arXiv:2512.20298v4 Announce Type: replace-cross Abstract: Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. This depth over breadth case study directly compares state-of-the-art LLMs and mental health professionals in assessing Borderline (BPD) and Narcissistic (NPD) Personality Disorders based on Polish-language first-person autobiographical accounts. Within our sample, the […]
Geometric Origin of Exact Mean-Field Reductions: M”obius Symmetry and the Lorentzian Ansatz
arXiv:2605.23669v1 Announce Type: cross Abstract: Low-dimensional descriptions of large systems of coupled oscillators and spiking neurons rely heavily on the Lorentzian Ansatz. We show that its privileged role is geometric rather than heuristic: for the transport induced by Riccati dynamics, the Cauchy-Lorentz family indeed emerges as the unique connected two-dimensional family of continuous probability densities […]
Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior
arXiv:2502.20349v5 Announce Type: replace Abstract: How can cognitive science build generalizable theories that span the full scope of natural situations and behaviors? We argue that progress in Artificial Intelligence (AI) offers timely opportunities for cognitive science to embrace experiments with increasingly naturalistic stimuli, tasks, and behaviors; and computational models that can accommodate these changes. We […]
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
arXiv:2604.24810v3 Announce Type: replace-cross Abstract: Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant challenge. Therefore, smart and adaptive inference strategies that dynamically balance computational cost or latency with predictive accuracy are critical in edge computing scenarios. In this work, we build on Adaptive […]
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
arXiv:2605.06840v5 Announce Type: replace Abstract: Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over future outcomes. Yet whether this deliberation constitutes genuine planning, how it is structured, and what aspects of it drive performance remain poorly understood. In this work, we introduce a new method to […]
Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
arXiv:2512.19184v2 Announce Type: replace-cross Abstract: This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework. Our key development lies in strategically combining a Koopman based approach with existing techniques, achieving tighter generalization guarantees compared to traditional norm-based bounds. To mitigate computational challenges associated […]
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
arXiv:2510.00915v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binarize rewards to $,1$, but imperfect verifiers inevitably introduce emphfalse negatives (rejecting correct answers) and emphfalse positives (accepting incorrect ones). We formalize verifier unreliability as a stochastic reward channel with […]
A Systematic Evaluation of Co-folding Model Representations for Small-Molecule Learning
arXiv:2602.13249v2 Announce Type: replace Abstract: Small-molecule foundation models are typically pretrained on standalone molecular data, unlike vision and language models that often benefit from cross-modal or relational supervision. Protein-ligand co-folding provides a molecular analogue of such supervision by exposing models to atom-level ligand-protein interactions, raising the question of whether co-folding models can yield strong small-molecule […]
Moonwalk: Inverse-Forward Differentiation
arXiv:2402.14212v4 Announce Type: replace-cross Abstract: Backpropagation’s main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth of trainable networks. This raises a fundamental question: can we avoid storing these activations? We address this by revisiting the structure of gradient computation. Backpropagation computes gradients through a sequence of […]
Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment
arXiv:2601.16027v2 Announce Type: replace Abstract: The rise of live streaming has transformed online interaction, enabling massive real-time engagement but also exposing platforms to complex risks such as scams and coordinated malicious behaviors. Detecting these risks is challenging because harmful actions often accumulate gradually and recur across seemingly unrelated streams. To address this, we propose CS-VAR […]
STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction
arXiv:2508.12247v3 Announce Type: replace-cross Abstract: Recently, spatio-temporal time-series prediction has developed rapidly, yet existing deep learning methods struggle with learning complex long-term spatio-temporal dependencies efficiently. The long-term spatio-temporal dependency learning brings two new challenges: 1) The long-term temporal sequence naturally includes multiscale information, which is hard to extract efficiently; 2) The multiscale temporal information from […]
VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning, yet its reliance on external verifiers limits its scalability. Recent findings suggest that RLVR primarily functions by eliciting latent capabilities, motivating the development of verifier-free algorithms. However, in such settings, standard […]