TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang
The paper introduces Actionable Case-Based Feature Importance (A‑CBFI), a framework that integrates structural causal models with counterfactual recourse for tabular machine learning. A‑CBFI isolates synergistic interaction bottlenecks and releases suppressive structural locks, concentrating over 98.3% of intervention effort on diagnosed root causes. Empirical tests in finance and healthcare show a 76.9% reduction in active human intervention while keeping recourse costs comparable to exhaustive causal methods.
By Sejong Oh
arXiv:2607. 09695v1 Announce Type: new Abstract: This paper addresses the challenging problem of dynamic feature drift in federated learning, where data distributions evolve across clients and over time -- a common scenario in real-world applications like financial technology.
By Kaijie Chen, Alex Johnson, Maria Garcia, Wei Zhang, Daniel Kim
The paper evaluates four main approaches to scalable nonlinear causal discovery—differentiable, amortized, score‑matching, and combinatorial search—highlighting their distinct accuracy‑runtime trade‑offs. It introduces SPADE, a spline‑based score‑evaluation method that compiles and reuses sufficient statistics, reducing combinatorial search complexity from O(nd³) to O(nd²+d³). Empirical results show SPADE dramatically extends the scalability‑accuracy frontier, solving large‑scale problems with high structural accuracy on both synthetic and real‑world data.
By Hendrik Suhr, Sascha Xu, Jilles Vreeken
arXiv:2610.00968v1 Announce Type: cross
Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...
By Arman Behnam, Binghui Wang
The paper introduces Actionable Case-Based Feature Importance (A-CBFI), a method that transforms the descriptive decomposition of Localized-CBFI into a prescriptive, interventional diagnosis using a structural causal model. A-CBFI selects the cheapest single action when possible and only considers joint actions if a single action cannot achieve the desired outcome. Experiments on ten tabular benchmarks show that A-CBFI matches or exceeds the success of untargeted causal beam search while prescribing fewer direct actions and requiring fewer model evaluations.
By Sejong Oh
The paper introduces SAERL, a method that uses Sparse Autoencoders (SAEs) to guide reinforcement learning data engineering for large language models. By mapping training samples into a feature space of interpretable SAE activations, SAERL selects samples with strong, non‑redundant gradient signals, organizes functional regions from easy to hard, and batches from single activation clusters with moderate mixing. Experiments on mathematical reasoning tasks show consistent performance gains across model scales and RL algorithms, with notable improvements on Qwen2.5-Math-1.5B.
By Yi Jing, Zao Dai, Jinwu Hu, Zijun Yao, Lei Hou, Juanzi Li, Xiaozhi Wang
The paper introduces SAEScientist-Bench, a benchmark that tests whether AI agents can autonomously conduct mechanistic interpretability research using Sparse Autoencoders (SAEs). Agents are tasked with designing contrastive probes and navigating a large feature dictionary in Gemma-2-9B-IT to identify optimal features for a target concept, with performance measured against expert-curated references on activation rank, concept selectivity, and causal steering. Results show that while frontier agents can discover features and outperform controls, they still lag behind expert baselines, especially in causal steering, highlighting both the potential and current limitations of closed-loop autonomous AI research.
By Yuqiao Tan, Shizhu He, Jun Zhao, Kang Liu
arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.
By Shrenik Zinage
FLARE introduces a dense supervision paradigm for long‑horizon coding agents, leveraging a Generative Reward Model (GRM) trained via the RADAR diagnostic framework. The GRM provides real‑time, step‑level risk feedback, enabling FLARE to act as an active scaffold that intercepts high‑risk steps during inference and supplies structured signals for post‑training fine‑tuning and reinforcement learning. Experiments show FLARE outperforms existing methods, achieving a 5× reduction in token consumption and significant performance gains in both supervised fine‑tuning and RL settings.
By Jingxuan Xu, Gang Wu, Yanan Wu, Yutao Mou, Songwei Yu, Tianzhuang He, Zhengshuo Gong, Zhao Liu, Zihang Xu, Wenqiang Zhu, Xinping Lei, Weihao Li, Yuhui Bai, Zhongqiu Wang, Yan Wu, Ariel Deng