A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning
arXiv:2604.16333v1 Announce Type: new
Abstract: Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This mismatch complicates clinical interpretation and patient stratification and remains insufficiently model...
Governing the Agentic Enterprise: A Governance Maturity Model for Managing AI Agent Sprawl in Business Operations
arXiv:2604.16338v1 Announce Type: new
Abstract: The rapid adoption of agentic AI in enterprise business operations--autonomous systems capable of planning, reasoning, and executing multi-step workflows--has created an urgent governance crisis. Organizations face uncontrolled agent sprawl: the proli...
Semantic Consensus: Process-Aware Conflict Detection and Resolution for Enterprise Multi-Agent LLM Systems
arXiv:2604.16339v1 Announce Type: new
Abstract: Multi-agent large language model (LLM) systems are rapidly emerging as the dominant architecture for enterprise AI automation, yet production deployments exhibit failure rates between 41% and 86.7%, with nearly 79% of failures originating from specifi...
Computational Hermeneutics: Evaluating generative AI as a cultural technology
arXiv:2604.16403v1 Announce Type: new
Abstract: Generative AI systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the hu...
Heterogeneous Self-Play for Realistic Highway Traffic Simulation
arXiv:2604.16406v1 Announce Type: new
Abstract: Realistic highway simulation is critical for scalable safety evaluation of autonomous vehicles, particularly for interactions that are too rare to study from logged data alone. Yet highway traffic generation remains challenging because it requires bro...
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within the realm of Natural Language Processing, limited ...
AI swarms could hijack democracy without anyone noticing
AI-powered personas are becoming so realistic that they can infiltrate online communities and subtly steer public opinion. Unlike traditional bots, they adapt, coordinate, and refine their messaging at a massive scale, creating a false sense of consensus. Early warning signs—like deepfakes and fake ...
Gradient-based Planning for World Models at Longer Horizons
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshapin...
The Spectral Geometry of Thought: Phase Transitions, Instruction Reversal, Token-Level Dynamics, and Perfect Correctness Prediction in How Transformers Reason
arXiv:2604.15350v1 Announce Type: new
Abstract: We discover that large language models exhibit \emph{spectral phase transitions} in their hidden activation spaces when engaging in reasoning versus factual recall. Through systematic spectral analysis across \textbf{11 models} spanning \textbf{5 arch...
Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons
arXiv:2604.15360v1 Announce Type: new
Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data characteristics, forecast uncertainty, planning horizon, and battery c-rate. Synthetic datasets are...
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
arXiv:2604.15456v1 Announce Type: new
Abstract: Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to accelerate evidence-grounded scientific discovery by integrating AI age...
GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology
arXiv:2604.15495v1 Announce Type: new
Abstract: Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans and embodied AI. In these spaces, dense visual features quickly become stale given the quasi-stati...
LACE: Lattice Attention for Cross-thread Exploration
arXiv:2604.15529v1 Announce Type: new
Abstract: Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reas...
arXiv:2604.15558v1 Announce Type: new
Abstract: Deliberative multi-agent systems allow agents to exchange messages and revise beliefs over time. While this interaction is meant to improve performance, it can also create dangerous conformity effects: agreement, confidence, prestige, or majority size...
What Do Your Logits Know? (The Answer May Surprise You!)
Recent work has shown that probing model internals can reveal a wealth of information not apparent from the model generations. This poses the risk of unintentional or malicious information leakage, where model users are able to learn information that the model owner assumed was inaccessible. Using v...
Think AI "knows" what it’s doing? Scientists say think again
Calling AI things like “smart” or saying it “knows” something might sound harmless, but it can quietly mislead people about what AI actually does. A new study shows that news writers are more careful than expected, rarely using strongly human-like language. When they do, it often falls on a spectrum...
Quantum AI just got shockingly good at predicting chaos
Researchers have shown that blending quantum computing with AI can dramatically improve predictions of complex, chaotic systems. By letting a quantum computer identify hidden patterns in data, the AI becomes more accurate and stable over time. The method outperformed standard models while using far ...
Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training
arXiv:2604.14206v1 Announce Type: new
Abstract: This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) optimizer generate...
NuHF Claw: A Risk Constrained Cognitive Agent Framework for Human Centered Procedure Support in Digital Nuclear Control Rooms
arXiv:2604.14160v1 Announce Type: new
Abstract: The rapid digitization of nuclear power plant main control rooms has fundamentally reshaped operator interaction patterns, introducing complex soft-control behaviors and elevated cognitive risks that are not adequately addressed by existing human reli...
Simulating Human Cognition: Heartbeat-Driven Autonomous Thinking Activity Scheduling for LLM-based AI systems
arXiv:2604.14178v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have demonstrated remarkable capabilities in reasoning and tool use, yet they often suffer from rigid, reactive control flows that limit their adaptability and efficiency. Most existing frameworks rely on fixed pipeli...
Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling
arXiv:2604.14221v1 Announce Type: new
Abstract: Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets. Current resources often lack fine-grained anomaly annotations, do not provide explic...