Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems
arXiv:2603.20589v1 Announce Type: new Abstract: Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. Several groups have recently used random $k$-satisfiability ($k$-SAT) as a synthetic benchmark for new generative...
Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression
arXiv:2603.20616v1 Announce Type: new Abstract: Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting long-context deployment. Existing token eviction methods reduce memory by discarding less important tokens, which can...
CFNN: Continued Fraction Neural Network
arXiv:2603.20634v1 Announce Type: new Abstract: Accurately characterizing non-linear functional manifolds with singularities is a fundamental challenge in scientific computing. While Multi-Layer Perceptrons (MLPs) dominate, their spectral bias hinders resolving high-curvature features without excessive parameters. We introduce Continued Fraction Neural Networks...
Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity
arXiv:2603.20645v1 Announce Type: new Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated on low-dimensional structures -- remains incomplete. This paper investigates how diffusion models learn such structured...
Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models
arXiv:2603.20655v1 Announce Type: new Abstract: We introduce Exponential Family Discriminant Analysis (EFDA), a unified generative framework that extends classical Linear Discriminant Analysis (LDA) beyond the Gaussian setting to any member of the exponential family. Under the assumption that each class-conditional...
Centrality-Based Pruning for Efficient Echo State Networks
arXiv:2603.20684v1 Announce Type: new Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the randomly initialized reservoir often contains redundant nodes, leading to unnecessary computational overhead and reduced efficiency....
Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
arXiv:2603.20775v1 Announce Type: new Abstract: In personalized marketing, uplift models estimate incremental effects by modeling how customer behavior changes under alternative treatments. However, real-world data often exhibit biases - such as selection bias, spillover effects, and unobserved confounding - which...
OmniPatch: A Universal Adversarial Patch for ViT-CNN Cross-Architecture Transfer in Semantic Segmentation
arXiv:2603.20777v1 Announce Type: new Abstract: Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target weights are unknown. Most existing approaches either craft image-wide perturbations or optimize patches for a...
Neural Autoregressive Flows for Markov Boundary Learning
arXiv:2603.20791v1 Announce Type: new Abstract: Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances improve upon traditional constraint-based techniques by scoring local causal...
Large Neighborhood Search meets Iterative Neural Constraint Heuristics
arXiv:2603.20801v1 Announce Type: new Abstract: Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively refine candidate assignments. In this work, we make explicit the connection between such iterative neural...
Achieving $\widetilde{O}(1/\epsilon)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
arXiv:2603.20819v1 Announce Type: new Abstract: This paper studies finite-sample set-membership identification for discrete-time bilinear systems under bounded symmetric log-concave disturbances. Compared with existing finite-sample results for linear systems and related analyses under stronger noise assumptions, we consider the more challenging...
Court reverses ruling on qualified immunity, denies review of death-row case and First Amendment challenge by citizen journalist
In a list of orders released on Monday morning, the Supreme Court reversed a ruling by a federal appeals court, holding that a Vermont police officer is entitled to qualified […]The postCourt reverses ruling on qualified immunity, denies review of...
Birthright citizenship: reading the text and sidestepping the parent trap
“The text is the law, and it is the text that must be observed,” Justice Antonin Scalia famously insisted at page 22 of a notable book on legal interpretation. “Only […]The postBirthright citizenship: reading the text and sidestepping the parent...
Startup Gimlet Labs is solving the AI inference bottleneck in a surprisingly elegant way
Gimlet Labs just raised an $80 million Series A for tech that lets AI run across NVIDIA, AMD, Intel, ARM, Cerebras and d-Matrix chips, simultaneously.
MAPLE: Metadata Augmented Private Language Evolution
arXiv:2603.19258v1 Announce Type: cross Abstract: While differentially private (DP) fine-tuning of large language models (LLMs) is a powerful tool, it is often computationally prohibitive or infeasible when state-of-the-art models are only accessible via proprietary APIs. In such settings, generating DP...
Grounded Multimodal Retrieval-Augmented Drafting of Radiology Impressions Using Case-Based Similarity Search
arXiv:2603.17765v1 Announce Type: cross Abstract: Automated radiology report generation has gained increasing attention with the rise of deep learning and large language models. However, fully generative approaches often suffer from hallucinations and lack clinical grounding, limiting their reliability in real-world...
Learning Dynamic Belief Graphs for Theory-of-mind Reasoning
arXiv:2603.20170v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under uncertainty -- especially in high-stakes settings such as disaster...
A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
arXiv:2603.19253v1 Announce Type: cross Abstract: Argument mining (AM) is an interdisciplinary research field focused on the automatic identification and classification of argumentative components, such as claims and premises, and the relationships between them. Recent advances in large language models (LLMs)...
PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
arXiv:2603.19579v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space....
DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution
arXiv:2603.19248v1 Announce Type: cross Abstract: Immersive conversational systems in production face a persistent trade-off between responsiveness and long-horizon task capability. Real-time interaction is achievable for lightweight turns, but requests involving planning and tool invocation (e.g., search and media generation) produce...
Teaching an Agent to Sketch One Part at a Time
arXiv:2603.19500v1 Announce Type: new Abstract: We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach...
GeoChallenge: A Multi-Answer Multiple-Choice Benchmark for Geometric Reasoning with Diagrams
arXiv:2603.19252v1 Announce Type: cross Abstract: Evaluating the symbolic reasoning of large language models (LLMs) calls for geometry benchmarks that require multi-step proofs grounded in both text and diagrams. However, existing benchmarks are often limited in scale and rarely provide visually...
Utility-Guided Agent Orchestration for Efficient LLM Tool Use
arXiv:2603.19896v1 Announce Type: new Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task performance...
ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models
arXiv:2603.19515v1 Announce Type: new Abstract: Large language models (LLMs) with advanced cognitive capabilities are emerging as agents for various reasoning and planning tasks. Traditional evaluations often focus on specific reasoning or planning questions within controlled environments. Recent studies have explored...
Pitfalls in Evaluating Interpretability Agents
arXiv:2603.20101v1 Announce Type: new Abstract: Automated interpretability systems aim to reduce the need for human labor and scale analysis to increasingly large models and diverse tasks. Recent efforts toward this goal leverage large language models (LLMs) at increasing levels of...
Hyperagents
arXiv:2603.19461v1 Announce Type: new Abstract: Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such...
Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
arXiv:2603.19264v1 Announce Type: cross Abstract: With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as healthcare and biomedicine. However, the cost of labeling...
Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization
arXiv:2603.19268v1 Announce Type: cross Abstract: Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for complex physical systems such as combustion science, general-purpose LLMs often generate severe hallucinations...
A Human-Centered Workflow for Using Large Language Models in Content Analysis
arXiv:2603.19271v1 Announce Type: cross Abstract: While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a...
Transformers are Stateless Differentiable Neural Computers
arXiv:2603.19272v1 Announce Type: cross Abstract: Differentiable Neural Computers (DNCs) were introduced as recurrent architectures equipped with an addressable external memory supporting differentiable read and write operations. Transformers, in contrast, are nominally feedforward architectures based on multi-head self-attention. In this work...