Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review
Claims, disputes, and litigations are major legal issues in construction projects, which often result in cost overruns, delays, and adverse working relationships among the contracting parties. Recent advances in natural language processing (NLP) techniques offer great potentials that can process...
Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification
arXiv:2602.12575v1 Announce Type: new Abstract: Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data...
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602.12542v1 Announce Type: new Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, its "black-box" nature prevents...
The Balancing Act: Looking Backward, Looking Ahead
Accuracy Standards for AI at Work vs. Personal Life: Evidence from an Online Survey
arXiv:2602.13283v1 Announce Type: new Abstract: We study how people trade off accuracy when using AI-powered tools in professional versus personal contexts for adoption purposes, the determinants of those trade-offs, and how users cope when AI/apps are unavailable. Because modern AI...
Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
arXiv:2602.15248v1 Announce Type: new Abstract: Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is...
LAMMI-Pathology: A Tool-Centric Bottom-Up LVLM-Agent Framework for Molecularly Informed Medical Intelligence in Pathology
arXiv:2602.18773v1 Announce Type: new Abstract: The emergence of tool-calling-based agent systems introduces a more evidence-driven paradigm for pathology image analysis in contrast to the coarse-grained text-image diagnostic approaches. With the recent large-scale experimental adoption of spatial transcriptomics technologies, molecularly validated...
Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training
arXiv:2602.19225v1 Announce Type: new Abstract: Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately distinguishing high-value informative signals from stochastic noise is critical for sample-efficient training. In real-world...
Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs
arXiv:2603.00024v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to sycophantic behavior, uncritically conforming to user beliefs. As models increasingly condition responses on user-specific context (personality traits, preferences, conversation history), they gain information to tailor agreement more effectively....
Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
arXiv:2603.04896v1 Announce Type: new Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent...
Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
arXiv:2603.02280v1 Announce Type: new Abstract: With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distributions. However, CIL faces the core challenge of catastrophic forgetting, often manifested...
HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit
arXiv:2602.23699v1 Announce Type: cross Abstract: The quadratic computational cost of processing vision tokens in Multimodal Large Language Models (MLLMs) hinders their widespread adoption. While progressive vision token pruning offers a promising solution, current methods misinterpret shallow layer functions and use...
Uncertainty-aware Language Guidance for Concept Bottleneck Models
arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive...
2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
arXiv:2602.21889v1 Announce Type: new Abstract: Across a growing number of fields, human decision making is supported by predictions from AI models. However, we still lack a deep understanding of the effects of adoption of these technologies. In this paper, we...
Corporate Governance in the Age of AI: Board Responsibilities and Best Practices
As AI transforms business operations, corporate boards face new governance challenges requiring updated oversight frameworks and expertise.
The Poly Problem in Zoning: Redefining “Family” for a Changing Society lawreview - Minnesota Law Review
By ARIC SHORT & TANYA PIERCE. Full Text. Single-family zoning has long dictated not only where people may live but also with whom. Although extensively critiqued for perpetuating racial and economic exclusion, these laws also privilege relationships defined by blood,...
Trace raises $3M to solve the AI agent adoption problem in enterprise
Trace is launching with $3 million in seed funding, including investment from Y Combinator, Zeno Ventures, Transpose Platform Management, Goodwater Capital, Formosa Capital, and WeFunder.
$\kappa$-Explorer: A Unified Framework for Active Model Estimation in MDPs
arXiv:2602.20404v1 Announce Type: new Abstract: In tabular Markov decision processes (MDPs) with perfect state observability, each trajectory provides active samples from the transition distributions conditioned on state-action pairs. Consequently, accurate model estimation depends on how the exploration policy allocates visitation...
OpenAI calls in the consultants for its enterprise push
OpenAI is partnering with four consulting giants in an effort to see more adoption of its OpenAI Frontier AI agent platform.
Developing AI Agents with Simulated Data: Why, what, and how?
arXiv:2602.15816v1 Announce Type: new Abstract: As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse...
How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
arXiv:2602.17084v1 Announce Type: new Abstract: The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and...