Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
arXiv:2603.12556v1 Announce Type: new Abstract: We establish empirical scaling laws for Single-Layer Physics-Informed Neural Networks on canonical nonlinear PDEs. We identify a dual optimization failure: (i) a baseline pathology, where the solution error fails to decrease with network width, even...
Lyapunov Stable Graph Neural Flow
arXiv:2603.12557v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are highly vulnerable to adversarial perturbations in both topology and features, making the learning of robust representations a critical challenge. In this work, we bridge GNNs with control theory to introduce...
A Spectral Revisit of the Distributional Bellman Operator under the Cram\'er Metric
arXiv:2603.12576v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) studies the evolution of full return distributions under Bellman updates rather than focusing on expected values. A classical result is that the distributional Bellman operator is contractive under the Cram\'er metric,...
Maximizing Incremental Information Entropy for Contrastive Learning
arXiv:2603.12594v1 Announce Type: new Abstract: Contrastive learning has achieved remarkable success in self-supervised representation learning, often guided by information-theoretic objectives such as mutual information maximization. Motivated by the limitations of static augmentations and rigid invariance constraints, we propose IE-CL (Incremental-Entropy...
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
arXiv:2603.12596v1 Announce Type: new Abstract: Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, creating path-dependent noise. To understand this drift, we can use...
Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation
arXiv:2603.12618v1 Announce Type: new Abstract: Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental...
Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
arXiv:2603.12635v1 Announce Type: new Abstract: High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate models. Most surrogate models for such systems are deterministic, for example when neural operators are involved....
LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
arXiv:2603.12645v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) based Large Language Models (LLMs) have demonstrated impressive performance and computational efficiency. However, their deployment is often constrained by substantial memory demands, primarily due to the need to load numerous expert modules. While...
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
arXiv:2603.12666v1 Announce Type: new Abstract: Retrosynthesis prediction is a core task in organic synthesis that aims to predict reactants for a given product molecule. Traditionally, chemists select a plausible bond disconnection and derive corresponding reactants, which is time-consuming and requires...
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
arXiv:2603.12676v1 Announce Type: new Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also predict...
Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
arXiv:2603.12684v1 Announce Type: new Abstract: Federated Clustering (FC) is an emerging and promising solution in exploring data distribution patterns from distributed and privacy-protected data in an unsupervised manner. Existing FC methods implicitly rely on the assumption that clients are with...
No skin in the game: why agentic AI requires principal-agent governance
The wild six weeks for NanoClaw’s creator that led to a deal with Docker
Gavriel Cohen is living an open source developer's dream as his project has achieved acclaim and a partnership with Docker in a matter of weeks.
Spotify will let you edit your Taste Profile to control your recommendations
When you edit your Taste Profile, you'll impact your personalized playlists like Discover Weekly, recommendations, and Wrapped.
Peacock expands into AI-driven video, mobile-first live sports, and gaming
Peacock is betting on new AI-powered video experiences, vertical clips, and mobile games to help its growth.
Before quantum computing arrives, this startup wants enterprises already running on it
After selling his AI startup to AMD for $665 million, Peter Sarlin is back with Qutwo, a new venture building the infrastructure it believes enterprises will need when quantum computing finally arrives.
Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes
arXiv:2603.11594v1 Announce Type: new Abstract: Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using...
Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
arXiv:2603.11299v1 Announce Type: new Abstract: This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a...
COMPASS: The explainable agentic framework for Sovereignty, Sustainability, Compliance, and Ethics
arXiv:2603.11277v1 Announce Type: new Abstract: The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, and ethical alignment. Whilst existing frameworks address individual dimensions in isolation, no unified architecture systematically...
Speculative Decoding Scaling Laws (SDSL): Throughput Optimization Made Simple
arXiv:2603.11053v1 Announce Type: new Abstract: Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- mental approach to optimize the throughput of the inference pipeline, which involves LLM training and...
Mind the Sim2Real Gap in User Simulation for Agentic Tasks
arXiv:2603.11245v1 Announce Type: new Abstract: As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, serving two roles: generating user turns and providing evaluation signals. Yet, these simulations are frequently...
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
arXiv:2603.11399v1 Announce Type: new Abstract: Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions,...
A Semi-Decentralized Approach to Multiagent Control
arXiv:2603.11802v1 Announce Type: new Abstract: We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas semi-Markov control admits a distribution over time for agent actions, semi-Markov communication, or what we...
CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
arXiv:2603.11863v1 Announce Type: new Abstract: The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of such systems is hindered by the...
VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought
arXiv:2603.11631v1 Announce Type: new Abstract: Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major...
See, Symbolize, Act: Grounding VLMs with Spatial Representations for Better Gameplay
arXiv:2603.11601v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can...
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
arXiv:2603.11756v1 Announce Type: new Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign...
TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting
arXiv:2603.11352v1 Announce Type: new Abstract: Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelity but scale poorly with sequence length, whereas fixed-length patching improves efficiency by imposing uniform boundaries that may...
Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation
arXiv:2603.11342v1 Announce Type: new Abstract: The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been proposed to interpret these models, the...
Artificial Intelligence for Sentiment Analysis of Persian Poetry
arXiv:2603.11254v1 Announce Type: new Abstract: Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in...