regression

A long-time OpenClaw user reports that the 2026.10.10 update broke all 11 chat turns due to a hard-coded 512 MB SQLite worker heap cap causing repeated ERR_WORKER_OUT_OF_MEMORY crashes, alongside a broken migration of Claude CLI model refs — and frustration that similar update regressions keep recurring despite an existing issue.

A video comparing KS3, the latest hermite spline regression block in the K-Splanifolds series, against MLPs on fitting a 4D Mandelbulb over 9,000 gradient descent steps, with KS3 achieving the lowest loss at nearly identical forward/backward latency.

作者对比了 Claude Code 与另外 11 个生产级 AI 编码代理的 harness 实现，指出三、四月的性能回退是 harness bug 而非模型问题，并总结出薄 harness 层的风险与改进建议。

iPadOS 27 introduced a regression where tapping any app other than the presenting one during AirPlay forces the external display to mirror the entire iPad screen, breaking presenter-view workflows in GoodNotes, Keynote, and PowerPoint for educators and presenters.

This paper introduces a geometric framework using the average kernel size to derive deterministic, model-independent bounds on empirical error and accuracy for regression and classification in metric-space settings, highlighting an accuracy–stability trade-off and an empirical error floor caused by data ambiguity.

本文研究模型误设下的有界回归问题，分析了 sign-betting regression 估计器在 L1 度量下的高概率 oracle 不等式，并证明了 proper learning 的最优通用近似因子为 3。

这篇论文将音乐愉悦度预测构建为回归问题，利用MUSIN-G数据集的128通道EEG数据，比较了六种深度学习模型（DeepConvNet、CNN-LSTM、GNN-CNN、TCN等），并通过可解释性分析识别影响预测的关键时间片段。

CoGameReg 提出一种基于合作博弈的协同半监督回归框架，利用 Tri-Game Shapley 机制为异构回归器加权并构建共享伪标签，用于稀疏井标签条件下的地震储层属性预测，并在基准数据集与油田盲井验证中取得更优表现。

This paper identifies blind spots in evaluating deep imbalanced regression, proposing balanced metrics and showing high tail-region instability across random seeds.

The article discusses a paper titled 'Reasoning-Intensive Regression' that proposes MENTAT, a lightweight method combining batch-reflective prompt optimization with neural ensemble learning to improve numerical score prediction from text in AI tasks, showing up to 65% improvement over baselines.

The author discusses the use of classical ML versus LLMs for business classification and regression problems, evaluates the Jev model as a potential tool, and compares it with existing techniques like Structured Outputs and DSPy, highlighting accessibility and effectiveness concerns.

The paper introduces PersonaLink, a training-free method that distills user history into a bounded persona, matching retrieval on classification tasks but not on regression, highlighting a task-type asymmetry.

This paper introduces a graph neural regression framework for non-invasive estimation of body composition metrics such as body fat percentage and bone mineral density, demonstrating improved accuracy over previous methods using clinical data.

Peter Szilagyi reports that Fable introduced a regression into a cryptographic library, which his fuzzer detected, and Fable refused to patch the security issue.

This paper proposes LitEm, a neural regression model that enables transductive knowledge graph embedding models to predict numerical attributes, achieving strong benchmark results and introducing a co-training framework for improved performance.

The paper introduces D^CF5, a diagnostic to predict regionwise gains in dynamic ensembling for regression tasks under distribution shift, validated with high correlation across datasets.

The article reports on performance regression in Grok 4.6 compared to 4.5, with lower pass rate and higher latency, affecting practical business tasks.

This paper tests whether perceived attributes of consumer technologies, rated by humans and frontier language models, predict ownership prevalence better than years-since-launch, finding modest improvements but limitations for short-term forecasts.

This paper proposes a new asymmetric robust bounded sparse smooth (aR) loss function for l1-norm penalized geometric twin support vector machine (aRSGTSVM) to handle classification and regression tasks with label and feature noise, achieving feature selection and robustness. Experiments on synthetic and UCI datasets plus China stock market index tracking demonstrate superiority.

This paper introduces TS2TabPFN, a framework that combines explicit feature extraction with the TabPFN 2.5 tabular foundation model for time series classification and extrinsic regression. Experiments show it outperforms state-of-the-art models in TSER and achieves competitive results in TSC.