MIDAS: Mosaic Input-Specific Differentiable Architecture Search
arXiv:2602.17700v1 Announce Type: cross Abstract: Differentiable Neural Architecture Search (NAS) provides efficient, gradient-based methods for automatically designing neural networks, yet its adoption remains limited in practice. We present MIDAS, a novel approach that modernizes DARTS by replacing static architecture parameters...
UBio-MolFM: A Universal Molecular Foundation Model for Bio-Systems
arXiv:2602.17709v1 Announce Type: cross Abstract: All-atom molecular simulation serves as a quintessential ``computational microscope'' for understanding the machinery of life, yet it remains fundamentally limited by the trade-off between quantum-mechanical (QM) accuracy and biological scale. We present UBio-MolFM, a universal...
"Everyone's using it, but no one is allowed to talk about it": College Students' Experiences Navigating the Higher Education Environment in a Generative AI World
arXiv:2602.17720v1 Announce Type: cross Abstract: Higher education students are increasingly using generative AI in their academic work. However, existing institutional practices have not yet adapted to this shift. Through semi-structured interviews with 23 college students, our study examines the environmental...
Five Fatal Assumptions: Why T-Shirt Sizing Systematically Fails for AI Projects
arXiv:2602.17734v1 Announce Type: cross Abstract: Agile estimation techniques, particularly T-shirt sizing, are widely used in software development for their simplicity and utility in scoping work. However, when we apply these methods to artificial intelligence initiatives -- especially those involving large...
GeneZip: Region-Aware Compression for Long Context DNA Modeling
arXiv:2602.17739v1 Announce Type: cross Abstract: Genomic sequences span billions of base pairs (bp), posing a fundamental challenge for genome-scale foundation models. Existing approaches largely sidestep this barrier by either scaling relatively small models to long contexts or relying on heavy...
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
arXiv:2602.17749v1 Announce Type: cross Abstract: A challenge in marine bioacoustic analysis is the detection of animal signals, like calls, whistles and clicks, for behavioral studies. Manual labeling is too time-consuming to process sufficient data to get reasonable results. Thus, an...
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
arXiv:2602.17750v1 Announce Type: cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain and stress. Machine learning has lead to considerable advances in this field lately....
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
arXiv:2602.17751v1 Announce Type: cross Abstract: Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to their popularity and the ease of identifying them through their...
The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
arXiv:2602.17753v1 Announce Type: cross Abstract: Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing...
Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update
In this intellectual work, the clinical and educational aspects of dentistry were confronted with practical applications of artificial intelligence (AI). The aim was to provide an up-to-date overview of the upcoming changes and a brief analysis of the influential advancements...
Symbolic computation of conservation laws of nonlinear partial differential equations in multi‐dimensions
Abstract A direct method for the computation of polynomial conservation laws of polynomial systems of nonlinear partial differential equations (PDEs) in multi‐dimensions is presented. The method avoids advanced differential‐geometric tools. Instead, it is solely based on calculus, variational calculus, and...
Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications
arXiv:2602.18582v1 Announce Type: new Abstract: When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed. As AI agents tackle increasingly complex tasks, aligning their behavior...
Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
arXiv:2602.18640v1 Announce Type: new Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating...
Many AI Analysts, One Dataset: Navigating the Agentic Data Science Multiverse
arXiv:2602.18710v1 Announce Type: new Abstract: The conclusions of empirical research depend not only on data but on a sequence of analytic decisions that published results seldom make explicit. Past ``many-analyst" studies have demonstrated this: independent teams testing the same hypothesis...
Task-Aware Exploration via a Predictive Bisimulation Metric
arXiv:2602.18724v1 Announce Type: new Abstract: Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic exploration, many methods either assume access to low-dimensional states or lack task-aware exploration strategies,...
Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation
arXiv:2602.18749v1 Announce Type: new Abstract: Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation methods fail to address an under-explored challenge in collaboration: bidirectional model learnability...
TPRU: Advancing Temporal and Procedural Understanding in Large Multimodal Models
arXiv:2602.18884v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data, a bottleneck hindering their application in real-world embodied AI. This gap is largely caused by...
Early Evidence of Vibe-Proving with Consumer LLMs: A Case Study on Spectral Region Characterization with ChatGPT-5.2 (Thinking)
arXiv:2602.18918v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as scientific copilots, but evidence on their role in research-level mathematics remains limited, especially for workflows accessible to individual researchers. We present early evidence for vibe-proving with a...
DREAM: Deep Research Evaluation with Agentic Metrics
arXiv:2602.18940v1 Announce Type: new Abstract: Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent benchmarks propose distinct methodologies, yet they suffer...
(Perlin) Noise as AI coordinator
arXiv:2602.18947v1 Announce Type: new Abstract: Large scale control of nonplayer agents is central to modern games, while production systems still struggle to balance several competing goals: locally smooth, natural behavior, and globally coordinated variety across space and time. Prior approaches...
Modularity is the Bedrock of Natural and Artificial Intelligence
arXiv:2602.18960v1 Announce Type: new Abstract: The remarkable performance of modern AI systems has been driven by unprecedented scales of data, computation, and energy -- far exceeding the resources required by human intelligence. This disparity highlights the need for new guiding...
Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
arXiv:2602.18968v1 Announce Type: new Abstract: Tool invocation is a core capability of agentic systems, yet failures often arise not from individual tool calls but from how multiple tools are organized and executed together. Existing approaches tightly couple tool execution with...
When Do LLM Preferences Predict Downstream Behavior?
arXiv:2602.18971v1 Announce Type: new Abstract: Preference-driven behavior in LLMs may be a necessary precondition for AI misalignment such as sandbagging: models cannot strategically pursue misaligned goals unless their behavior is influenced by their preferences. Yet prior work has typically prompted...
How Far Can We Go with Pixels Alone? A Pilot Study on Screen-Only Navigation in Commercial 3D ARPGs
arXiv:2602.18981v1 Announce Type: new Abstract: Modern 3D game levels rely heavily on visual guidance, yet the navigability of level layouts remains difficult to quantify. Prior work either simulates play in simplified environments or analyzes static screenshots for visual affordances, but...
InfEngine: A Self-Verifying and Self-Optimizing Intelligent Engine for Infrared Radiation Computing
arXiv:2602.18985v1 Announce Type: new Abstract: Infrared radiation computing underpins advances in climate science, remote sensing and spectroscopy but remains constrained by manual workflows. We introduce InfEngine, an autonomous intelligent computational engine designed to drive a paradigm shift from human-led orchestration...
Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight
arXiv:2602.18986v1 Announce Type: new Abstract: Organizations across finance, healthcare, transportation, content moderation, and critical infrastructure are rapidly deploying highly automated AI systems, yet they lack principled methods to quantify how increasing automation amplifies harm when failures occur. We propose a...
Benchmark Test-Time Scaling of General LLM Agents
arXiv:2602.18998v1 Announce Type: new Abstract: LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests. While existing benchmarks focus on domain-aware environments for developing specialized agents, evaluating general-purpose agents requires more realistic settings that...
Evaluating Large Language Models on Quantum Mechanics: A Comparative Study Across Diverse Models and Tasks
arXiv:2602.19006v1 Announce Type: new Abstract: We present a systematic evaluation of large language models on quantum mechanics problem-solving. Our study evaluates 15 models from five providers (OpenAI, Anthropic, Google, Alibaba, DeepSeek) spanning three capability tiers on 20 tasks covering derivations,...
Asking the Right Questions: Improving Reasoning with Generated Stepping Stones
arXiv:2602.19069v1 Announce Type: new Abstract: Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may not be able to...
Defining Explainable AI for Requirements Analysis
arXiv:2602.19071v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) has become popular in the last few years. The Artificial Intelligence (AI) community in general, and the Machine Learning (ML) community in particular, is coming to the realisation that in many...