Learning-free L2-Accented Speech Generation using Phonological Rules
arXiv:2603.07550v1 Announce Type: new Abstract: Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that...
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
arXiv:2603.07841v1 Announce Type: new Abstract: Recent advances in large language models has strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an unseen and unlabeled...
FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures
arXiv:2603.06600v1 Announce Type: new Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates...
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
arXiv:2603.06605v1 Announce Type: new Abstract: Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, the input layout becomes a key design choice. Grids expose time$\times$variable structure but require...
OptiRoulette Optimizer: A New Stochastic Meta-Optimizer for up to 5.3x Faster Convergence
arXiv:2603.06613v1 Announce Type: new Abstract: This paper presents OptiRoulette, a stochastic meta-optimizer that selects update rules during training instead of fixing a single optimizer. The method combines warmup optimizer locking, random sampling from an active optimizer pool, compatibility-aware learning-rate scaling...
Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modeling
arXiv:2603.06615v1 Announce Type: new Abstract: For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden and data imbalance. To this end, we propose an Annealed Co-Generation (ACG)...
Grouter: Decoupling Routing from Representation for Accelerated MoE Training
arXiv:2603.06626v1 Announce Type: new Abstract: Traditional Mixture-of-Experts (MoE) training typically proceeds without any structural priors, effectively requiring the model to simultaneously train expert weights while searching for an optimal routing policy within a vast combinatorial space. This entanglement often leads...
Graph Property Inference in Small Language Models: Effects of Representation and Inference Strategy
arXiv:2603.06635v1 Announce Type: new Abstract: Recent progress in language modeling has expanded the range of tasks that can be approached through natural language interfaces, including problems that require structured reasoning. However, it remains unclear how effectively limited-capacity language models can...
SR-TTT: Surprisal-Aware Residual Test-Time Training
arXiv:2603.06642v1 Announce Type: new Abstract: Test-Time Training (TTT) language models achieve theoretically infinite context windows with an O(1) memory footprint by replacing the standard exact-attention KV-cache with hidden state ``fast weights'' W_fast updated via self-supervised learning during inference. However, pure...
One step further with Monte-Carlo sampler to guide diffusion better
arXiv:2603.06685v1 Announce Type: new Abstract: Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches. However, existing methodologies utilizing posterior sam- pling typically confront a substantial estimation error, which results in inaccu-...
Bi Directional Feedback Fusion for Activity Aware Forecasting of Indoor CO2 and PM2.5
arXiv:2603.06724v1 Announce Type: new Abstract: Indoor air quality (IAQ) forecasting plays a critical role in safeguarding occupant health, ensuring thermal comfort, and supporting intelligent building control. However, predicting future concentrations of key pollutants such as carbon dioxide (CO2) and fine...
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
arXiv:2603.06726v1 Announce Type: new Abstract: Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series foundation models (TSFMs) effectively capture temporal dependencies, they typically underutilize cross-variate correlations and non-periodic patterns that...
Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment
arXiv:2603.06727v1 Announce Type: new Abstract: Current safety alignment methods encode safe behavior implicitly within model parameters, creating a fundamental opacity: we cannot easily inspect why a model refuses a request, nor intervene when its safety judgments fail. We propose Safe...
Heterogeneous Decentralized Diffusion Models
arXiv:2603.06741v1 Announce Type: new Abstract: Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly coupled clusters, limiting participation to well-resourced institutions. While Decentralized Diffusion Models (DDM) enable training multiple experts in isolation, existing approaches require 1176 GPU-days...
Latent Autoencoder Ensemble Kalman Filter for Data assimilation
arXiv:2603.06752v1 Announce Type: new Abstract: The ensemble Kalman filter (EnKF) is widely used for data assimilation in high-dimensional systems, but its performance often deteriorates for strongly nonlinear dynamics due to the structural mismatch between the Kalman update and the underlying...
Anthropic launches code review tool to check flood of AI-generated code
Anthropic launched Code Review in Claude Code, a multi-agent system that automatically analyzes AI-generated code, flags logic errors, and helps enterprise developers manage the growing volume of code produced with AI.
Sandberg, Clegg join Nscale board as this ‘Stargate Norway’ startup hits $14.6B valuation
Nvidia-backed British AI infrastructure startup Nscale has raised another megaround of $2 billion.
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
arXiv:2603.05607v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels. Meanwhile, millions of unannotated 3D meshes...
Model Change for Description Logic Concepts
arXiv:2603.05562v1 Announce Type: cross Abstract: We consider the problem of modifying a description logic concept in light of models represented as pointed interpretations. We call this setting model change, and distinguish three main kinds of changes: eviction, which consists of...
On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction
arXiv:2603.05532v1 Announce Type: cross Abstract: Despite continuing hype about the role of AI in drug discovery, no "AI-discovered drugs" have so far received regulatory approval. Here we assess one of the latest AI based tools in this domain. The ability...
Exploring Human-in-the-Loop Themes in AI Application Development: An Empirical Thematic Analysis
arXiv:2603.05510v1 Announce Type: cross Abstract: Developing and deploying AI applications in organizations is challenging when human decision authority and oversight are underspecified across the system lifecycle. Although Human-in-the-Loop (HITL) and Human-Centered AI (HCAI) principles are widely acknowledged, operational guidance for...
RoboLayout: Differentiable 3D Scene Generation for Embodied Agents
arXiv:2603.05522v1 Announce Type: new Abstract: Recent advances in vision language models (VLMs) have shown strong potential for spatial reasoning and 3D scene layout generation from open-ended language instructions. However, generating layouts that are not only semantically coherent but also feasible...
Reasoning Models Struggle to Control their Chains of Thought
arXiv:2603.05706v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what they verbalize in their CoT, it could undermine CoT monitorability. To...
From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews
arXiv:2603.05514v1 Announce Type: cross Abstract: Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work....
On the Value of Tokeniser Pretraining in Physics Foundation Models
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the...
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
arXiv:2603.06333v1 Announce Type: new Abstract: Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a practical framework to monitor and control...
Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility
arXiv:2603.05581v1 Announce Type: cross Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture these multi-scale dynamics across multiple travel modes....
When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On
arXiv:2603.05659v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But...
Verify as You Go: An LLM-Powered Browser Extension for Fake News Detection
arXiv:2603.05519v1 Announce Type: new Abstract: The rampant spread of fake news in the digital age poses serious risks to public trust and democratic institutions, underscoring the need for effective, transparent, and user-centered detection tools. Existing browser extensions often fall short...
CodeScout: Contextual Problem Statement Enhancement for Software Agents
arXiv:2603.05744v1 Announce Type: new Abstract: Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements specification. Recent analysis of software engineering agents reveals that failures on such underspecified requests are highly correlated...