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...
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...
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...
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...
An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices
arXiv:2601.02641v1 Announce Type: new
Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we in...
mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks
arXiv:2601.02451v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test. We adapt Manifold-Constrained Hyper-Connections (\mhc)~\citep{xie2025mhc}, recently proposed for Transforme...
SLO-Conditioned Action Routing for Retrieval-Augmented Generation: Objective Ablation and Failure Modes
arXiv:2601.00841v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) introduces a practical control problem: retrieval depth and generation behavior must be chosen per query to satisfy service-level objectives (SLOs) such as cost, refusal rate, and hallucination risk. This work mode...
Horizon Reduction as Information Loss in Offline Reinforcement Learning
arXiv:2601.00831v1 Announce Type: new
Abstract: Horizon reduction is a common design strategy in offline reinforcement learning (RL), used to mitigate long-horizon credit assignment, improve stability, and enable scalable learning through truncated rollouts, windowed training, or hierarchical decom...
Value-guided action planning with JEPA world models
arXiv:2601.00844v1 Announce Type: new
Abstract: Building deep learning models that can reason about their environment requires capturing its underlying dynamics. Joint-Embedded Predictive Architectures (JEPA) provide a promising framework to model such dynamics by learning representations and predi...
Semantic Alignment of Multilingual Knowledge Graphs via Contextualized Vector Projections
arXiv:2601.00814v1 Announce Type: new
Abstract: The paper presents our work on cross-lingual ontology alignment system which uses embedding based cosine similarity matching. The ontology entities are made contextually richer by creating descriptions using novel techniques. We use a fine-tuned trans...
MathLedger: A Verifiable Learning Substrate with Ledger-Attested Feedback
arXiv:2601.00816v1 Announce Type: new
Abstract: Contemporary AI systems achieve extraordinary performance yet remain opaque and non-verifiable, creating a crisis of trust for safety-critical deployment. We introduce MathLedger, a substrate for verifiable machine cognition that integrates formal ver...
Evaluating Anomaly Detectors for Simulated Highly Imbalanced Industrial Classification Problems
arXiv:2601.00005v1 Announce Type: new
Abstract: Machine learning offers potential solutions to current issues in industrial systems in areas such as quality control and predictive maintenance, but also faces unique barriers in industrial applications. An ongoing challenge is extreme class imbalance...
Yahtzee: Reinforcement Learning Techniques for Stochastic Combinatorial Games
arXiv:2601.00007v1 Announce Type: new
Abstract: Yahtzee is a classic dice game with a stochastic, combinatorial structure and delayed rewards, making it an interesting mid-scale RL benchmark. While an optimal policy for solitaire Yahtzee can be computed using dynamic programming methods, multiplaye...
Finetuning Large Language Models for Automated Depression Screening in Nigerian Pidgin English: GENSCORE Pilot Study
arXiv:2601.00004v1 Announce Type: new
Abstract: Depression is a major contributor to the mental-health burden in Nigeria, yet screening coverage remains limited due to low access to clinicians, stigma, and language barriers. Traditional tools like the Patient Health Questionnaire-9 (PHQ-9) were val...
IMBWatch -- a Spatio-Temporal Graph Neural Network approach to detect Illicit Massage Business
arXiv:2601.00075v1 Announce Type: new
Abstract: Illicit Massage Businesses (IMBs) are a covert and persistent form of organized exploitation that operate under the facade of legitimate wellness services while facilitating human trafficking, sexual exploitation, and coerced labor. Detecting IMBs is ...
Network Traffic Analysis with Process Mining: The UPSIDE Case Study
arXiv:2512.23718v1 Announce Type: new
Abstract: Online gaming is a popular activity involving the adoption of complex systems and network infrastructures. The relevance of gaming, which generates large amounts of market revenue, drove research in modeling network devices' behavior to evaluate bandw...
A Comprehensive Study of Deep Learning Model Fixing Approaches
arXiv:2512.23745v1 Announce Type: new
Abstract: Deep Learning (DL) has been widely adopted in diverse industrial domains, including autonomous driving, intelligent healthcare, and aided programming. Like traditional software, DL systems are also prone to faults, whose malfunctioning may expose user...
A philosopher at the University of Cambridge says there’s no reliable way to know whether AI is conscious—and that may remain true for the foreseeable future. According to Dr. Tom McClelland, consciousness alone isn’t the ethical tipping point anyway; sentience, the capacity to feel good or bad, is ...
Three-way decision with incomplete information based on similarity and satisfiability
arXiv:2512.21421v1 Announce Type: new
Abstract: Three-way decision is widely applied with rough set theory to learn classification or decision rules. The approaches dealing with complete information are well established in the literature, including the two complementary computational and conceptual...