Multi-Step Semantic Reasoning in Generative Retrieval
arXiv:2603.12368v1 Announce Type: cross Abstract: Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries...
No More DeLuLu: Physics-Inspired Kernel Networks for Geometrically-Grounded Neural Computation
arXiv:2603.12276v1 Announce Type: new Abstract: We introduce the yat-product, a kernel operator combining quadratic alignment with inverse-square proximity. We prove it is a Mercer kernel, analytic, Lipschitz on bounded domains, and self-regularizing, admitting a unique RKHS embedding. Neural Matter Networks...
Sinkhorn-Drifting Generative Models
arXiv:2603.12366v1 Announce Type: new Abstract: We establish a theoretical link between the recently proposed "drifting" generative dynamics and gradient flows induced by the Sinkhorn divergence. In a particle discretization, the drift field admits a cross-minus-self decomposition: an attractive term toward...
SpectralGuard: Detecting Memory Collapse Attacks in State Space Models
arXiv:2603.12414v1 Announce Type: new Abstract: State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a critical safety vulnerability. We show that the spectral radius rho(A-bar) of the discretized transition operator governs...
Overcoming the Modality Gap in Context-Aided Forecasting
arXiv:2603.12451v1 Announce Type: new Abstract: Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. However, recent empirical studies reveal a puzzling gap: multimodal models often fail to outperform their...
Probing Length Generalization in Mamba via Image Reconstruction
arXiv:2603.12499v1 Announce Type: new Abstract: Mamba has attracted widespread interest as a general-purpose sequence model due to its low computational complexity and competitive performance relative to transformers. However, its performance can degrade when inference sequence lengths exceed those seen during...
Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty
arXiv:2603.12507v1 Announce Type: new Abstract: Minimising a spectral risk objective, defined as a convex combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail...
Learning Pore-scale Multiphase Flow from 4D Velocimetry
arXiv:2603.12516v1 Announce Type: new Abstract: Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce...
CALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning
arXiv:2603.12543v1 Announce Type: new Abstract: Distributed reinforcement learning policies face network delays, jitter, and packet loss when deployed across edge devices and cloud servers. Standard RL training assumes zero-latency interaction, causing severe performance degradation under realistic network conditions. We introduce...
Asymptotic and Finite-Time Guarantees for Langevin-Based Temperature Annealing in InfoNCE
arXiv:2603.12552v1 Announce Type: new Abstract: The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain poorly understood. We provide a theoretical analysis by modeling embedding evolution under Langevin dynamics...
CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction
arXiv:2603.12591v1 Announce Type: new Abstract: Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a practical framework that enables each client perform structured, device-specific pruning guided...
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...
Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback
arXiv:2603.12595v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational...
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...
Feynman: Knowledge-Infused Diagramming Agent for Scalable Visual Designs
arXiv:2603.12597v1 Announce Type: new Abstract: Visual design is an essential application of state-of-the-art multi-modal AI systems. Improving these systems requires high-quality vision-language data at scale. Despite the abundance of internet image and text data, knowledge-rich and well-aligned image-text pairs are...
FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
arXiv:2603.12612v1 Announce Type: new Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a formidable challenge, as the ``curse of dimensionality'' induces severe exploration inefficiency and training instability in expansive action spaces. Consequently, recent high-throughput paradigms have...
Sobolev--Ricci Curvature
arXiv:2603.12652v1 Announce Type: new Abstract: Ricci curvature is a fundamental concept in differential geometry for encoding local geometric structure, and its graph-based analogues have recently gained prominence as practical tools for reweighting, pruning, and reshaping network geometry. We propose Sobolev-Ricci...
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...
Google, Accel India accelerator chooses 5 startups and none are ‘AI wrappers’
Google and Accel say about 70% of AI startup pitches tied to India were "wrappers" as they reviewed more than 4,000 applications for their Atoms cohort.
ByteDance reportedly pauses global launch of its Seedance 2.0 video generator
The company is reportedly delaying the launch as its engineers and lawyers work to avert further legal issues.
Wiz investor unpacks Google’s $32B acquisition
Shardul Shah of Index Ventures walks us through Google's biggest acquisition ever.
US Army announces contract with Anduril worth up to $20B
The Army described this as a single enterprise contract consolidating more than 120 separate "procurement actions."
Meta reportedly considering layoffs that could affect 20% of the company
These layoffs could help Facebook's parent company offset its aggressive spending on AI infrastructure, as well as AI-related acquisitions and hiring.
No skin in the game: why agentic AI requires principal-agent governance
‘Not built right the first time’ — Musk’s xAI is starting over again, again
The AI lab is revamping its effort to build an AI coding tool, with two new executives joining from Cursor.
Nyne, founded by a father-son duo, gives AI agents the human context they’re missing
The data infrastructure startup raised $5.3 million in seed funding led by Wischoff Ventures and South Park Commons.
Steven Spielberg says he’s ‘never used AI’ in any of his films
At SXSW, Steven Spielberg said AI has uses in many fields, but not when it comes to replacing creative people in film and TV writing.
The biggest AI stories of the year (so far)
The AI industry is constantly churning out news, like major acquisitions, indie developer successes, public outcry, and existentially dangerous contract negotiations.