnability with the best reconstruction. Read in full from the rendered report, except the interactive Table 2, which did not load. | Read |
| NOTE-312 | FFN layers as key-value memories | Keys fire on input patterns, and values project to next-token preferences. The reading is interpretable in upper layers of one small WikiText model, with weak key-value agreement and mostly compositional outputs. | Read |
| NOTE-313 | Does Localization Inform Editing? | Per fact, causal tracing’s localization is unrelated to where an edit succeeds. The edit layer explains 94.7% of the variance in ROME’s success on GPT-J, and tracing adds 0.1%. Main text read, robustness appendices skimmed. | Read |
| NOTE-314 | RippleEdits | Knowledge-editing benchmarks test the edited fact and leave its consequences untested. On six ripple criteria, weight editors average 38–66, and prompting with the new fact beats them. Main text read. | Read |
| NOTE-315 | ROME | Causal tracing localizes factual recall to mid-layer MLPs at the last subject token, and a rank-one MLP edit there rewrites single facts with both generalization and specificity. Main text and the CounterFact appendix read. | Read |
| NOTE-316 | MEMIT | ROME’s idea extended to thousands of facts by spreading updates across a range of MLP layers in one batched solve. 85.8 on 10,000 CounterFact edits on GPT-J, where ROME falls to 50.3. Main text read, appendices skimmed. | Read |
| NOTE-317 | Where did the gap go? | Retuned message-passing baselines close the reported graph-transformer gap on LRGB’s Peptides datasets, mostly by adding an MLP prediction head. Feature normalization lifts every model on the superpixel datasets. Read in full. | Read |
| NOTE-318 | Pitfalls of GNN evaluation | With one shared training and tuning protocol over 100 splits × 20 seeds, GCN ranks first among four GNNs, and the single Planetoid split reorders models. Read in full. | Read |
| NOTE-319 | GIN | Message passing is bounded by 1-WL, and sum aggregation with an MLP reaches the bound where mean and max do not. The theory predicts the training-fit ordering. Test accuracy separates the aggregators decisively only on featureless graphs. Main text read, proofs skimmed. | Read |
| NOTE-323 | Reading Tea Leaves | Two human tasks, word intrusion and topic intrusion, measure whether a topic model’s topics and document assignments mean anything to people. The model with the best held-out likelihood, CTM, does worst on both. Read in full. | Read |
| NOTE-324 | Automatic Evaluation of Topic Coherence | Average pairwise PMI of a topic’s top words, estimated on Wikipedia, correlates with human coherence ratings about as well as the paper’s measure of annotator agreement. Knowledge-base and search-engine measures are less reliable. Read in full. | Read |
| NOTE-325 | word2vec (architectures) | CBOW and skip-gram, log-linear models that learn word vectors from billions of words cheaply, and the analogy benchmark that made “king − man + woman ≈ queen” a test. Read in full. | Read |
| NOTE-328 | Linear Representation Hypothesis (Park, Choe, Veitch) | Formalizes “concepts are directions” with counterfactual word pairs, proves that the unembedding direction is a probe and the embedding direction a steering vector, and shows the two coincide under a causal inner product. One such product is the inverse unembedding covariance. The experiments, on LLaMA-2 7B, are qualitative. Read in full, appendices included. | Read |
| NOTE-329 | Levy, Goldberg & Dagan: lessons from word embeddings | The prediction-over-count advantage for word embeddings came from design choices bundled with word2vec, not from the algorithm. Ported to PPMI and SVD and tuned alike across 672 representations, no method wins consistently, SGNS beats GloVe, and context-distribution smoothing is the only universally safe setting. Read in full. | Read |
| NOTE-330 | Baroni, Dinu & Kruszewski: Don’t count, predict! | 84 count and predict models on 14 lexical-semantics benchmarks from one 2.8B-token corpus. word2vec CBOW beats count vectors almost everywhere and is far more robust to bad settings. The count side lacked word2vec’s smoothing and negative shift, which is how LIT-607 later reversed the result. Read in full. | Read |
| NOTE-356 | Rigor with Machine Learning from Field Theory to the Poincaré Conjecture | Stochastic, black-box ML can still yield rigorous results in mathematics and physics along two routes. The first is conjecture generation followed by human proof, or RL searches whose outputs can be checked exactly; this is how an RL/Bayesian-optimized ribbon verifier ruled out over 800 candidate counterexamples to the smooth 4D Poincaré conjecture. The second is importing ML theory, such as NNGP/NN-field-theory and gradient-descent metric flows that include Perelman’s Ricci flow. | Skimmed |
| NOTE-358 | Diffusion Models Beat GANs | An ablated U-Net (ADM) and a classifier-gradient scale that trades recall for precision take diffusion past BigGAN-deep on ImageNet FID. The architecture search is single-run; every guided number is the best of a small scale sweep scored on the metric it reports; and the trade is non-monotone — a scale of 1 makes an unconditional model’s FID worse (26.21 → 33.03). The claim that large scales produce no adversarial examples is asserted in the introduction and checked only by eye and in Inception feature space. | Read |
| NOTE-359 | Non-Euclidean Edge of Stability | Steepest descent under the ℓ∞, block and spectral norms, and SignGD and normalized Spectral GD, sharpen to 2/η and train there — measured in the update’s own norm, where the ordinary top Hessian eigenvalue stays far below the threshold. Reading it: the identity is exact and the empirics are consistent, but the sharpness is a Frank-Wolfe estimate, the divergence theorem covers one line of initializations, and everything is full-batch. | Read |
| NOTE-362 | Integrated Gradients | Integrating the gradient along the straight line from a baseline gives the unique path attribution that is implementation-invariant, complete and symmetry-preserving. The justification is axioms, not a measurement: the paper argues that empirical evaluation of attributions is confounded, and its one comparison with another method is by eye. | Read |
| NOTE-364 | Dropout and BN variance shift | Dropout upstream of batch norm changes a unit’s variance between train and test mode, and BN’s frozen moving variance does not follow. The measured cost is large: CIFAR-100 DenseNet 77.42% → 68.55%, and the model misclassifies its own training data in eval mode. It shrinks with a lower drop rate and with a wider fan-in. Moving dropout after the last BN avoids the cost, but adds only about 0.2 top-1 on ImageNet by itself. | Read |
| NOTE-365 v3 | Sanity Checks for Saliency Maps | Randomize the model’s weights, or train it on permuted labels, and see whether the saliency map changes. Gradients and GradCAM change. Guided Backprop is invariant to the higher layers. Integrated Gradients keeps the input’s structure while its sign decorrelates. Visual inspection cannot tell these cases apart, and that is the paper’s point. | Read |
| NOTE-367 | Narang et al., transformer modifications | About fifty transformer modifications reimplemented in one T5/Mesh TensorFlow codebase at 223M parameters, with hyperparameters fixed, on four task families. Most do not beat the vanilla pre-norm, relative-attention baseline. The winners are GLU-variant activations, RMSNorm, untied embeddings, and sparse experts or other variants that cost parameters or time. The Universal Transformer, ReZero, Fixup, block sharing and the Evolved Transformer all lose. The positives are largely same-codebase re-runs, GLU Variants included, so the paper is strong evidence against the losers and weak evidence that the winners transfer. | Read |
| NOTE-368 | Attention-only transformers | A necessity test of the feed-forward layer with three matchings. Deleting it in place costs 0.47 nats. At matched FLOPs the cost is 0.26 nats. At matched parameters, with the budget moved into attention depth, the cost is 0.0055 nats, and it shrinks with tokens. What remains is concentrated on low-context tokens, where recall has to come from the weights. QK-norm is what keeps the attention-only stack trainable. At or below 87M parameters, on a synthetic reasoning corpus. | Read |
| NOTE-370 | EDM2 | Magnitude-preserving layers plus forced weight normalization take the ADM U-Net from FID 8.00 to 2.56 on ImageNet-512 at equal compute, in a cumulative single-model ladder whose steps are all far larger than FID’s noise. Post-hoc EMA reconstructs any averaging length from two stored power-function averages, and what it shows is that the best length depends on architecture, learning rate, guidance weight and the metric — the last by 2% against 14% under guidance. | Read |
| NOTE-372 | Dropout as a Bayesian approximation | Dropout before every weight layer, with L2, is shown to optimize (up to an approximated KL term) a variational objective for a deep Gaussian process, so running T stochastic forward passes at test time yields a predictive mean and variance for free. The evidence is qualitative figures, a UCI regression table on 50-unit one-layer networks, and one RL run; the MC-versus-weight-scaling comparison is stated without numbers. | Read |
| NOTE-373 | Tiny RNNs discover cognitive strategies | GRUs with only 1–4 units predict individual animals’ and humans’ choices in six reward-learning tasks better than 30+ classical cognitive models of the same dimensionality. Because they are so small, they can be read as dynamical systems, exposing strategies (state-dependent learning rates, perseveration, reward-induced indifference) that no classical model contains. | Skimmed |
| NOTE-375 | Burrell: automated decision-making as domination | Defining algorithmic injustice as allocative unfairness is a product of computer science’s status and funding, not of the harms themselves. Redefining it as domination, following Iris Marion Young, widens the remedies from code fixes to law, participation, resistance and bans. | Skimmed |
| NOTE-378 | Talagrand Meets Talagrand | Take a centered Gaussian process on a finite set T with max variance σ² and minimum separation a. The expected softmax-weighted average g(β) = E[Σ_t X_t e^{βX_t}/Z] satisfies g(β) ≤ √(2σ²·E D(ν_β‖ν₀)) for every β (Thm 1, D = KL from uniform). For β ≥ β*, g(β) ≥ c·a·√(E D(ν_β‖ν₀)), with c the Sudakov constant (1/17 via Marcus–Rosen) (Thm 2). For i.i.d. N(0,σ²) logits there is an exact identity, g(β) = βσ²(1 − E‖ν_β‖²₂) (Lemma 2). The quenched free energy is bounded above by √(2σ²·E D_{1/2}(ν_β‖ν₀)), with an i.i.d. lower bound of (cσ/2)√(E D_{1/2}) (Thms 4–5). | Read |