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This paper proposes a timing-aware, interpretable self-attention framework that encodes wearable behavioral signals as discrete events with onset timestamps and durations, outperforming existing event-based approaches while improving interpretability for task transition detection.
Pythia, an interpretable AI model trained on a new high-resolution atlas of 67 RNA-binding proteins, is introduced as a collaboration between Genophoria, CZ Biohub, UCSF, and Calico to predict how RBPs recognize RNA by sequence and shape.
该论文提出一种基于非线性时间延迟系统的序列建模框架，通过可微语义延迟估计器、对角状态空间的闭式 O(1) 解以及可解释的模糊语义调制机制，在文本分类和生成任务上超越了现有最先进方法。
阿里提出 MARI 框架，通过决策记忆库（DMB）存储结构化决策记忆（SDM），将异构用户行为蒸馏为可解释的目标、约束和权衡记录，以增强 LLM 推荐系统的推理能力，显著优于现有基线，尤其在困难选择预测任务上表现突出。
本文提出一种可解释的机器学习分析，利用多中心纵向临床队列预测参与正念干预后患者在12周和24周的BDI-II抑郁评分，发现基线严重程度仍是最强预测因子，而短期结局更依赖临床与医院背景，长期结局则更依赖行为依从性和人口统计因素。
The paper proposes a two-part pipeline for interpretable resume–vacancy matching: an offline LLM labeler refined by recruiter feedback that produces eight named matching dimensions, distilled into a LoRA-adapted CPU-servable bi-encoder with per-dimension heads. Trained on 168,772 labeled pairs, the deployed distilled model agrees with recruiter-recorded decisions on 95.79% of a 927-sample production-feedback subset.
A new moving-horizon approximate branch-and-reduce method for training near-optimal deep classification trees on large-scale datasets with continuous features, achieving better accuracy than heuristic baselines and far greater scalability than global optimal solvers.
KernelOnet proposes an interpretable neural operator framework that explicitly incorporates kernel functions in place of trunk networks, offering data-driven, physics-informed, and hybrid kernel strategies for PDE solving with higher accuracy and fewer parameters than DeepONet.
This paper systematically analyzes how reinforcement learning post-training reshapes flow-based VLA policies (such as π0.5 and GR00T N1.5/N1.6), finding that RL induces low-rank parameter updates concentrated in the action expert's Timestep Modules. The authors show shift-vector update directions strongly predict task success and use them to improve RL-trained policies without additional training.
SMILESGNN introduces a multimodal architecture combining SMILES transformers and graph neural networks with cross-attention for interpretable drug toxicity prediction, achieving competitive performance on benchmarks like ClinTox and Tox21 with minimal parameters.
This paper proposes a compact stance-indexed representation of anterior-posterior center-of-pressure from plantar vertical ground reaction force data for classifying Parkinson's disease, achieving high accuracy in machine learning evaluations over existing methods.
The paper introduces a two-model architecture called Summarize-Judge-Refine (SJR) for multimodal content moderation, which decouples content understanding and policy learning via natural language summaries, enabling significant performance gains and few-shot policy adaptation.
This paper evaluates traditional machine learning models versus LLM-generated rule-based systems for heart disease prediction, showing that traditional models have higher accuracy but LLM rules provide better interpretability for clinical use.
This paper introduces a multi-stage rule-chaining framework for compositional and interpretable cognitive reasoning on the Abstraction and Reasoning Corpus (ARC) benchmark, achieving over 95% accuracy through integrated deterministic solvers and hierarchical abstraction.
The paper introduces Structurally Speaking, a structured prompting protocol for motif-oriented graph captioning that improves compactness and consistency while maintaining graph recovery compared to direct prompting of LLMs.
MonoTM is an interpretable topic modeling framework that decouples mixture estimation from feature interpretation using sparse autoencoders, providing semantically meaningful topics beyond traditional word-based representations.
BioGlyph is a method that translates network topology into interpretable language roles to enhance large language models' ability to reason about complex networks, demonstrating significant performance improvements across various domains.
Lngram v2 introduces an efficient and scalable latent conditional memory mechanism for transformers, decoupling memory capacity from backbone width and demonstrating consistent improvements in vision-language models through discrete addresses and structured representations.
CAHR-Net proposes a condition-adaptive hysteresis reconstruction network that improves magnetic core loss modeling by injecting operating conditions into intermediate representations, achieving lower errors with fewer parameters compared to existing methods.
OpenAI's upcoming Astra model reportedly uses looped transformers without an interpretable chain of thought, indicating faster AI progress than predicted by AI 2027 forecasts.