ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks

• URL: http://arxiv.org/abs/2605.24340v1
• Date: Sat, 23 May 2026 01:52:50 GMT
• Title: ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks
• Abstract summary: Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure.<n>We present ChainzRule (CR), a neural architecture replacing typical activations with learnable layers governed by Differential Regularization (DREG)<n>CR achieves $85.71% pm 2.01%$ on Pima Diabetes, $46.20% pm 0.37%$ on SST-5 sentiment classification with a frozen encoder, and $70.17%$ on Full ordinal regression with 3.2M parameters.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensive, inference budgets are tight, and models that cannot explain their behavior are difficult to trust and maintain. We present ChainzRule (CR), a neural architecture replacing typical activations with learnable polynomial layers governed by Differential Regularization (DREG), a layer-wise Jacobian penalty computed analytically during the forward pass at standard inference cost. The core claim is that bounding intermediate derivatives forces the network toward low-frequency, structurally stable representations, simultaneously reducing dependence on labeled data volume, improving robustness to distribution shift, and providing a measurable, gradient-based handle on model behavior. Evaluated across five domains, CR achieves $85.71\% \pm 2.01\%$ on Pima Diabetes (statistically superior to SVM and XGBoost), $46.20\% \pm 0.37\%$ on SST-5 sentiment classification with a frozen encoder (superior to RNTN using approximately 5\% of its training data), $55.79\%$ on SST-5 with a fine-tuned BERT backbone (versus BERT-base linear head at $54.9\%$), $70.17\%$ on Yelp Full ordinal regression with 3.2M parameters versus a 10-model average of $66.35\%$, and $+2.32\%$ mean corruption accuracy on CIFAR-10-C. All results with reported $p$-values fall below the $α= 0.05$ threshold after Bonferroni correction. CR maintains a gradient tail ratio $τ$ (p99/mean) of $1.01$--$1.02$ against $1.07$--$1.09$ for all typical activation function baselines across every data fraction, a structural invariant we propose as the mechanistic driver of sample efficiency and a deployment-time proxy for model reliability.

Related papers

• SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models [53.86918766240095]
  Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation.<n>We propose textbfState Anomaly Neutralization (SANE), which applies adaptive $tanh$ compression at chunk boundaries while preserving the intra-chunk parallel structure.
  arXiv  Detail & Related papers  (2026-08-23T10:41:07Z)
• From Training to Deployment: Post-Hoc Causal Feature Identification via Sensitivity Ratios [10.912482254600478]
  Given a model that is already trained, which features does it rely on causally versus spuriously?<n>We introduce the textbfNormalised Sensitivity Ratio(NSR), a post-hoc, model-agnostic diagnostic for this question.
  arXiv  Detail & Related papers  (2026-07-28T10:26:09Z)
• Scaling Laws for Agent Harnesses via Effective Feedback Compute [53.68149869349268]
  emphEffective Feedback Compute (EFC) is a trace-level scaling coordinate that credits feedback only when it is informative, valid, non-redundant, and retained for subsequent decisions.<n>EFC-based coordinates consistently predict failure rates better than raw-compute baselines.
  arXiv  Detail & Related papers  (2026-05-28T09:45:47Z)
• Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection [0.0]
  We introduce textbfPrism, a framework for structural symmetry diagnosis in complex networks.<n>Prism computes the emphduality defect $(L,P) = |LP - PL|_F / |L|_F$ -- a scalar measuring how far the network deviates from structural self-consistency.
  arXiv  Detail & Related papers  (2026-05-18T04:20:05Z)
• Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback [1.2362187555287152]
  Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors.<n>We propose a behavioral forecast-evaluation methodology that complements accuracy tests by assessing the intermediate decision process itself.
  arXiv  Detail & Related papers  (2026-05-07T06:31:34Z)
• Benchmarking the Utility of Privacy-Preserving Cox Regression Under Data-Driven Clipping Bounds: A Multi-Dataset Simulation Study [0.0]
  Differential privacy (DP) is a mathematical framework that guarantees individual privacy.<n>This study systematically evaluated the impact of DP mechanisms with data-driven clipping bounds on the Cox proportional hazards model.
  arXiv  Detail & Related papers  (2026-04-23T09:53:15Z)
• The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability [0.0]
  We show that geometric stability, the consistency of a representation's pairwise distance structure, addresses two functions.<n> unsupervised stability fails entirely for steering on real-world tasks.<n>It excels at drift detection, measuring nearly $2times$ greater geometric change than CKA during post-training alignment.
  arXiv  Detail & Related papers  (2026-04-20T01:24:45Z)
• When Self-Reference Fails to Close: Matrix-Level Dynamics in Large Language Models [0.0]
  Self-referential statements and meta-cognitive prompts are more stable than paradoxical self-reference on key collapse-related metrics.<n>We propose, as a conjecture, that NCTR forces finite-depth transformers toward dynamical regimes where these problems concentrate.
  arXiv  Detail & Related papers  (2026-04-13T23:23:02Z)
• $V_{0.5}$: Generalist Value Model as a Prior for Sparse RL Rollouts [81.48669089692189]
  Generalist Value Models (such as $V_0.5$) achieve pre-trained value estimation by explicitly encoding model capabilities in-context.<n>We propose $V_0.5$, which adaptively fuses the baseline predicted by such value model with the empirical mean derived from sparse rollouts.<n>$V_0.5$ significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.
  arXiv  Detail & Related papers  (2026-03-11T14:57:41Z)
• Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models [6.908972852063454]
  We introduce a diagnostic framework grounded in Random Matrix Theory (RMT) and Heavy-Tailed Self-Regularization (HTSR)<n>We evaluate nine model families on two Iowa DOT crash classification tasks (173,512 and 371,062 records respectively)<n>We find that the power-law exponent $$ provides a structural quality signal: well-regularized models consistently yield $$ within $[2, 4]$ (mean $2.87 pm 0.34$)<n>We propose an $$-based early stopping criterion and a spectral model selection protocol, and validate both against cross-validated F
  arXiv  Detail & Related papers  (2026-02-23T05:42:54Z)
• Test time training enhances in-context learning of nonlinear functions [51.56484100374058]
  Test-time training (TTT) enhances model performance by explicitly updating designated parameters prior to each prediction.<n>We investigate the combination of TTT with in-context learning (ICL), where the model is given a few examples from the target distribution at inference time.
  arXiv  Detail & Related papers  (2025-09-30T03:56:44Z)
• FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA [68.44043212834204]
  Low-Rank Adaptation (LoRA) is widely used for efficient fine-tuning of language models in learning (FL)<n>Low-Rank Adaptation (LoRA) is widely used for efficient fine-tuning of language models in learning (FL)
  arXiv  Detail & Related papers  (2025-05-19T07:32:56Z)
• Exact Certification of (Graph) Neural Networks Against Label Poisoning [50.87615167799367]
  We introduce an exact certification method for label flipping in Graph Neural Networks (GNNs)<n>We apply our method to certify a broad range of GNN architectures in node classification tasks.<n>Our work presents the first exact certificate to a poisoning attack ever derived for neural networks.
  arXiv  Detail & Related papers  (2024-11-30T17:05:12Z)
• Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization [63.28279815753543]
  In Domain Generalization (DG) settings, models trained on a given set of training domains have notoriously chaotic performance on shifted test domains. We first show that a simple protocol for averaging model parameters along the optimization path, starting early during training, significantly boosts domain generalizationity. We show that an ensemble of independently trained models also has a chaotic behavior in the DG setting.
  arXiv  Detail & Related papers  (2021-10-21T00:08:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.