April 2026

Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)-based approaches often overlook graph context or depend on distillation from larger models, limiting generalisation. We propose TRN-R1-Zero, a post-training framework for TRN reasoning trained solely via reinforcement learning. TRN-R1-Zero directly optimises base LLMs using a Neighbour-aware Group Relative Policy […]

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A deeper look at the missing layer in modern systems The Problem You Don’t Notice – Until You Do Most systems don’t fail loudly. They don’t crash or burn or take down everything at once. Instead, they drift. Something small breaks. A constraint is violated. A user notices before the system does. You open the logs expecting answers. And the logs are there. Plenty of them. Requests came in, retries happened, downstream services responded, and eventually things succeeded. […]

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Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subgroups remains poorly understood. Using pooled data from ten consecutive annual waves of the U.S. Behavioral Risk Factor Surveillance System (2015-2024; n = 3,242,218), we investigate heterogeneity in the association between leisure-time PA and frequent mental distress (FMD, >=14 days/month) across age groups. Survey-weighted logistic regression reveals a striking age gradient: the adjusted odds ratio for PA ranges from […]

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A complete walkthrough of implementing the original Attention Is All You Need encoder-decoder Transformer—no torch. nn.Transformer, no shortcuts. The 2017 paper “Attention Is All You Need” by Vaswani et al. fundamentally changed natural language processing by replacing recurrent networks with a purely attention-based architecture . Yet for most practitioners, the Transformer remains a black box—something you import from Hugging Face and fine-tune. This article walks through a full from-scratch PyTorch implementation of the original encoder-decoder Transformer, inspired by the Harvard […]

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Modern production systems generate more data than most developers can realistically process. Every request emits logs. Every service exports metrics. Every dependency introduces another layer of signals. In theory, this should make systems easier to understand. In practice, it does the opposite. Dashboards become dense, alerts become noisy, and when something breaks, the same questions still come up. What is actually wrong? Who is affected? Where do you even start? The problem is not observability. It is interpretation. […]

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arXiv:2604.17026v1 Announce Type: new Abstract: Transmission expansion planning (TEP) plays a critical role in ensuring power system reliability and facilitating the integration of renewable energy resources. However, this process requires planners to constantly deal with significant uncertainty. While multistage stochastic TEP models provide a robust framework for identifying investment plans under uncertainty, the rapid growth in problem size hinders their computational tractability. To address this challenge, this paper develops a hybrid machine learning-optimisation framework for stochastic TEP. The […]

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arXiv:2604.17025v1 Announce Type: new Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a system undeployable. Current orchestration paradigms suffer from sycophantic compliance, context attention decay [Liu et al., 2024], and stochastic oscillation during self-correction [Huang et al., 2024]. We introduce the Convergent AI Agent Framework (CAAF), which transitions agentic workflows from open-loop generation to closed-loop Fail-Safe Determinism via three pillars: (1) Recursive Atomic Decomposition with physical […]

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arXiv:2604.17024v1 Announce Type: new Abstract: Query-based 3D object detection methods using multi-view images often struggle to efficiently leverage dynamic multi-scale information, e.g., the relationship between the object features and the geometric of the queries are not sufficiently learned, directly exploring the multi-scale spatiotemporal features will pay too many costs. To address these challenges, we propose CAM3DNet, a novel sparse query-based framework which combines three new modules, composite query (CQ), adaptive self-attention (ASA), and multi-scale hybrid sampling (MSHS). First, […]

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arXiv:2604.17023v1 Announce Type: new Abstract: For four decades, the QWERTY keyboard organized white-collar knowledge work. Typing’s dominance was instrumental, not cognitively necessary. As multimodal AI achieves human-parity understanding of speech and gesture, this necessity dissolves. We introduce instrumental dissolution — loss of institutional-default status while persisting in specialist niches. The keyboard era ends not through hardware replacement but through migration of its function into AI systems. The central contribution identifies the verification bottleneck: as AI collapses production friction, […]

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