✗ Failed on benchmark 2026

Gaussian-Remainder Tail-Risk Optimizer

Implementation & benchmark of arXiv:2607.12357 — A Gaussian-Remainder Hierarchy for Sums of Random Variables with Big-Jump Statistics

Usefulness7/10

Difficulty5/10

Novelty7/10

Dynamics Stability Speedup Generalization Optimizer Training dynamics Regularization Probability Statistics Stochastic processes Statistical mechanics

Source paper: A Gaussian-Remainder Hierarchy for Sums of Random Variables with Big-Jump Statistics arXiv:2607.12357 ⓘ · analyzed Aug 30, 2026

AI-generated research hypothesis, automatically tested. Not peer-reviewed.

Idea description

Replace the assumption that a minibatch gradient is fully Gaussian by a Gaussian center plus an explicit single-example big-jump correction. At each update, estimate the distribution of per-example gradient projections along the proposed update direction and use the predicted aggregate tail probability to reduce the step size or increase clipping only when the minibatch is in its non-Gaussian crossover regime.

Formulas

$$P_N(X)=p^{*N}(X)=\int_{-\infty}^{\infty}\frac{dk}{2\pi}e^{-ikX}[\tilde p(k)]^N,\qquad \tilde p(k)=\int_{-\infty}^{\infty}e^{ikx}p(x)\,dx.$$

$$g_N(X)=\frac{1}{\sqrt{2\pi N\sigma^2}}\exp\left[-\frac{(X-N\mu)^2}{2N\sigma^2}\right].$$

$$P_N^{(1)}(X)=g_N(X)+N\big[(p-g)*g_{N-1}\big](X)=g_N(X)+N\left[(p*g_{N-1})(X)-g_N(X)\right].$$

$$\eta_t=\eta_0\min\left(1,\sqrt{\frac{\delta}{\max(R_N(r_t),\delta)}}\right),\qquad R_N(r_t)=\int_{|z|>r_t}P_N^{(1)}(z)\,dz.$$

Mathematical statement

Let z_i=u^T g_i be the scalar projection of per-example gradient g_i onto a normalized proposed update direction u, with empirical one-sample density p(z), mean mu, and variance sigma^2. Let g_N(z) be the Gaussian density with mean N mu and variance N sigma^2, and let g_{N-1}(z) have mean (N-1) mu and variance (N-1) sigma^2. The exact sum density is P_N=p^{*N}. Writing p=g+q, where g is the Gaussian density with mean mu and variance sigma^2 and q=p-g, gives the first Gaussian-remainder truncation P_N^(1)=g_N+N(q*g_{N-1})=g_N+N[(p*g_{N-1})-(g*g_{N-1})]. The Gaussian term describes the central limit region, while the residual convolution retains single-big-jump tail structure. For a threshold r, compute the predicted tail risk R_N(r)=integral over |z|>r of P_N^(1)(z) dz and impose R_N(r) less than or equal to delta as a trust-region condition.

Implementation notes

Integrate this method into minibatch SGD or Adam immediately before applying the parameter update. For a minibatch of size N, compute per-example gradients g_i using per-sample automatic differentiation. Set u to the normalized current momentum direction, or to the normalized minibatch mean gradient when momentum is unavailable, and record z_i=u^T g_i. Maintain exponentially weighted estimates of mu and sigma^2. Fit a one-dimensional KDE or histogram p_hat to recent z_i values. Evaluate g_hat_N and g_hat_{N-1} on a grid, compute the FFT convolution p_hat*g_hat_{N-1}, and form P_hat_N^(1)=g_hat_N+N[(p_hat*g_hat_{N-1})-g_hat_N]. Numerically integrate its positive-part tail beyond r_t=c sqrt(N sigma_hat^2)+abs(N mu_hat), and call the result R_hat_N. Apply the learning-rate factor eta_t shown above, or clip the projected aggregate gradient when R_hat_N exceeds delta. The paper supplies the hierarchy; KDE bandwidth, c, delta, and exponential averaging are empirical design parameters. The first cheap experiment is an MLP on MNIST or CIFAR-10 with SGD, comparing vanilla SGD, global-norm clipping, and this method at matched average learning rate. Sweep N in {16,64,256}. The prediction is that the Gaussian-only model underestimates rare large updates at small N, while the correction predicts their frequency; observed exceedance rates should approach delta within a factor of two, and the non-Gaussian crossover should move toward larger thresholds approximately proportional to the square root of N.

Verification

Failed on benchmark

Stage 1 · Toy mechanism gate: Passed ✓

Stage 2 · Mechanism transferred to benchmark: Not reproduced ✗

Stage 2 · Practical benchmark result: No measurable effect

Methodology: 8 paired seeds, permutation test (p<0.05) against a learning-rate-tuned baseline. How verification works

Stage 1 — Mechanism check agent confidence 8/10

Built a Gaussian-remainder tail-risk module with analytic mixture convolutions, Monte Carlo validation, parameter sweeps, and a 1-D SGD trust-region demo. The mechanism manifested: predicted crossover thresholds were approximately 10.37, 14.21, 20.02, 29.42, and 45.17 for N=8..128, with near-sqrt(N) scaling (fitted slope 4.10), and the representative P1 tail was 0.000542 versus exact Monte Carlo 0.000616 (12.1% relative error). The optimizer did not win in this tiny test: vanilla SGD had mean final-window loss 0.000409 versus 0.000573 for risk SGD, so this is a validated mechanism signal rather than an observed training improvement.

Agent confidence: 8/10

Baseline: 1-D heavy-tailed quadratic SGD: mean loss over last 200 steps = 0.000409; final loss = 0.000240; mean learning-rate factor = 1.000.
Idea: Gaussian-remainder risk SGD: mean loss over last 200 steps = 0.000573; final loss = 0.001000; mean learning-rate factor = 0.993. Tail mechanism: P1=0.000542 versus Monte Carlo=0.000616 at N=32; crossover threshold scales approximately sqrt(N), with fitted slope 4.10.

Limitations: Only a 1-D stochastic quadratic was used for optimization, not an MLP/MNIST experiment. The demo uses known mixture parameters rather than estimating a KDE from per-example gradients, does not compare global-norm clipping or Adam, and uses a first-order signed-tail approximation with positive-part integration where needed. The linear-in-N epsilon signal is regime-dependent and was not uniformly monotone at every threshold.

How to run: python3 experiment.py

Stage 2 — Benchmark vs. tuned baseline (arXiv:2607.12357) Sep 2, 2026

Ahead of the baseline (−0.0%) but not statistically significant (p=1, wins 5 / 8 paired seeds).

Benchmark: Tabular regression (Friedman)
Model: mlp_tiny
Paired seeds: 8
Baseline mean: 8.6778
Idea mean: 8.6772
Effect (Δ): -0.0006 (−0.0%; negative = idea better)
Wins: 5 / 8 paired seeds
p-value: 1 (permutation test, 20 000 shuffles)
Smallest detectable effect: ±1.1%
Mechanism: Not confirmed ✗
Practical effect: No effect
Baseline tuning: swept over 3 configs

Limitations:

Only the registered tabular track and mlp_tiny model were tested. No other built-in tracks were run; KDE bandwidth, tail threshold multiplier, and delta were fixed rather than broadly swept.

How to run: python3 bench_experiment.py

Verdict computed by deterministic test code from paired-seed statistics — not by the language model.

Stage 2 — Benchmark Aug 31, 2026 latest Failed ✗

Agent confidence: 9/10

The Gaussian-remainder risk-scaled Adam intervention was tested on the structurally matched tabular Friedman regression task using the shared mlp_tiny architecture. It did not produce a significant improvement: paired delta was -0.000554 with permutation p=1.0. The trained-model mechanism signature was also not confirmed because predicted tail probability 0.02265 differed from observed 0.0.

Baseline: Tuned Adam, lr=0.01: 8-seed test MSE mean 8.6777626872, std 0.9022750956.
Idea: Risk-scaled Adam, lr=0.01: 8-seed test MSE mean 8.6772084832, std 0.9489654596; mean update factor 0.9385851807.

Benchmark result