February 2026

Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting findings. Recent advances in AI have the potential to transform such pipelines in a way that domain experts can focus on interpreting and understanding findings, rather than debugging rigid pipelines or manually annotating data. As part of an active collaboration between data science/AI researchers and behavioral neuroscientists, we showcase an example AI-enhanced pipeline, specifically designed to transform and accelerate the way […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results B. Hyperbolic Space B.1. Riemannian Manifold B.2. Models of Hyperbolic Space B.3. […]

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RAG systems fail because of broken data pipelines, not bad models. Poor chunking orphans meaning. Stale indexes serve outdated answers. Missing metadata can’t filter irrelevant results. Fix Monday: audit your corpus completeness, rebuild indexes regularly, and measure retrieval recall before blaming the LLM. RAG is search with a generative frontend—treat it like one.

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results Appendix The appendix is structured in four sections. A. Proofs on differential […]

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Coordinating actions is the most fundamental form of cooperation in multi-agent reinforcement learning (MARL). Successful decentralized decision-making often depends not only on good individual actions, but on selecting compatible actions across agents to synchronize behavior, avoid conflicts, and satisfy global constraints. In this paper, we propose Action Graph Policies (AGP), that model dependencies among agents’ available action choices. It constructs, what we call, textit{coordination contexts}, that enable agents to condition their decisions on global action dependencies. Theoretically, we […]

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LadybirdBrowser/ladybird: Abandon Swift adoption Back in August 2024 the Ladybird browser project announced an intention to adopt Swift as their memory-safe language of choice. As of this commit it looks like they’ve changed their mind: Everywhere: Abandon Swift adoption After making no progress on this for a very long time, let’s acknowledge it’s not going anywhere and remove it from the codebase. Via Hacker News Tags: ladybird, swift

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Author(s): Mukundan Sankar Originally published on Towards AI. I built a Streamlit SQL coach that grades your queries and fixes your form without giving away the answer. I use SQL a lot. I made this app to help me when my queries are wrong. I didn’t build it because I’m perfect at SQL. Photo by Rubaitul Azad on UnsplashThe author describes the motivation and development of an AI app designed to enhance SQL training, likening the training process […]

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Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback is often expensive and slow to obtain, making online reinforcement learning (RL) impractical. High-coverage hardware verification exemplifies this challenge due to its reliance on industrial simulators and non-differentiable execution signals. We propose LLM4Cov, an offline agent-learning framework that models verification as memoryless state transitions guided by deterministic evaluators. Building on this formulation, we introduce execution-validated data curation, policy-aware agentic data synthesis, and worst-state-prioritized […]

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Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural […]

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