arXiv:2604.05190v1 Announce Type: cross Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language models (LLMs) offer a promising opportunity to use artificial intelligence to improve screening. This study systematically explored both encoder- and decoder-based generative LLMs for screening clinical narratives to […]
Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibration
arXiv:2602.00913v3 Announce Type: replace-cross Abstract: Human value detection from single sentences is a sparse, imbalanced multi-label task. We study whether Schwartz higher-order (HO) categories help this setting on ValueEval’24 / ValuesML (74K English sentences) under a compute-frugal budget. Rather than proposing a new architecture, we compare direct supervised transformers, hard HO$rightarrow$values pipelines, Presence$rightarrow$HO$rightarrow$values cascades, compact […]
SemLink: A Semantic-Aware Automated Test Oracle for Hyperlink Verification using Siamese Sentence-BERT
arXiv:2604.05711v1 Announce Type: cross Abstract: Web applications rely heavily on hyperlinks to connect disparate information resources. However, the dynamic nature of the web leads to link rot, where targets become unavailable, and more insidiously, semantic drift, where a valid HTTP 200 connection exists, but the target content no longer aligns with the source context. Traditional […]
FastDiSS: Few-step Match Many-step Diffusion Language Model on Sequence-to-Sequence Generation–Full Version
arXiv:2604.05551v1 Announce Type: cross Abstract: Self-conditioning has been central to the success of continuous diffusion language models, as it allows models to correct previous errors. Yet its ability degrades precisely in the regime where diffusion is most attractive for deployment: few-step sampling for fast inference. In this study, we show that when models only have […]
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
arXiv:2604.06129v1 Announce Type: cross Abstract: This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a compact representation through a learned polynomial function, from which each token retrieves contextual information. We prove that PoM satisfies the contextual […]
Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models
arXiv:2604.05110v1 Announce Type: cross Abstract: Breast cancer screening relies heavily on mammography, where the craniocaudal (CC) and mediolateral oblique (MLO) views provide complementary information for diagnosis. However, many datasets lack complete paired views, limiting the development of algorithms that depend on cross-view consistency. To address this gap, we propose a three-channel denoising diffusion probabilistic model […]
Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation
arXiv:2604.05150v1 Announce Type: cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows execute deterministically without further model invocation. This paradigm has antecedents in prior work on declarative pipeline optimization (DSPy) and hybrid neural-symbolic planning (LLM+P); our contribution is a systems-oriented […]
Spec Kit Agents: Context-Grounded Agentic Workflows
arXiv:2604.05278v1 Announce Type: cross Abstract: Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain “context blind” in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks. Read-only probing hooks […]
MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library
arXiv:2604.05458v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) face important limitations. Signature-based methods are effective for known attack patterns, but they struggle to detect zero-day attacks and often miss modified variants of previously known attacks, while many machine learning approaches offer limited interpretability. These challenges become even more severe in IoT environments because […]
SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs
arXiv:2604.04947v1 Announce Type: cross Abstract: With the rapid proliferation of online sports journalism, extracting meaningful pre-game and post-game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news […]
Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
arXiv:2604.04979v1 Announce Type: cross Abstract: Coding agents repeatedly consume long tool observations even though only a small fraction of each observation matters for the next step. We study task-conditioned tool-output pruning: given a focused query and one tool output, return the smallest verbatim evidence block the agent should inspect next. We introduce a benchmark of […]
Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning
arXiv:2604.05931v1 Announce Type: cross Abstract: Zero-shot unsupervised reinforcement learning (URL) offers a promising direction for building generalist agents capable of generalizing to unseen tasks without additional supervision. Among existing approaches, successor representations (SR) have emerged as a prominent paradigm due to their effectiveness in structured, low-dimensional settings. However, SR methods struggle to scale to high-dimensional […]