arXiv:2602.21237v1 Announce Type: new Abstract: Modern cost-based DBMSs frequently exhibit execution instability and tail-latency amplification when high-dimensional relational operations trigger memory-regime transitions such as hash-table spilling and external materialization. We identify a structural failure mode in which intermediate representations are prematurely linearized under memory pressure, causing disproportionate I/O amplification and phase-transition-like latency behavior. To mitigate this, we propose a tensor-based execution path that delays premature linearization and preserves higher-dimensional locality through late materialization and structured intermediate layouts. Using […]

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Privacy-preserving training on sensitive data commonly relies on differentially private stochastic optimization with gradient clipping and Gaussian noise. The clipping threshold is a critical control knob: if set too small, systematic over-clipping induces optimization bias; if too large, injected noise dominates updates and degrades accuracy. Existing adaptive clipping methods often depend on per-example gradient norm statistics, adding computational overhead and introducing sensitivity to datasets and architectures. We propose a control-driven clipping strategy that adapts the threshold using a […]

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How are you, hacker? 🪐 What’s happening in tech today, February 27, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. From Lessons from Building a 100+ Agent Swarm in Web3 to Claude Opus 4.6 and GPT-5.3 Codex: Evaluating the New Leaders in AI-Driven Software Engineering, let’s dive right in. Symfony 7.4: 10 Advanced Logging Patterns You Should Know About By @mattleads [ 11 […]

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arXiv:2602.04926v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is now standard for knowledge-intensive LLM tasks, but most systems still treat every query as fresh, repeatedly re-retrieving long passages and re-reasoning from scratch, inflating tokens, latency, and cost. We present AutoPrunedRetriever, a graph-style RAG system that persists the minimal reasoning subgraph built for earlier questions and incrementally extends it for later ones. AutoPrunedRetriever stores entities and relations in a compact, ID-indexed codebook and represents questions, facts, and answers as […]

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arXiv:2512.19855v2 Announce Type: replace-cross Abstract: This letter extends the exactly sparse Gaussian variational inference (ESGVI) algorithm for state estimation in two complementary directions. First, ESGVI is generalized to operate on matrix Lie groups, enabling the estimation of states with orientation components while respecting the underlying group structure. Second, factors are introduced to accommodate heavy-tailed and skewed noise distributions, as commonly encountered in ultra-wideband (UWB) localization due to non-line-of-sight (NLOS) and multipath effects. Both extensions are shown to integrate […]

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arXiv:2602.18496v1 Announce Type: new Abstract: Radiotherapy continues to become more precise and data dense, with current treatment regimens generating high frequency imaging and dosimetry streams ideally suited for AI driven temporal modeling to characterize how normal tissues evolve with time. Each fraction in biologically guided radiotherapy(BGRT) treated non small cell lung cancer (NSCLC) patients records new metabolic, anatomical, and dose information. However, clinical decision making is largely informed by static, population based NTCP models which overlook the dynamic, […]

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arXiv:2601.07966v1 Announce Type: new Abstract: The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational […]

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In the increasingly complex landscape of enterprise service management, the remarkable transformation led by Aravind Barla at a global Fortune 100 company stands as a compelling testament to visionary leadership and strategic technical execution. This next-generation Employee Experience Platform (EXP) implementation, serving over 100,000 employees across multiple continents, has redefined industry standards for service delivery integration and AI-driven automation while establishing new benchmarks for what’s possible in large-scale digital transformation initiatives. Inheriting a Fragmented Ecosystem When Aravind Barla […]

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Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single-cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their training data. To meta-learn a perturbation effect estimator, we present MapPFN, a prior-data fitted network (PFN) pretrained on synthetic data generated from a prior over causal perturbations. Given a set of […]

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arXiv:2601.04269v1 Announce Type: new Abstract: This paper proposes a conceptual framework in which intelligence and consciousness emerge from relational structure rather than from prediction or domain-specific mechanisms. Intelligence is defined as the capacity to form and integrate causal connections between signals, actions, and internal states. Through context enrichment, systems interpret incoming information using learned relational structure that provides essential context in an efficient representation that the raw input itself does not contain, enabling efficient processing under metabolic constraints. […]

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