OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
arXiv:2603.23938v1 Announce Type: new Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded acoustic control...
Argument Mining as a Text-to-Text Generation Task
arXiv:2603.23949v1 Announce Type: new Abstract: Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures...
The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More
arXiv:2603.23971v1 Announce Type: new Abstract: Developers and consumers increasingly choose reasoning language models (RLMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this question, evaluating...
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping
arXiv:2603.23998v1 Announce Type: new Abstract: Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm, the network structure remains static along the training timeline, and additional computational depth...
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
arXiv:2603.23562v1 Announce Type: new Abstract: Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synthetic tokens or using stronger generators yields diminishing returns below the performance...
Safe Reinforcement Learning with Preference-based Constraint Inference
arXiv:2603.23565v1 Announce Type: new Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions...
AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization
arXiv:2603.23566v1 Announce Type: new Abstract: AscendC (Ascend C) operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck: unlike the CUDA ecosystem, there are few public reference implementations to learn from, and performance hinges on a...
Causal Reconstruction of Sentiment Signals from Sparse News Data
arXiv:2603.23568v1 Announce Type: new Abstract: Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as...
StateLinFormer: Stateful Training Enhancing Long-term Memory in Navigation
arXiv:2603.23571v1 Announce Type: new Abstract: Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are...
Dual-Criterion Curriculum Learning: Application to Temporal Data
arXiv:2603.23573v1 Announce Type: new Abstract: Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and...
CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records
arXiv:2603.23719v1 Announce Type: new Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features...
BXRL: Behavior-Explainable Reinforcement Learning
arXiv:2603.23738v1 Announce Type: new Abstract: A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learning (XRL) methods can answer queries such as "explain this...
Kronecker-Structured Nonparametric Spatiotemporal Point Processes
arXiv:2603.23746v1 Announce Type: new Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical Poisson and Hawkes processes rely on restrictive parametric assumptions that limit their ability to...
Self Paced Gaussian Contextual Reinforcement Learning
arXiv:2603.23755v1 Announce Type: new Abstract: Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper,...
Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncertainty propagation. This paper introduces an uncertainty-aware probabilistic latent transport framework...
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
arXiv:2603.23792v1 Announce Type: new Abstract: Diffusion models often generate novel samples even when the learned score is only \emph{coarse} -- a phenomenon not accounted for by the standard view of diffusion training as density estimation. In this paper, we show...
Deep Neural Regression Collapse
arXiv:2603.23805v1 Announce Type: new Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the...
Circuit Complexity of Hierarchical Knowledge Tracing and Implications for Log-Precision Transformers
arXiv:2603.23823v1 Announce Type: new Abstract: Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens to clarify what is provable about transformer-style computation on deep concept hierarchies. Using recent results...
Why the Maximum Second Derivative of Activations Matters for Adversarial Robustness
arXiv:2603.23860v1 Announce Type: new Abstract: This work investigates the critical role of activation function curvature -- quantified by the maximum second derivative $\max|\sigma''|$ -- in adversarial robustness. Using the Recursive Curvature-Tunable Activation Family (RCT-AF), which enables precise control over curvature...
Can VLMs Reason Robustly? A Neuro-Symbolic Investigation
arXiv:2603.23867v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper, we study covariate shifts in which the...
HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation
arXiv:2603.23871v1 Announce Type: new Abstract: Large language models trained with reinforcement learning (RL) for mathematical reasoning face a fundamental challenge: on problems the model cannot solve at all - "cliff" prompts - the RL gradient vanishes entirely, preventing any learning...
GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference
arXiv:2603.23961v1 Announce Type: new Abstract: Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although microbial communities provide a promising and more cost-effective alternative, reliable inference remains challenging because the...
Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory
arXiv:2603.23967v1 Announce Type: new Abstract: Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation multi-robot systems (T-MRS), e.g., collaborative automatic guided vehicles (AGVs)....
Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions,...
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
arXiv:2603.24033v1 Announce Type: new Abstract: Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence and rely on deterministic single-point predictions, which limits solution diversityand often necessitates extensive downstream search...
Birthright citizenship: more on Pete Patterson’s claims
Attorney Pete Patterson’s latest post on birthright citizenship repeats the biggest mistakes of his original post and also makes some new mistakes, chasing irrelevances and mangling the key legal issues. […]The postBirthright citizenship: more on Pete Patterson’s claimsappeared first onSCOTUSblog.
Justices dubious about “harsh” rules for omissions by bankrupt debtors
Yesterday’s argument in Keathley v. Buddy Ayers Construction displayed a bench almost uniformly skeptical of a lower court’s absolute standard for responding to the failure of a debtor in bankruptcy […]The postJustices dubious about “harsh” rules for omissions by bankrupt...
The least surprising chapter of the Manus story is what’s happening right now
Did anyone think there would not be a reckoning over this tie-up?
Mercor competitor Deccan AI raises $25M, sources experts from India
Deccan AI concentrates its workforce in India to manage quality in a fast-growing but fragmented AI training market.
The AI skills gap is here, says AI company, and power users are pulling ahead
Anthropic finds AI isn’t replacing jobs yet, but early data shows growing inequality as experienced users gain an edge, raising concerns about future displacement and workforce divides.