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...
Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrained Fusion
arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific...
Instruction-Tuned, but Not More Verifiable Instruction-Following: A Cross-Task Diagnosis for LoRA Adapters
arXiv:2603.22379v1 Announce Type: new Abstract: Adapters are often selected and deployed based on nominal labels (e.g., instruction-tuned), which implicitly suggest what capability improves after adaptation. We test whether nominal training objectives reliably align with realized cross-task capability gains by evaluating...
Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data
arXiv:2603.22380v1 Announce Type: new Abstract: Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, in practical scenarios, measurements are often contaminated by noise and limited by sparse sampling, which...
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...
A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning
arXiv:2603.22465v1 Announce Type: new Abstract: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K magnitude pruning effectively reduces the communication payload, it remains inherently energy-agnostic. It assumes all parameter...
Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
arXiv:2603.22525v1 Announce Type: new Abstract: Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time field reconstruction from sparse sensor measurements. Yet their robustness to adversarial perturbations remains uncharacterized, a...
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
arXiv:2603.22530v1 Announce Type: new Abstract: Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their...
A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks
arXiv:2603.22586v1 Announce Type: new Abstract: In-context learning (ICL) allows a model to adapt at inference time by conditioning on examples rather than updating parameters. Existing time-series foundation models use implicit positional context, retrieval, or task-specific objectives, but rarely explicit instruction-conditioned...
The 14th Amendment does not codify English principles of subjectship: A brief reply to the Amar brothers
Professors Akhil and Vikram Amar have responded to my recent post arguing that the 14th Amendment does not grant automatic citizenship to the children of temporary visitors to the United […]The postThe 14th Amendment does not codify English principles of...
Court appears likely to side with Trump administration on rights of asylum seekers
The Supreme Court on Tuesday appeared likely to uphold the federal government’s policy of systematically turning back asylum seekers before they can reach the U.S. border with Mexico. During roughly […]The postCourt appears likely to side with Trump administration on...
Justice Scalia’s uncertain legacy
Controlling Opinions is a recurring series by Richard Re that explores the interaction of law, ideology, and discretion at the Supreme Court. On the surface, Justice Antonin Scalia’s legacy has […]The postJustice Scalia’s uncertain legacyappeared first onSCOTUSblog.
Temporary Protected Status and the Supreme Court: an explainer
The Supreme Court announced last week that it will hear argument in late April on the Trump administration’s effort to remove protected immigration status from Syrian and Haitian nationals. Its […]The postTemporary Protected Status and the Supreme Court: an explainerappeared...
SCOTUStoday for Tuesday, March 24
Citizens United v. FEC, a major case on political spending, was argued for the first time on this day in 2009. After reargument approximately six months later, the court in […]The postSCOTUStoday for Tuesday, March 24appeared first onSCOTUSblog.
With $3.5B in fresh capital, Kleiner Perkins is going all in on AI
The fundraise includes $1 billion for investing in early-stage startups, and $2.5 billion for late-stage growth businesses.
Arm is releasing the first in-house chip in its 35-year history
Arm is producing its own CPU for the first time. It developed the CPU with Meta, which is also the chip's first customer.
Agile Robots becomes the latest robotics company to partner with Google DeepMind
Agile Robots will incorporate Google DeepMind's robotics foundation models into its bots while collecting data for the AI research lab.
gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs
arXiv:2603.20948v1 Announce Type: new Abstract: gUFO is a lightweight implementation of the Unified Foundational Ontology (UFO) suitable for Semantic Web OWL 2 DL applications. UFO is a mature foundational ontology with a rich axiomatization and that has been employed in...
ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture
arXiv:2603.21340v1 Announce Type: new Abstract: This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model...
Graph of States: Solving Abductive Tasks with Large Language Models
arXiv:2603.21250v1 Announce Type: new Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize...
Locally Coherent Parallel Decoding in Diffusion Language Models
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that are particularly appealing for code generation and editing. Achieving sub-linear latency in discrete...
LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs
arXiv:2603.20293v1 Announce Type: new Abstract: Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for learning from these graphs often assume...
Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models
arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward Models (GRMs) achieve superior accuracy through chain-of-thought (CoT) reasoning, they incur substantial computational costs. Conversely,...
The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processes
arXiv:2603.20994v1 Announce Type: new Abstract: In shared autonomy, a critical tension arises when an automated assistant must choose between obeying a human's instruction and deliberately overriding it to prevent harm. This safety-critical behavior is known as intelligent disobedience. To formalize...
Knowledge Boundary Discovery for Large Language Models
arXiv:2603.21022v1 Announce Type: new Abstract: We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowledge boundary by automatically generating two types of questions: (i)...
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
arXiv:2603.20213v1 Announce Type: new Abstract: Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from ranking prominence towards content inclusion. Generative Engine Optimization (GEO), specifically, aims to maximize visibility and...