arXiv:2603.00376v3 Announce Type: replace Abstract: NeuroHex is a brain-inspired hexagonal coordinate system designed to support highly efficient world models and reference frames for online adaptive AI systems. Inspired by the hexadirectional firing structure of grid cells in the human brain, NeuroHex adopts a cubic isometric hexagonal coordinate formulation that provides full 60deg rotational symmetry and […]
SWE-QA: A Dataset and Benchmark for Complex Code Understanding
arXiv:2604.24814v1 Announce Type: cross Abstract: In this paper, we introduce SWE-QA, a text and code corpus aimed at benchmarking multi-hop code comprehension, addressing the gap between simplified evaluation tasks and the complex reasoning required in real-world software development. While existing code understanding benchmarks focus on isolated snippets, developers must routinely connect information across multiple dispersed […]
MemRec: Collaborative Memory-Augmented Agentic Recommender System
arXiv:2601.08816v3 Announce Type: replace-cross Abstract: The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predictions. However, existing agents maintain these memories in isolation. This overlooks crucial collaborative signals, such as user-item co-engagements and peer relationships across the community, which […]
Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora
arXiv:2604.24819v1 Announce Type: cross Abstract: Reliably transferring specialized human knowledge from text into large language models remains a fundamental challenge in artificial intelligence. Fine-tuning on domain corpora has enabled substantial capability gains, but the process operates without feedback: when a model fails on a domain task, there is no method to diagnose what is deficient […]
Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act
arXiv:2604.03254v2 Announce Type: replace-cross Abstract: Technical and legal debates frequently suggest that “accuracy” is an objective, measurable, and purely technical property. We challenge this view, showing that evaluating AI performance fundamentally depends on context-dependent normative decisions. These techno-normative choices are crucial for rigorous AI deployment, as they determine which errors are prioritised, how risks are […]
SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration
arXiv:2602.10750v2 Announce Type: replace-cross Abstract: The growing sophistication of modern malware and phishing campaigns has diminished the effectiveness of traditional signature-based intrusion detection systems. This work presents SecureScan, an AI-driven, triple-layer detection framework that integrates logistic regression-based classification, heuristic analysis, and external threat intelligence via the VirusTotal API for comprehensive triage of URLs, file hashes, […]
Benchmarking and Adapting On-Device LLMs for Clinical Decision Support
arXiv:2601.03266v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly advanced in clinical decision-making, yet the deployment of proprietary systems is hindered by privacy concerns and reliance on cloud-based infrastructure. Open-source alternatives allow local inference but often have large model sizes that limit their use in resource-constrained clinical settings. Here, we benchmark on-device LLMs […]
Intellectual Stewardship: Re-adapting Human Minds for Creative Knowledge Work in the Age of AI
arXiv:2603.18117v2 Announce Type: replace-cross Abstract: Background: Amid the opportunities and risks introduced by generative AI, learning research needs to envision how human minds and responsibilities should re-adapt as AI augments or automates various tasks and enters daily learning, knowledge work, and social life. Approach: Drawing on theories of learning, intelligence, and knowledge creation, this conceptual […]
Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation
arXiv:2604.24987v1 Announce Type: new Abstract: Chart-to-table translation converts chart images into structured tabular data. Accurate translation is crucial for Multimodal Language Model (MLM) to answer complex queries. We observe imbalances in the number of images across different aspects of the y-axis information in public chart datasets. Such imbalances can introduce unintended biases, causing uneven MLM […]
Sparse Personalized Text Generation with Multi-Trajectory Reasoning
arXiv:2604.24996v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on dense interaction histories, making them ineffective in cold-start scenarios where such data is sparse or unavailable. While external signals (e.g., content of similar users) […]
AI as Consumer and Participant: A Co-Design Agenda for MBSE Substrates and Methodology
arXiv:2604.25526v1 Announce Type: cross Abstract: AI tools are being deployed over MBSE models today, and those models were not designed for this kind of consumption. The problem is not simply that tools hallucinate: well-prompted frontier models produce competent, useful output over a conformant SysML model, but the reasoning they produce is drawn from training rather […]
SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton
arXiv:2604.25498v1 Announce Type: cross Abstract: Generating symphonic music requires simultaneously managing high-level structural form and dense, multi-track orchestration. Existing symbolic models often struggle with a “complexity-control imbalance”, in which scaling bottlenecks limit long-term granular steerability. We present SymphonyGen, a 3D hierarchical framework for contemporary cinematic orchestration. SymphonyGen employs a cascading decoder architecture that decomposes the […]