Search: “research”

Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling

arXiv:2606.27831v1 Announce Type: cross Abstract: This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framew

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Research

Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design

DGX agent

arXiv:2606.27923v1 Announce Type: cross Abstract: We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior

researcharxiv-cs-ai

29 Jun 2026

How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks

arXiv:2606.28242v1 Announce Type: cross Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on s

HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models

arXiv:2606.27627v1 Announce Type: cross Abstract: Discrete audio representations have become increasingly popular for building multimodal text-audio systems and integrating audio capabilities into Lar

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

arXiv:2604.00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidl

Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge

arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models

Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks

arXiv:2606.27759v1 Announce Type: new Abstract: Training binary neural networks (BNNs) from scratch is dominated by the straight-through estimator (STE), whose forward/backward mismatch produces sever

Learning Complementary Action Modeling from Automotive Maintenance Instructions

arXiv:2606.27808v1 Announce Type: new Abstract: A minute lexical variation can reverse the procedural meaning of an instruction even when the rest of the sentence remains unchanged. In automotive main

Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space

arXiv:2606.28196v1 Announce Type: new Abstract: Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects be

researcharxiv-cs-ro

Learning Stochastic Bridges for Video Object Removal via Video-to-Video Translation

arXiv:2601.12066v4 Announce Type: replace Abstract: Existing video object removal methods predominantly rely on diffusion models following a noise-to-data paradigm, where generation starts from uninfo

Learning to Reason with Curriculum II: Compositional Generalization

arXiv:2606.27721v1 Announce Type: new Abstract: Compositional generalization, the ability to solve complex problems by combining solutions to simpler sub-problems, is a fundamental capability of both

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries

arXiv:2510.25731v2 Announce Type: replace-cross Abstract: Initial-boundary value problems (IBVPs) provide the essential framework for modelling a wide range of phenomena in physics and engineering. We

Lifted Causal Inference

arXiv:2606.28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby spee

Masked Language Flow Models

arXiv:2606.27617v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approxim

Measuring the Redundancy of Decoder Layers in SpeechLLMs

arXiv:2603.05121v2 Announce Type: replace-cross Abstract: Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters

Mechanism-Driven Monitors for Preemptive Detection of LLM Training Instability

arXiv:2606.28116v1 Announce Type: new Abstract: Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they o

MER-R1: Multimodal Emotion Reasoning via Slow-Fast Thinking Synergy

arXiv:2606.27652v1 Announce Type: new Abstract: We find that explicit reasoning does not necessarily translate into better multimodal emotion recognition (MER) accuracy, even though it makes predictio

Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution

arXiv:2606.28039v1 Announce Type: cross Abstract: Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely avail

MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations

arXiv:2606.27779v1 Announce Type: new Abstract: Generating lifelike facial animation for dyadic conversations requires reconciling high-level cognitive intent with precise low-level motor reflexes, ye

Model Releases

Mitigating LLM-based p-Hacking by Preregistering for the Next LLM

model-releasesarxiv-cs-ai

arXiv:2606.27687v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests. However, L

MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation

arXiv:2606.28142v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) methods commonly update the affine parameters of normalization layers to adapt deployed models under distribution shifts. How

Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF

arXiv:2606.27671v1 Announce Type: new Abstract: Predicting electromagnetic field (EMF) strength in urban environments is essential for cellular network planning but computationally expensive with phys

MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis

arXiv:2410.02103v4 Announce Type: replace Abstract: Recent works in volume rendering, extit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with

Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing

arXiv:2606.27598v1 Announce Type: cross Abstract: Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail. We hypo

NLL-Guided Full-Attention Layer Selection for Training-Free Sliding-Window Adaptation

arXiv:2606.27791v1 Announce Type: cross Abstract: Hybrid attention models that mix full and sliding-window attention across layers offer a promising approach to efficient long-context inference, but t

Non-Linear Strategic Classification Made Practical

arXiv:2606.28204v1 Announce Type: cross Abstract: Algorithmic developments in Strategic Classification have been mostly limited to linear classifiers in settings where the best response has a closed-f

On the stability of scale-space metrics

arXiv:2606.27605v1 Announce Type: cross Abstract: We study the stability of a classical family of metrics defined over functions' Gaussian scale-space representations, focusing on the comparison of im

Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering

arXiv:2606.28076v1 Announce Type: new Abstract: Knowledge graph question answering (KGQA) aims to answer natural-language questions by reasoning over structured facts. Existing multi-hop KGQA methods

PAC-Bayesian Certificates for Quadratic Closed-Loop Control

arXiv:2606.28281v1 Announce Type: cross Abstract: PAC-Bayesian bounds provide finite-sample guarantees for data-dependent randomized predictors, but applying them to learning-based control is difficul

Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation

arXiv:2606.27978v1 Announce Type: cross Abstract: Pixel-space continuous-token autoregressive (AR) generation directly models images as sequences of raw pixel patches, avoiding discrete tokenization o

Pepti-drift: Toxicity-Repulsive Drifting for Antigen-Conditioned Discrete Peptide Generation

arXiv:2606.27824v1 Announce Type: cross Abstract: Peptides are a promising therapeutic modality that combine the chemical tunability of small molecules with the target specificity of macromolecular th

Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians

arXiv:2503.13051v3 Announce Type: replace-cross Abstract: Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into

Physics-constrained neural networks for surrogate modeling of lossless periodic structures

arXiv:2606.28119v1 Announce Type: cross Abstract: We introduce a physics-constrained neural network (PCNN) for the rapid prediction of rigorous coupled-wave analysis (RCWA) outputs in the form of Jone

PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion

arXiv:2606.27760v1 Announce Type: new Abstract: End-to-end pixel-space diffusion models bypass the lossy compression of Latent Diffusion Models (LDMs) but struggle to jointly model low-frequency seman

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

arXiv:2606.23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation becaus

Quenched large deviations for Monte Carlo integration with Coulomb gases

arXiv:2508.01392v2 Announce Type: replace Abstract: Gibbs measures, such as Coulomb gases, are popular in modelling systems of interacting particles. Recently, we proposed to use Gibbs measures as ran

Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering

arXiv:2606.27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the q

Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

arXiv:2606.27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, effic

Reconstructing the Developmental Trajectory of Adipocytes in Human Adipose Tissue Using Single-Cell RNA Sequencing

arXiv:2606.27657v1 Announce Type: cross Abstract: Obesity is a global health crisis associated with metabolic disorders such as type 2 diabetes and cardiovascular disease. This study employed single-c

Recovering Sharp Conductivity Features in the Finite-Data Calderon Problem with Physics-Informed Neural Networks

arXiv:2606.28158v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have recently emerged as a promising framework for addressing the Calderon inverse problem from limited boundar

Reduction of Probabilistic Chemical Reaction Networks

arXiv:2606.27737v1 Announce Type: new Abstract: Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemente

Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs

arXiv:2603.20239v2 Announce Type: replace-cross Abstract: 3D Scene Graphs (3DSGs) provide hierarchical, multi-resolution abstractions that encode the geometric and semantic structure of an environment

Robustness of Constraint Automata for Description Logics with Concrete Domains

arXiv:2601.19644v2 Announce Type: replace-cross Abstract: Decidability or complexity issues about the consistency problem for description logics with concrete domains have already been analysed with t

Sampling the Schwinger Model with Gauge-Equivariant Diffusion

arXiv:2606.27481v1 Announce Type: cross Abstract: We present a first study of a diffusion-based approach to accelerated sampling of the N_f = 2 lattice Schwinger model. Our work is inspired by recent

Scaling limit of the Random Language Model

arXiv:2606.28105v1 Announce Type: cross Abstract: We develop a quantitative theory of the Random Language Model (RLM), an ensemble of stochastic context-free grammars, in a scaling limit where the num

SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures

arXiv:2606.27884v1 Announce Type: cross Abstract: Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and mem

Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization

arXiv:2606.28307v1 Announce Type: cross Abstract: We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipsch

Self-Concordant Perturbations for Linear Bandits

arXiv:2510.24187v3 Announce Type: replace-cross Abstract: We consider the adversarial linear bandits setting and present a unified algorithmic framework that bridges Follow-the-Regularized-Leader (FTR