Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
arXiv:2601.14485v1 Announce Type: new
Abstract: The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities and their corresponding execution modes. Genetic programming has been w...
arXiv:2601.11620v1 Announce Type: new
Abstract: Whether machines can be conscious depends not only on what they compute, but \emph{when} they compute it. Most deployed artificial systems realise their functions via sequential or time-multiplexed updates. Conscious experience appears unified and sim...
PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement
arXiv:2601.11747v1 Announce Type: new
Abstract: Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs based on natural language instructions. While VLMs have shown initial succe...
Analytic Bijections for Smooth and Interpretable Normalizing Flows
arXiv:2601.10774v1 Announce Type: new
Abstract: A key challenge in designing normalizing flows is finding expressive scalar bijections that remain invertible with tractable Jacobians. Existing approaches face trade-offs: affine transformations are smooth and analytically invertible but lack express...
Attention Consistency Regularization for Interpretable Early-Exit Neural Networks
arXiv:2601.08891v1 Announce Type: new
Abstract: Early-exit neural networks enable adaptive inference by allowing predictions at intermediate layers, reducing computational cost. However, early exits often lack interpretability and may focus on different features than deeper layers, limiting trust a...
Affect and Effect: Limitations of regularisation-based continual learning in EEG-based emotion classification
arXiv:2601.07858v1 Announce Type: new
Abstract: Generalisation to unseen subjects in EEG-based emotion classification remains a challenge due to high inter-and intra-subject variability. Continual learning (CL) poses a promising solution by learning from a sequence of tasks while mitigating catastr...
HOSC: A Periodic Activation with Saturation Control for High-Fidelity Implicit Neural Representations
arXiv:2601.07870v1 Announce Type: new
Abstract: Periodic activations such as sine preserve high-frequency information in implicit neural representations (INRs) through their oscillatory structure, but often suffer from gradient instability and limited control over multi-scale behavior. We introduce...
arXiv:2601.08005v1 Announce Type: new
Abstract: Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications can occur internally when companies deploy highly capable systems within...
The Hessian of tall-skinny networks is easy to invert
arXiv:2601.06096v1 Announce Type: new
Abstract: We describe an exact algorithm for solving linear systems $Hx=b$ where $H$ is the Hessian of a deep net. The method computes Hessian-inverse-vector products without storing the Hessian or its inverse in time and storage that scale linearly in the numb...
Automatic Question Generation for Intuitive Learning Utilizing Causal Graph Guided Chain of Thought Reasoning
arXiv:2601.06098v1 Announce Type: new
Abstract: Intuitive learning is crucial for developing deep conceptual understanding, especially in STEM education, where students often struggle with abstract and interconnected concepts. Automatic question generation has become an effective strategy for perso...
Dynamic Intelligence Ceilings: Measuring Long-Horizon Limits of Planning and Creativity in Artificial Systems
arXiv:2601.06102v1 Announce Type: new
Abstract: Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many syst...
Comment on arXiv:2511.21731v1: Identifying Quantum Structure in AI Language: Evidence for Evolutionary Convergence of Human and Artificial Cognition
arXiv:2601.06104v1 Announce Type: new
Abstract: This note is a friendly technical check of arXiv:2511.21731v1. I highlight a few places where the manuscript's interpretation of (i) the reported CHSH/Bell-type calculations and (ii) Bose--Einstein (BE) fits to rank-frequency data seems to go beyond w...
Stanford’s AI spots hidden disease warnings that show up while you sleep
Stanford researchers have developed an AI that can predict future disease risk using data from just one night of sleep. The system analyzes detailed physiological signals, looking for hidden patterns across the brain, heart, and breathing. It successfully forecast risks for conditions like cancer, d...
Active Sensing Shapes Real-World Decision-Making through Dynamic Evidence Accumulation
arXiv:2601.04214v1 Announce Type: new
Abstract: Human decision-making heavily relies on active sensing, a well-documented cognitive behaviour for evidence gathering to accommodate ever-changing environments. However, its operational mechanism in the real world remains non-trivial. Currently, an in-...
Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference
Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond these metrics, PPG waveforms contain richer physiological information, as recent deep learning (DL) studies demonstrate....
Recent advances in test-time alignment methods, such as Best-of-N sampling, offer a simple and effective way to steer language models (LMs) toward preferred behaviors using reward models (RM). However, these approaches can be computationally expensive, especially when applied uniformly across prompt...
Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts
arXiv:2601.03315v1 Announce Type: new
Abstract: We report a case study of four end-to-end attempts to autonomously generate ML research papers using a pipeline of six LLM agents mapped to stages of the scientific workflow. Of these four, three attempts failed during implementation or evaluation. On...
Aligning Findings with Diagnosis: A Self-Consistent Reinforcement Learning Framework for Trustworthy Radiology Reporting
arXiv:2601.03321v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have shown strong potential for radiology report generation, yet their clinical translation is hindered by architectural heterogeneity and the prevalence of factual hallucinations. Standard supervised fine-tuni...
arXiv:2601.03322v1 Announce Type: new
Abstract: Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. However, their utility is currently limited because EEG decoding often suffer...