Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning
arXiv:2603.22292v1 Announce Type: new Abstract: Sequential decision making using Markov Decision Process underpins many realworld applications. Both model-based and model free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often...
Efficient Embedding-based Synthetic Data Generation for Complex Reasoning Tasks
arXiv:2603.22294v1 Announce Type: new Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an effective approach to improve the performance of smaller but more resource and compute efficient LLMs through fine-tuning....
Between the Layers Lies the Truth: Uncertainty Estimation in LLMs Using Intra-Layer Local Information Scores
arXiv:2603.22299v1 Announce Type: new Abstract: Large language models (LLMs) are often confidently wrong, making reliable uncertainty estimation (UE) essential. Output-based heuristics are cheap but brittle, while probing internal representations is effective yet high-dimensional and hard to transfer. We propose a...
Latent Semantic Manifolds in Large Language Models
arXiv:2603.22301v1 Announce Type: new Abstract: Large Language Models (LLMs) perform internal computations in continuous vector spaces yet produce discrete tokens -- a fundamental mismatch whose geometric consequences remain poorly understood. We develop a mathematical framework that interprets LLM hidden states...
Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models
arXiv:2603.22303v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are accurate, lightweight, and broadly applicable. Since an LLM with a prompt defines a conditional distribution, we argue that...
Full waveform inversion method based on diffusion model
arXiv:2603.22307v1 Announce Type: new Abstract: Seismic full-waveform inversion is a core technology for obtaining high-resolution subsurface model parameters. However, its highly nonlinear characteristics and strong dependence on the initial model often lead to the inversion process getting trapped in local...
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching
arXiv:2603.22309v1 Announce Type: new Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of deep neural networks to learn solution operators of PDEs has introduced great potential. In this paper, we present UniFluids, a...
Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
arXiv:2603.22314v1 Announce Type: new Abstract: Tropical cyclones (TCs) pose severe threats to life, infrastructure, and economies in tropical and subtropical regions, underscoring the critical need for accurate and timely forecasts of both track and intensity. Recent advances in AI-based weather...
A graph neural network based chemical mechanism reduction method for combustion applications
arXiv:2603.22318v1 Announce Type: new Abstract: Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are computationally prohibitive. To address this challenge, we present two data-driven...
Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
arXiv:2603.22320v1 Announce Type: new Abstract: While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use...
DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression
arXiv:2603.22324v1 Announce Type: new Abstract: We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge acquired during post-training. Standard quantization objectives minimize reconstruction error but are agnostic to the base model, allowing quantization noise to disproportionately...
Hybrid Associative Memories
arXiv:2603.22325v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orthogonal mechanisms: RNNs compress the entire past into a fixed-size state,...
A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
arXiv:2603.22326v1 Announce Type: new Abstract: Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs)....
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
arXiv:2603.22328v1 Announce Type: new Abstract: Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predictive confidence remains a critical challenge. This issue arises in scenarios where the likelihood...
Conformal Risk Control for Safety-Critical Wildfire Evacuation Mapping: A Comparative Study of Tabular, Spatial, and Graph-Based Models
arXiv:2603.22331v1 Announce Type: new Abstract: Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on how much fire spread is missed. Despite extensive work on wildfire spread prediction using deep learning, no...
Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms
arXiv:2603.22332v1 Announce Type: new Abstract: Data imputation is a cornerstone technique for handling missing values in real-world datasets, which are often plagued by missingness. Despite recent progress, prior studies on Large Language Models-based imputation remain limited by scalability challenges, restricted...
Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
arXiv:2603.22333v1 Announce Type: new Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head...
Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
arXiv:2603.22339v1 Announce Type: new Abstract: Chinchilla Approach 2 is among the most widely used methods for fitting neural scaling laws. Its parabolic approximation introduces systematic biases in compute-optimal allocation estimates, even on noise-free synthetic data. Applied to published Llama 3...
First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution
arXiv:2603.22346v1 Announce Type: new Abstract: We isolate and empirically characterize first-mover bias -- a path-dependent concentration of feature importance caused by sequential residual fitting in gradient boosting -- as a specific mechanistic cause of the well-known instability of SHAP-based feature...
COMPASS-Hedge: Learning Safely Without Knowing the World
arXiv:2603.22348v1 Announce Type: new Abstract: Online learning algorithms often faces a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing baseline safety against a fixed comparator. While existing methods excel in one or two of these regimes,...
Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework
arXiv:2603.22362v1 Announce Type: new Abstract: Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging, and non-destructive testing. Conventional FWI methods are limited by their notorious sensitivity...
FAAR: Format-Aware Adaptive Rounding for NVFP4
arXiv:2603.22370v1 Announce Type: new Abstract: Deploying large language models (LLMs) on edge devices requires extremely low-bit quantization. Ultra-low precision formats such as NVFP4 offer a promising solution for reducing memory footprint and accelerating computation. However, existing quantization methods typically rely...
Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure
arXiv:2603.22384v1 Announce Type: new Abstract: Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience,...
Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization
arXiv:2603.22429v1 Announce Type: new Abstract: Symbolic regression aims to discover human-interpretable equations that explain observational data. However, existing approaches rely heavily on discrete structure search (e.g., genetic programming), which often leads to high computational cost, unstable performance, and limited scalability...
Model Predictive Control with Differentiable World Models for Offline Reinforcement Learning
arXiv:2603.22430v1 Announce Type: new Abstract: Offline Reinforcement Learning (RL) aims to learn optimal policies from fixed offline datasets, without further interactions with the environment. Such methods train an offline policy (or value function), and apply it at inference time without...
SkillRouter: Retrieve-and-Rerank Skill Selection for LLM Agents at Scale
arXiv:2603.22455v1 Announce Type: new Abstract: As LLM agent ecosystems grow, the number of available skills (tools, plugins) has reached tens of thousands, making it infeasible to inject all skills into an agent's context. This creates a need for skill routing...
Electronic Frontier Foundation to swap leaders as AI, ICE fights escalate
Public interest in government tech abuses is peaking. EFF's new leader plans to build on that.
Kentucky woman rejects $26M offer to turn her farm into a data center
A "major artificial intelligence company" reportedly offered a Kentucky family $26 million to build a data center on their farm.
Anthropic hands Claude Code more control, but keeps it on a leash
Anthropic’s new auto mode for Claude Code lets AI execute tasks with fewer approvals, reflecting a broader shift toward more autonomous tools that balance speed with safety through built-in safeguards.
OpenAI’s plans to make ChatGPT more like Amazon aren’t going so well
OpenAI says it's moving away from Instant Checkout, which allowed users to buy items directly through the ChatGPT interface.