Search: “research”

DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models

arXiv:2602.17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking conte

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Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models

DGX agent

arXiv:2606.04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally s

researcharxiv-cs-ai

4 Jun 2026

Efficient and Training-Free Single-Image Diffusion Models

arXiv:2606.04299v1 Announce Type: new Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that o

Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention

arXiv:2606.04474v1 Announce Type: new Abstract: Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognit

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning

arXiv:2606.04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes. Computational epitope prediction is critical for unders

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

arXiv:2606.04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality. As Gao et a

Evaluating Autoformalization Robustness via Semantically Similar Paraphrasing

arXiv:2511.12784v3 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as powerful tools for autoformalization. Despite their impressive performance, these models can s

Evaluating Reasoning Fidelity in Visual Text Generation

arXiv:2606.04479v1 Announce Type: cross Abstract: Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generatio

Exact Unlearning in Reinforcement Learning

arXiv:2606.04182v1 Announce Type: cross Abstract: We formulate the problem of exact unlearning in reinforcement learning, where the goal is to design an efficient framework that enables the removal of

Explaining a probabilistic prediction on the simplex with Shapley compositions

arXiv:2408.01382v3 Announce Type: replace Abstract: Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of e

Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)

arXiv:2606.05145v1 Announce Type: cross Abstract: When post-trained language models fail on reasoning problems, the common test-time-scaling response is to spend more compute on additional attempts, a

Fast Cubical Persistent Homology on 2D and 3D Images via Union-Find, Pruning, and Lookup Tables

arXiv:2606.04801v1 Announce Type: new Abstract: We present Flash Cubical, a highly efficient computation of cubical persistence on a V-filtration for 2D and 3D images over F_2. The implementation is b

Fast & Faithful Function Vectors

arXiv:2606.05079v1 Announce Type: new Abstract: Function vectors (FVs) are task representations elicited during in-context learning that can be used to steer Large Language Models (LLMs). However, des

Finite-Iteration Local Dynamics and Warm Starts for Alternating Power Iteration in Spiked Tensor PCA

arXiv:2606.04065v1 Announce Type: cross Abstract: We study simultaneous alternating power iteration for fixed-order asymmetric rank-one spiked tensor models. Our main contribution is a finite-iteratio

Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks

arXiv:2606.04429v1 Announce Type: cross Abstract: A common heuristic used to explain the generalization of first-order gradient methods on non-convex neural networks is that 'flat interpolators genera

FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors

arXiv:2606.05101v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD m

Folded Transport MCMC: Certifiable Quotient Posterior Computation for Symmetric Bayesian Models

arXiv:2606.04307v1 Announce Type: new Abstract: Bayesian models with finite symmetry - mixture models with exchangeable components, structural identification with closely-spaced modes - define posteri

From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming Lifecycle

arXiv:2107.01629v3 Announce Type: replace-cross Abstract: Livestreaming has evolved into a thriving industry where creators can directly monetize and engage with their audiences and followers. In prac

From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments

arXiv:2606.04275v1 Announce Type: cross Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time

GenSpan: Generation-Calibrated Motion Span Priors for Multi-Verb Video Corpus Moment Retrieval

arXiv:2603.22121v2 Announce Type: replace-cross Abstract: Video Corpus Moment Retrieval (VCMR) aims to retrieve both the correct video and its temporal segment corresponding to a natural-language quer

GIFT: Games as Informal Training for Generalizable LLMs

arXiv:2601.05633v2 Announce Type: replace Abstract: Recent LLMs excel at formal tasks such as mathematical reasoning and code generation, but still struggle with broader abilities such as planning, cr

GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings

arXiv:2606.04367v1 Announce Type: new Abstract: Interlinear glossed text (IGT) is the standard format for linguistic annotation in language documentation. Producing it manually, however, is often slow

Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning

arXiv:2606.05046v1 Announce Type: new Abstract: We introduce Graph Cascades, a mesoscopic rewiring strategy for Graph Neural Networks (GNNs) and Graph Transformers (GTs) that captures intermediate-sca

Graph-Guided Universum Learning in Generalized Eigenvalue Proximal SVMs for Alzheimer's Disease Classification

arXiv:2606.04699v1 Announce Type: cross Abstract: Early and accurate detection of Alzheimer's disease (AD) is important for timely intervention and disease management. Generalized Eigenvalue Proximal

Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives

arXiv:2606.05018v1 Announce Type: new Abstract: Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual p

Hierarchical Space Partition for Surface Reconstruction

arXiv:2606.04891v1 Announce Type: new Abstract: Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR

HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning

arXiv:2606.04345v1 Announce Type: cross Abstract: This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture. Traditi

IMPose: Interactive Multi-person Pose Estimation with Dynamic Correction Propagation

arXiv:2606.04480v1 Announce Type: new Abstract: High-quality dynamic human pose annotation equips AI with precise motion kinematics to enable human behavior mastery, yet remains labor-intensive and ti

Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes

arXiv:2512.14177v3 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on

Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space

arXiv:2604.04944v2 Announce Type: replace-cross Abstract: Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of

Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning

arXiv:2606.04061v1 Announce Type: new Abstract: Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence, which severely deg

IRIS-GAN: Staged Specialist Detection of Deepfake Faces

arXiv:2606.04863v1 Announce Type: new Abstract: We introduce IRIS-GAN, a specialist forensic detector for synthetic face images under cross-generator shift. Rather than addressing universal synthetic-

Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks

arXiv:2606.04404v1 Announce Type: cross Abstract: The deep neural network is a widely used framework in machine learning that has been widely applied in various fields. However, deep neural networks o

L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI

arXiv:2606.04419v1 Announce Type: cross Abstract: MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam

L^3: Large Lookup Layers

arXiv:2601.21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP

Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification

arXiv:2511.06331v2 Announce Type: replace Abstract: Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservat

LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding

arXiv:2606.04302v1 Announce Type: new Abstract: Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes e

Learning Admissible Heuristics via Cost Partitioning

arXiv:2606.04597v1 Announce Type: new Abstract: Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation. Cost partitioning com

Learning Control-Affine Reduced-Order Models via Autoencoders

arXiv:2606.05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (

Learning symplectic model reduction based on a approximation theorem of symplectic embeddings

arXiv:2606.04623v1 Announce Type: new Abstract: High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics evolving on symplectic manifolds.

Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models

arXiv:2606.05087v1 Announce Type: new Abstract: Frequent English verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'mak

LiSeCo: Linear Semantic Control for Language Generation

arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both

LMM-IR: Large-Scale Netlist-Aware Multimodal Framework for Static IR-Drop Prediction

arXiv:2511.12581v2 Announce Type: replace Abstract: Static IR drop analysis is a fundamental and critical task in the field of chip design. Nevertheless, this process can be quite time-consuming, pote

Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Catastrophic Slope Failure

arXiv:2601.03569v3 Announce Type: replace Abstract: Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detectio

Loss-Conditional PINNs for Parametric PDE Families

arXiv:2606.04420v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of ODEs and PDEs by minimising a weighted combination of residual, boundary, initial, and

Low-Rank Decay for Grokking in Scale-Invariant Transformers: A Spectral-Geometric View

arXiv:2606.04405v1 Announce Type: cross Abstract: Modern Transformer architectures frequently employ normalization mechanisms such as RMSNorm and Query-Key Normalization, making parts of the model app

MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution

arXiv:2606.05068v1 Announce Type: new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because

Making Expert Reasoning Learnable with Self-Distillation

arXiv:2602.02405v2 Announce Type: replace-cross Abstract: Improving the reasoning capabilities of large language models (LLMs) typically relies either on the model's ability to sample a correct soluti