The Singular Role of Public Pension Funds in Corporate Governance
Introduction U.S. public pension funds manage more than $6 trillion in assets.[1] The law, policy, and public debates about how they should manage this money are based on a theoretical model that is descriptively inaccurate and yields policy proposals that...
A foundation model for electrodermal activity data
arXiv:2603.16878v1 Announce Type: new Abstract: Foundation models have recently extended beyond natural language and vision to timeseries domains, including physiological signals. However, progress in electrodermal activity (EDA) modeling is hindered by the absence of large-scale, curated, and openly accessible datasets....
HoloByte: Continuous Hyperspherical Distillation for Tokenizer-Free Modeling
arXiv:2603.16917v1 Announce Type: new Abstract: Sequence modeling universally relies on discrete subword tokenization to circumvent the $\mathcal{O}(N^2)$ computational intractability of native byte-level attention. However, this heuristic quantization imposes artificial morphological boundaries, enforces vocabulary dependence, and fractures the continuity of the...
Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
arXiv:2603.16951v1 Announce Type: new Abstract: Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a...
Formal verification of tree-based machine learning models for lateral spreading
arXiv:2603.16983v1 Announce Type: new Abstract: Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Current remedies (post-hoc explainability, such as SHAP and LIME, and training-time constraints) either...
Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
arXiv:2603.16985v1 Announce Type: new Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibility. However, many Transformer variants implicitly assume stationarity and stable temporal dynamics -- assumptions routinely violated in financial...
Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Models
arXiv:2603.17044v1 Announce Type: new Abstract: Unified multimodal models share a language model backbone for both understanding and generating images. Can DPO align both capabilities simultaneously? We present the first systematic study of this question, applying DPO to Janus-Pro at 1B...
PRISM: Demystifying Retention and Interaction in Mid-Training
arXiv:2603.17074v1 Announce Type: new Abstract: We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and...
CircuitBuilder: From Polynomials to Circuits via Reinforcement Learning
arXiv:2603.17075v1 Announce Type: new Abstract: Motivated by auto-proof generation and Valiant's VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and multiplication gates. We formulate this problem as a single-player game,...
SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval
arXiv:2603.17109v1 Announce Type: new Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction. Most existing Brain-Computer Interface (BCI) approaches rely on memory-intensive fine-tuning of Large Language...
Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
arXiv:2603.17126v1 Announce Type: new Abstract: Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joint source-channel coding (DeepJSCC) schemes mainly optimize pixel-wise losses and provide no explicit...
Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse...
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
arXiv:2603.17148v1 Announce Type: new Abstract: Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This...
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
arXiv:2603.17175v1 Announce Type: new Abstract: Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn non-physical relationships that reduce their reliability in natural hazard applications. This study presents a...
Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
arXiv:2603.17199v1 Announce Type: new Abstract: Large language models (LLMs) can produce chains of thought (CoT) that do not accurately reflect the actual factors driving their answers. In multiple-choice settings with an injected hint favoring a particular option, models may shift...
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
arXiv:2603.17246v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contributing to cross-modal separation known as the modality gap. While this phenomenon has...
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep learning methods often ignore underlying cardiac pathology losing vital morphology in precordial leads. We...
Classifier Pooling for Modern Ordinal Classification
arXiv:2603.17278v1 Announce Type: new Abstract: Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic...
Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning
arXiv:2603.17365v1 Announce Type: new Abstract: Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask two questions: what correlation geometry should internal noise have, and is the implemented perturbation compatible...
Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models
arXiv:2603.17384v1 Announce Type: new Abstract: Current continuous generative models (e.g., Diffusion Models, Flow Matching) implicitly assume that locally consistent causal mechanisms naturally yield globally coherent counterfactuals. In this paper, we prove that this assumption fails fundamentally when the causal graph...
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
arXiv:2603.17403v1 Announce Type: new Abstract: Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed...
Causal Representation Learning on High-Dimensional Data: Benchmarks, Reproducibility, and Evaluation Metrics
arXiv:2603.17405v1 Announce Type: new Abstract: Causal representation learning (CRL) models aim to transform high-dimensional data into a latent space, enabling interventions to generate counterfactual samples or modify existing data based on the causal relationships among latent variables. To facilitate the...
Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control
arXiv:2603.17468v1 Announce Type: new Abstract: We present GuidedSAC, a novel reinforcement learning (RL) algorithm that facilitates efficient exploration in vast state-action spaces. GuidedSAC leverages large language models (LLMs) as intelligent supervisors that provide action-level guidance for the Soft Actor-Critic (SAC)...
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
arXiv:2603.17507v1 Announce Type: new Abstract: Federated Learning (FL) enables privacy-preserving intelligence on Internet of Things (IoT) devices but incurs a significant carbon footprint due to the high energy cost of frequent uplink transmission. While pre-trained models are increasingly available on...
Nvidia is quietly building a multibillion-dollar behemoth to rival its chips business
Nvidia's networking business raked in $11 billion last quarter despite getting significantly less fanfare than chips and gaming.
Patreon CEO calls AI companies’ fair use argument ‘bogus,’ says creators should be paid
Patreon CEO Jack Conte says AI companies should pay creators for training data, arguing their fair use defense falls apart when they license content from major publishers.
Rebel Audio is a new AI podcasting tool aimed at first-time creators
Rebel Audio is a new all-in-one podcasting tool that allows creators to record podcasts, edit, clip content for social, and publish episodes, all without ever leaving the platform.
The leaderboard “you can’t game,” funded by the companies it ranks
Artificial intelligence models are multiplying fast, and competition is stiff. With so many players crowding the space, which one will be the best — and who decides that? Arena, formerly LM Arena, has emerged as the de facto public leaderboard...
The PhD students who became the judges of the AI industry
Artificial intelligence models are multiplying fast, and competition is stiff. With so many players crowding the space, which one will be the best — and who decides that? Arena, formerly LM Arena, has emerged as the de facto public leaderboard...