arXiv:2604.09560v1 Announce Type: new
Abstract: Transformers, diffusion-maps, and magnetic Laplacians are usually treated as separate tools; we show they are all different regimes of a single Markov geometry built from pre-softmax query-scores. We define a QK "bidivergence" whose exponentiated and ...
“Giant superatoms” could finally solve quantum computing’s biggest problem
In the pursuit of powerful and stable quantum computers, researchers at Chalmers University of Technology, Sweden, have developed the theory for an entirely new quantum system – based on the novel concept of ‘giant superatoms’. This breakthrough enables quantum information to be protected, controlle...
Parameterized Complexity Of Representing Models Of MSO Formulas
arXiv:2604.08707v1 Announce Type: new
Abstract: Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the problem of checking if a given graph has a property specified by a given MSO2 formula can be solved by a...
RAMP: Hybrid DRL for Online Learning of Numeric Action Models
arXiv:2604.08685v1 Announce Type: new
Abstract: Automated planning algorithms require an action model specifying the preconditions and effects of each action, but obtaining such a model is often hard. Learning action models from observations is feasible, but existing algorithms for numeric domains ...
From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI
arXiv:2604.08603v1 Announce Type: new
Abstract: Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how active business scenarios reshape that space for the event at hand -- producing decisions that are fl...
OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains
arXiv:2604.08601v1 Announce Type: new
Abstract: The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute state mutations without sufficient context, coordination, or safety guarantees. We introduce OpenKedge, a protocol that re...
Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection
arXiv:2604.08572v1 Announce Type: new
Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent performance across datasets and models. We show that this instability is driven by differences in the act...
arXiv:2604.08571v1 Announce Type: new
Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly overfit to standard textual formatting. We propose a perturbation pipeline consisting of 14 techniques ...
QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation
arXiv:2604.08570v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework familiarity. We introduce QuanBenc...
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
arXiv:2604.08553v1 Announce Type: new
Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However, their effectiveness as predictors in the low-resource setting, where la...
Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models at ICLR 2026.
Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on knowledge-intensive tasks. In th...
This new chip could slash data center energy waste
A new chip design from UC San Diego could make data centers far more energy-efficient by rethinking how power is converted for GPUs. By combining vibrating piezoelectric components with a clever circuit layout, the system overcomes limitations of traditional designs. The prototype achieved impressiv...
Benchmark Shadows: Data Alignment, Parameter Footprints, and Generalization in Large Language Models
arXiv:2604.07363v1 Announce Type: new
Abstract: Large language models often achieve strong benchmark gains without corresponding improvements in broader capability. We hypothesize that this discrepancy arises from differences in training regimes induced by data distribution. To investigate this, we...
LLM-Generated Fault Scenarios for Evaluating Perception-Driven Lane Following in Autonomous Edge Systems
arXiv:2604.07362v1 Announce Type: new
Abstract: Deploying autonomous vision systems on edge devices faces a critical challenge: resource constraints prevent real-time and predictable execution of comprehensive safety tests. Existing validation methods depend on static datasets or manual fault injec...
BLEG: LLM Functions as Powerful fMRI Graph-Enhancer for Brain Network Analysis
arXiv:2604.07361v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have been widely used in diverse brain network analysis tasks based on preprocessed functional magnetic resonance imaging (fMRI) data. However, their performances are constrained due to high feature sparsity and inherent l...
Prediction Arena: Benchmarking AI Models on Real-World Prediction Markets
arXiv:2604.07355v1 Announce Type: new
Abstract: We introduce Prediction Arena, a benchmark for evaluating AI models' predictive accuracy and decision-making by enabling them to trade autonomously on live prediction markets with real capital. Unlike synthetic benchmarks, Prediction Arena tests model...
New Future of Work: AI is driving rapid change, uneven benefits
For the past five years, the New Future of Work report has captured how work is changing. This year, the shift feels especially sharp. Previous editions have focused on technology’s role in increasing productivity by automating tasks, accelerating communication, and expanding access to information, ...
Microsoft Chief Scientist Jaime Teevan and researchers Jenna Butler, Jake Hofman, and Rebecca Janssen unpack the New Future of Work Report 2025 and explore the ideal AI-driven working world. Plus, is AI a tool or a collaborator? And why the answer matters.
The post Ideas: Steering AI toward the work...
Spectral Edge Dynamics Reveal Functional Modes of Learning
arXiv:2604.06256v1 Announce Type: new
Abstract: Training dynamics during grokking concentrate along a small number of dominant update directions -- the spectral edge -- which reliably distinguishes grokking from non-grokking regimes. We show that standard mechanistic interpretability tools (head at...
FLeX: Fourier-based Low-rank EXpansion for multilingual transfer
arXiv:2604.06253v1 Announce Type: new
Abstract: Cross-lingual code generation is critical in enterprise environments where multiple programming languages coexist. However, fine-tuning large language models (LLMs) individually for each language is computationally prohibitive. This paper investigates...