arXiv:2605.02552v1 Announce Type: cross Abstract: Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tumor suppression against toxicity. However, most reinforcement learning approaches assume full observability of the patient state, a condition rarely met in clinical practice. We investigate whether memory-augmented policies can improve chemotherapy […]
AFFormer: Adaptive Feature Fusion Transformer for V2X Cooperative Perception under Channel Impairments
arXiv:2605.01888v1 Announce Type: cross Abstract: Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to channel impairments, such as noise, fading, and interference. To strengthen the reliability of intelligent transportation systems, this work improves the […]
OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis
arXiv:2605.02714v1 Announce Type: cross Abstract: The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representations from large-scale unlabeled datasets. However, current ophthalmic AI paradigms are predominantly constrained to single-modality inference, thereby creating a dissonance with clinical practice where diagnosis relies on the synthesis of […]
MambaSL: Exploring Single-Layer Mamba for Time Series Classification
arXiv:2604.15174v2 Announce Type: replace-cross Abstract: Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minimally redesigns the selective SSM and projection layers of a single-layer Mamba, guided by four TSC-specific […]
A Rosetta Stone Hypothesis for Neurophenomenology: Mathematical Predictions from Predictive Processing
arXiv:2409.20318v3 Announce Type: replace Abstract: Consciousness science faces the challenge of bridging first-person experience with third-person empirical measurements. Neurophenomenology aims to build such `generative passages’ connecting the content of experience with behavioural and neuroscientific data. However, the mathematical machinery for such bridges remains underdeveloped. Here we develop a Rosetta Stone hypothesis from predictive processing, where […]
Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts
arXiv:2605.01882v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown considerable potential in chart understanding and reasoning tasks. However, they still struggle with high information density (HID) charts characterized by multiple subplots, legends, and dense annotations due to three major challenges: (1) limited fine-grained perception results in the omission of critical visual cues; […]
Epistemic Reject Option Prediction
arXiv:2511.04855v2 Announce Type: replace Abstract: In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses this by allowing the model to abstain when prediction uncertainty is high. Traditional reject-option approaches focus solely on aleatoric uncertainty, an assumption valid only when large training data makes […]
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
arXiv:2604.13924v2 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data. This scarcity makes unsupervised approaches predominant, yet existing methods often rely on reconstruction or forecasting, which struggle with complex […]
Is there “Secret Sauce” in Large Language Model Development?
arXiv:2602.07238v2 Announce Type: replace Abstract: Do leading LLM developers possess a proprietary “secret sauce”, or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but […]
The Norm-Separation Delay Law of Grokking: A First-Principles Theory of Delayed Generalization
arXiv:2603.13331v2 Announce Type: replace Abstract: Grokking — the sudden generalisation that appears long after a model has perfectly memorised its training data — has been widely observed but lacks a quantitative theory explaining the length of the delay. We show that grokking is a norm-driven representational phase transition in regularised training dynamics, and establish the […]
InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
arXiv:2604.04106v2 Announce Type: replace Abstract: The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and privacy-preserving data sharing. However, existing methods face a two-fold challenge: they lack the deep semantic understanding to interpret complex user travel intent, and struggle to handle complex constraints while maintaining […]
Unsupervised Learning of Robust Spectral Shape Matching
arXiv:2304.14419v2 Announce Type: replace-cross Abstract: We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise […]