arXiv:2603.10139v1 Announce Type: cross Abstract: Every formal grammar defines a language and can in principle be used in three ways: to generate strings (production), to recognize them (parsing), or — given only examples — to infer the grammar itself (grammar induction). Generation and recognition are extensionally equivalent — they characterize the same set — but […]
A Platform-Agnostic Multimodal Digital Human Modelling Framework: Neurophysiological Sensing in Game-Based Interaction
arXiv:2603.10680v1 Announce Type: cross Abstract: Digital Human Modelling (DHM) is increasingly shaped by advances in AI, wearable biosensing, and interactive digital environments, particularly in research addressing accessibility and inclusion. However, many AI-enabled DHM approaches remain tightly coupled to specific platforms, tasks, or interpretative pipelines, limiting reproducibility, scalability, and ethical reuse. This paper presents a platform-agnostic […]
A Diffusion Analysis of Policy Gradient for Stochastic Bandits
arXiv:2603.10219v1 Announce Type: cross Abstract: We study a continuous-time diffusion approximation of policy gradient for $k$-armed stochastic bandits. We prove that with a learning rate $eta = O(Delta^2/log(n))$ the regret is $O(k log(k) log(n) / eta)$ where $n$ is the horizon and $Delta$ the minimum gap. Moreover, we construct an instance with only logarithmically many […]
Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors
arXiv:2603.08359v2 Announce Type: replace-cross Abstract: Learning to understand speech appears almost effortless for typically developing infants, yet from an information-processing perspective, acquiring a language from acoustic speech is an enormous challenge. This chapter reviews recent developments in using computational models to understand early language acquisition from speech and audiovisual input. The focus is on self-supervised […]
Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
arXiv:2603.10281v1 Announce Type: cross Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data manifolds used to train the score functions and the geometry of ADMM iterates, especially due […]
Are Video Reasoning Models Ready to Go Outside?
arXiv:2603.10652v1 Announce Type: cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such conditions, their understanding and reasoning degrade substantially, revealing a gap between clean, controlled (i.e., unperturbed) evaluation settings and real-world robustness. To address this limitation, we propose ROVA, a novel training framework that improves […]
Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers
arXiv:2603.10332v1 Announce Type: cross Abstract: While reasoning rerankers, such as Rank1, have demonstrated strong abilities in improving ranking relevance, it is unclear how they perform on other retrieval qualities such as fairness. We conduct the first systematic comparison of fairness between reasoning and non-reasoning rerankers. Using the TREC 2022 Fair Ranking Track dataset, we evaluate […]
A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification
arXiv:2603.07571v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is critical in safety-sensitive applications. While this challenge has been addressed from various perspectives, the influence of training objectives on OOD behavior remains comparatively underexplored. In this paper, we present a systematic comparison of four widely used training objectives: Cross-Entropy Loss, Prototype Loss, Triplet Loss, and Average […]
Reactive Writers: How Co-Writing with AI Changes How We Engage with Ideas
arXiv:2603.10374v1 Announce Type: cross Abstract: Emerging experimental evidence shows that writing with AI assistance can change both the views people express in writing and the opinions they hold afterwards. Yet, we lack substantive understanding of procedural and behavioral changes in co-writing with AI that underlie the observed opinion-shaping power of AI writing tools. We conducted […]
Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions
arXiv:2603.10651v1 Announce Type: cross Abstract: Task and Motion Planning combines high-level task sequencing (what to do) with low-level motion planning (how to do it) to generate feasible, collision-free execution plans. However, in many real-world domains, such as automated warehouses, tasks are predefined, shifting the challenge to if, when, and how to execute them safely and […]
Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder
arXiv:2603.10430v1 Announce Type: cross Abstract: The construction of high quality health indicators (HIs) is crucial for effective prognostics and health management. Although deep learning has significantly advanced HI modeling, existing approaches often struggle with distribution mismatches resulting from varying operating conditions. Although domain adaptation is typically employed to mitigate these shifts, two critical challenges remain: […]
ResearchEnvBench: Benchmarking Agents on Environment Synthesis for Research Code Execution
arXiv:2603.06739v2 Announce Type: replace-cross Abstract: Autonomous agents are increasingly expected to support scientific research, and recent benchmarks report progress in code repair and autonomous experimentation. However, these evaluations typically assume a pre-configured execution environment, which requires resolving complex software dependencies, aligning hardware and framework versions, and configuring distributed execution, yet this capability remains largely unbenchmarked. […]