arXiv:2604.04951v1 Announce Type: cross Abstract: Imagine receiving a video call from your CFO, surrounded by colleagues, asking you to urgently authorise a confidential transfer. You comply. Every person on that call was fake, and you just lost $25 million. This is not a hypothetical. It happened in Hong Kong in January 2024, and it is […]
Haematocrit and Shear Rate Modulate Local Cell-free Layer Thickness and Platelet Margination in Blood Flow Along a Sinusoidal Wall
arXiv:2604.05573v1 Announce Type: cross Abstract: The geometry of blood vessels strongly affects hemostasis and thrombosis through red blood cell (RBC) dynamics and platelet margination. Growing platelet aggregates, in turn, reshape the local vessel wall topography, leading to a strongly coupled system. However, it is not well understood how surface heterogeneities alter local hemodynamics and platelet […]
Chiplet-Based RISC-V SoC with Modular AI Acceleration
arXiv:2509.18355v5 Announce Type: replace-cross Abstract: Achieving high performance, energy efficiency, and cost-effectiveness while maintaining architectural flexibility is a critical challenge in the development and deployment of edge AI devices. Monolithic SoC designs struggle with this complex balance mainly due to low manufacturing yields (below 16%) at advanced 360 mm^2 process nodes. This paper presents a […]
MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
arXiv:2602.12705v4 Announce Type: replace-cross Abstract: We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous […]
Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement
arXiv:2604.06155v1 Announce Type: cross Abstract: Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported […]
Soft Tournament Equilibrium
arXiv:2604.04328v2 Announce Type: replace Abstract: The evaluation of general-purpose artificial agents, particularly those based on large language models, presents a significant challenge due to the non-transitive nature of their interactions. When agent A defeats B, B defeats C, and C defeats A, traditional ranking methods that force a linear ordering can be misleading and unstable. […]
Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
arXiv:2604.06138v1 Announce Type: cross Abstract: Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open-ended generation tasks most relevant to long-context reasoning pose well-known challenges for automatic evaluation. We propose a synthetic data generation pipeline designed to serve both as a training resource and as a […]
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
arXiv:2604.02368v3 Announce Type: replace Abstract: As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark […]
Geometric Limits of Knowledge Distillation: A Minimum-Width Theorem via Superposition Theory
arXiv:2604.04037v2 Announce Type: replace-cross Abstract: Knowledge distillation compresses large teachers into smaller students, but performance saturates at a loss floor that persists across training methods and objectives. We argue this floor is geometric: neural networks represent far more features than dimensions through superposition, and a student of width $d_S$ can encode at most $d_S cdot […]
Learned Elevation Models as a Lightweight Alternative to LiDAR for Radio Environment Map Estimation
arXiv:2604.05520v1 Announce Type: cross Abstract: Next-generation wireless systems such as 6G operate at higher frequency bands, making signal propagation highly sensitive to environmental factors such as buildings and vege- tation. Accurate Radio Environment Map (REM) estimation is therefore increasingly important for effective network planning and operation. Existing methods, from ray-tracing simulators to deep learning generative […]
Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck
arXiv:2604.05526v1 Announce Type: cross Abstract: This paper presents the submission of the S4 team to the Singing Voice Conversion Challenge 2025 (SVCC2025)-a novel singing style conversion system that advances fine-grained style conversion and control within in-domain settings. To address the critical challenges of style leakage, dynamic rendering, and high-fidelity generation with limited data, we introduce […]
Turbulence-like 5/3 spectral scaling in contextual representations of language as a complex system
arXiv:2604.05536v1 Announce Type: cross Abstract: Natural language is a complex system that exhibits robust statistical regularities. Here, we represent text as a trajectory in a high-dimensional embedding space generated by transformer-based language models, and quantify scale-dependent fluctuations along the token sequence using an embedding-step signal. Across multiple languages and corpora, the resulting power spectrum exhibits […]