arXiv:2601.20126v1 Announce Type: cross Abstract: Large Language Models (LLMs) often produce hallucinated or unverifiable content, undermining their reliability in factual domains. This work investigates Reinforcement Learning with Verifiable Rewards (RLVR) as a training paradigm that explicitly rewards abstention (“I don’t know”) alongside correctness to promote intellectual humility. We fine-tune and evaluate Granite-3.3-2B-Instruct and Qwen-3-4B-Instruct on […]
Large language models accurately predict public perceptions of support for climate action worldwide
arXiv:2601.20141v1 Announce Type: cross Abstract: Although most people support climate action, widespread underestimation of others’ support stalls individual and systemic changes. In this preregistered experiment, we test whether large language models (LLMs) can reliably predict these perception gaps worldwide. Using country-level indicators and public opinion data from 125 countries, we benchmark four state-of-the-art LLMs against […]
Meta-Cognitive Reinforcement Learning with Self-Doubt and Recovery
arXiv:2601.20193v1 Announce Type: cross Abstract: Robust reinforcement learning methods typically focus on suppressing unreliable experiences or corrupted rewards, but they lack the ability to reason about the reliability of their own learning process. As a result, such methods often either overreact to noise by becoming overly conservative or fail catastrophically when uncertainty accumulates. In this […]
Implications of temporal sampling in voltage imaging microscopy
arXiv:2601.20236v1 Announce Type: cross Abstract: Significance: Voltage imaging microscopy has emerged as a powerful tool to investigate neural activity both in vivo and in vitro. Various imaging approaches have been developed, including point-scanning, line-scanning and wide-field microscopes, however the effects of their different temporal sampling methods on signal fidelity have not yet been fully investigated. […]
Robust SDE Parameter Estimation Under Missing Time Information Setting
arXiv:2601.20268v1 Announce Type: cross Abstract: Recent advances in stochastic differential equations (SDEs) have enabled robust modeling of real-world dynamical processes across diverse domains, such as finance, health, and systems biology. However, parameter estimation for SDEs typically relies on accurately timestamped observational sequences. When temporal ordering information is corrupted, missing, or deliberately hidden (e.g., for privacy), […]
Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics — Rotating Detonation Engines
arXiv:2601.20295v1 Announce Type: cross Abstract: Bridging the sim2real gap between computationally inexpensive models and complex physical systems remains a central challenge in machine learning applications to engineering problems, particularly in multi-scale settings where reduced-order models typically capture only dominant dynamics. In this work, we present Cheap2Rich, a multi-scale data assimilation framework that reconstructs high-fidelity state […]
Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction
arXiv:2601.20304v1 Announce Type: cross Abstract: The application of iodinated contrast media (ICM) improves the sensitivity and specificity of computed tomography (CT) for a wide range of clinical indications. However, overdose of ICM can cause problems such as kidney damage and life-threatening allergic reactions. Deep learning methods can generate CT images of normal-dose ICM from low-dose […]
Demonstration-Free Robotic Control via LLM Agents
arXiv:2601.20334v1 Announce Type: cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift. We investigate whether general-purpose large language model (LLM) agent frameworks, originally developed for software engineering, can serve as an alternative control paradigm for embodied […]
Switchcodec: Adaptive residual-expert sparse quantization for high-fidelity neural audio coding
arXiv:2601.20362v1 Announce Type: cross Abstract: Recent neural audio compression models often rely on residual vector quantization for high-fidelity coding, but using a fixed number of per-frame codebooks is suboptimal for the wide variability of audio content-especially for signals that are either very simple or highly complex. To address this limitation, we propose SwitchCodec, a neural […]
GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions
arXiv:2601.20402v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful learning tools, but they lack awareness of learners’ cognitive and physiological states, limiting their adaptability to the user’s learning style. Contemporary learning techniques primarily focus on structured learning paths, knowledge tracing, and generic adaptive testing but fail to address real-time learning challenges […]
Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion
arXiv:2601.20422v1 Announce Type: cross Abstract: Modern content platforms offer paid promotion to mitigate cold start by allocating exposure via auctions. Our empirical analysis reveals a counterintuitive flaw in this paradigm: while promotion rescues low-to-medium quality content, it can harm high-quality content by forcing exposure to suboptimal audiences, polluting engagement signals and downgrading future recommendation. We […]
Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI
arXiv:2601.20503v1 Announce Type: cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are imaging features associated with cerebral small vessel disease (SVD) that are visible on brain magnetic resonance imaging (MRI) scans. The development and validation of deep learning models to segment and differentiate these features is difficult because they visually confound each […]