Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature
arXiv:2609.10652v1 Announce Type: new
Abstract: Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and re...
How to Combine Traditional Machine Learning with Agentic Reasoning
In this article, you will learn where traditional machine learning reaches its limits, what agentic reasoning adds, and how combining the two produces AI systems...
Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions
arXiv:2609.09306v1 Announce Type: new
Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions, i.e. gradients or Jacobians, characterizes the structure of phenomenal experience. It does so in an idealized world inhabited by neural networks, Gradland,...
OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflows
arXiv:2609.09203v1 Announce Type: new
Abstract: Existing benchmarks for autonomous AI scientists evaluate only final outputs---generated code, hypotheses, or papers---yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit scientific methodology, di...
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
arXiv:2609.09233v1 Announce Type: new
Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused on agent skills: reusable capabilities represented as skill packages, i.e., multi-file bundles contain...
When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic
arXiv:2609.05508v1 Announce Type: new
Abstract: Option-critic learns options: sub-policies together with a learned rule for when each one hands control back. Its headline result is that performance improves as options are added. We explain that result, with theory and experiment. First, the termina...
Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
arXiv:2609.05574v1 Announce Type: new
Abstract: Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to the graph's topological features, facilitating the extraction of high-...
AutoFyn Technical Report: Non-Parametric Expert Iteration for Long-Horizon Agents
arXiv:2609.05446v1 Announce Type: new
Abstract: We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than model weights. Each round begins from a fresh model ses...
Chain of Thought vs. Tree of Thoughts: Which is Best for AI Agents?
In this article, you will learn the key differences between Chain of Thought and Tree of Thoughts prompting, and how each reasoning framework is applied...
From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
arXiv:2609.04286v1 Announce Type: new
Abstract: Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve evidence, compare candidates, and support or execute actions. This systematized narrative review t...
Data-Optimized Contingency Screening: A Machine Learning Approach to Power System Security
arXiv:2609.04300v1 Announce Type: new
Abstract: Ensuring the security of the power system is essential for stability and reliability, especially in the event of disruption. Effective classification of contingency in power systems enables proactive decision-making and mitigates large-scale breakdown...
Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence
arXiv:2609.02981v1 Announce Type: new
Abstract: Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose learners, recommend tasks, and provide formative feedback. This paper studies the structure and applica...
Modern Transformers Are Implicit Hybrids: From Functional Differentiation to Principled Hybrid Architecture Design
arXiv:2609.02986v1 Announce Type: new
Abstract: Hybrid architectures combining Full Attention (FA) and Linear Attention (LA) are increasingly prominent, yet their allocation remains heuristic. We seek an evidence-grounded basis in head-level functional organization learned by RoPE-based Transformer...
arXiv:2609.02987v1 Announce Type: new
Abstract: Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward while having very different chances of producing a rare but high-reward ro...
Single-Agent vs. Multi-Agent Systems: When the Complexity Is Worth It
In this article, you will learn the key differences between single-agent and multi-agent AI systems, and how to decide which architecture fits your problem. Topics...
When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
arXiv:2609.01741v1 Announce Type: new
Abstract: Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extractors diverge on numeric-threshold presence at a false-negative rate of 0.43. We ask what formal log...
Efficient Context-Limited Telescope Bibliography Classification for the WASP-2025 Shared Task Using SciBERT
arXiv:2609.01647v1 Announce Type: new
Abstract: The creation of telescope bibliographies is a crucial part of assessing the scientific impact of observatories and ensuring reproducibility in astronomy. This task involves identifying, categorizing, and linking scientific publications that reference ...
Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI
arXiv:2609.01685v1 Announce Type: new
Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently inte...
Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
arXiv:2608.28840v1 Announce Type: new
Abstract: Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the same up to some class of transformations. While there exists many method...
A collective capability boundary in frontier large language models on guideline-conformant and case-specific oncology decision-making
arXiv:2608.28592v1 Announce Type: new
Abstract: Large language models (LLMs) achieve high scores on medical knowledge examinations, yet real-world oncology is not a knowledge test--it is a sequence of guideline-pathway choices, escalation judgments, and commitments under uncertainty. Existing bench...
arXiv:2608.27515v1 Announce Type: new
Abstract: The recently introduced block-sparse featurizer (BSF; Fel et al., 2026) is similar to a sparse autoencoder (SAE), but its atomic unit is a small subspace (a block of directions) rather than a single direction. It is designed for features that live on ...
Marginal Coverage Credit Reduces Redundant Exploration in Parallel State-Entropy Optimization
arXiv:2608.27507v1 Announce Type: new
Abstract: Policy Gradient for Parallel State Entropy maximization (PGPSE) expands state-space coverage by training independently parameterized policies in replicated copies of the same environment. However, its pooled team-entropy score measures only collective...
NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation
arXiv:2608.26222v1 Announce Type: new
Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, largely rely on response-level feedback: each candidate prompt typically req...