Search: “research”

Emergent Ordinal Geometry in Transformers Trained on Local Comparisons

arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals no

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Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence

DGX agent

arXiv:2606.01470v1 Announce Type: cross Abstract: Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We

researcharxiv-cs-ai

2 Jun 2026

eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

arXiv:2606.02054v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical

Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification

arXiv:2606.01679v1 Announce Type: new Abstract: Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by

researcharxiv-cs-cl

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

arXiv:2509.23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric posit

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals

arXiv:2503.06473v5 Announce Type: replace-cross Abstract: Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly

EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models

arXiv:2606.00722v1 Announce Type: cross Abstract: Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models

Equilibrium Propagation for Non-Conservative Systems

arXiv:2602.03670v2 Announce Type: replace-cross Abstract: Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and

Erased but Not Forgotten: How Backdoors Compromise Concept Erasure

arXiv:2504.21072v2 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sex

ERICA: Quantifying Replicability of Cluster Analysis

arXiv:2606.00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an an

EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing

arXiv:2606.00437v1 Announce Type: new Abstract: Process reward models (PRMs) are widely used in language-model training with dense step-level supervision. They assume PRM scores are stable proxies for

ETC: Extreme Token Compression via Task-aware Visual Information Distillation in VLMs

arXiv:2606.00543v1 Announce Type: new Abstract: In Vision-Language Models (VLMs), high-resolution images produce a large number of visual tokens, resulting in high computational costs and KV-cache ove

EuroBERT: Scaling Multilingual Encoders for European Languages

arXiv:2503.05500v3 Announce Type: replace-cross Abstract: General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirec

EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors

arXiv:2606.01884v1 Announce Type: new Abstract: Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration. How

Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities

arXiv:2511.20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activ

Evaluation of Baseline Methods for IDD-based SSD External Memory Search

arXiv:2606.01840v1 Announce Type: new Abstract: Many difficult search problems cannot be solved by algorithms such as A* using only RAM. Search algorithms which use external memory such as SSDs and HD

Event-Based Vision in Space: Applications, Trends, and Future Directions

arXiv:2606.01280v1 Announce Type: new Abstract: Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optica

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606.01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-s

EvoCut: Multi-Layer Evolution-Aware Visual Token Compression for Efficient Large Vision-Language Models

arXiv:2606.01756v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong performance on image and video understanding tasks, but their inference efficiency is constrained by

Examine Clinicians' Modification of Hedging Language in Ambient AI Documentation: A Comparative Study of AI Drafts and Final Notes

arXiv:2606.00018v1 Announce Type: cross Abstract: Ambient AI documentation systems generate clinical note drafts that clinicians frequently revise before signing off into electronic health records, ye

Exploiting In-Sensor Computing for Energy-Efficient Earth Observation

arXiv:2606.01271v1 Announce Type: new Abstract: The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the sev

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks

arXiv:2602.02763v3 Announce Type: replace Abstract: Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evi

Expressivity of congruence-based architectures for DNNs on positive-definite matrices

arXiv:2606.02490v1 Announce Type: new Abstract: This work studies neural architectures for classifying symmetric positive-definite matrices, focusing on congruence-like layers, in which the input matr

Extending Causal Metamodeling to a non-Markovian Queue

arXiv:2606.00795v1 Announce Type: cross Abstract: Metamodels for discrete-event simulations approximate the behavior of simulation models without running expensive simulations. Prior work introduced m

Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery

arXiv:2606.02011v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive. While low-bit quantization reduces per-token decoding cost, we

ext{VG}^2GT: Voxel-Gaussian Splatting Visual Geometry Grounded Transformer

arXiv:2606.01573v1 Announce Type: new Abstract: Gaussian splatting has shown strong potential for 3D reconstruction and novel view synthesis. However, most existing methods require accurate camera par

Tutorials

Farewell Ai2

This article likely discusses the closure or significant changes to AI2 (Allen Institute for AI), a prominent artificial intelligence research organization founded by Paul Allen. The piece probably co

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Fast and Lightweight Novel View Synthesis with Differentiable Multiplane Image

arXiv:2606.02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatt

FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation

arXiv:2601.06199v3 Announce Type: replace-cross Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form speech is bottlenecked by the explosive growth of input tokens. Unlike images or

FATE-VLA:Failue-aware test generation for vision-language-action models

arXiv:2606.02307v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are increasingly used as generalist robot policies, yet their evaluation still relies largely on static benchmarks t

researcharxiv-cs-ro

Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering

arXiv:2303.04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffe

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning

arXiv:2412.04177v2 Announce Type: replace Abstract: Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural netwo

FLaG: Fine-Grained Latent Grouping for Hallucination Detection

arXiv:2606.00301v1 Announce Type: new Abstract: Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global un

FlatVPR: Plug-and-play Geo-linear Residual Adapter for Geometric Rectification of Foundation Model Feature Manifolds

arXiv:2606.01734v1 Announce Type: new Abstract: This paper proposes ``FlatVPR,'' a novel geometric rectification paradigm that effectively bridges the trade-off between map lightweightness and localiz

Flexible Online Representation Learning Based on Similarity Matching

arXiv:2606.01546v1 Announce Type: new Abstract: Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data. Such a representation can

FlipItRight: Stable Pose-Targeted Throw-Flip Across Diverse Objects

arXiv:2606.01713v1 Announce Type: new Abstract: We propose FlipItRight, a framework for stable planar pose-targeted throw-flip with a high-DoF manipulator. The task is decomposed into an object-level

researcharxiv-cs-ro

Flow-Based Density Ratio Estimation for Intractable Distributions with Applications in Genomics

arXiv:2602.24201v2 Announce Type: replace Abstract: Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled compariso

Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications

arXiv:2606.00078v1 Announce Type: cross Abstract: Numerous modern applications in signal processing and medical imaging necessitate acquiring high-dimensional signals under tight resource constraints.

Flow-Transformed Implicit Processes for Function-Space Variational Inference

arXiv:2606.01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space m

FlowNar: Scalable Streaming Narration for Long-Form Videos

arXiv:2606.00620v1 Announce Type: new Abstract: Recent Large Multimodal Models (LMMs), primarily designed for offline settings, are ill-suited for the dynamic requirements of streaming video. While re

FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors

arXiv:2606.01352v1 Announce Type: new Abstract: Watch time has emerged as a pivotal metric for optimizing deep user engagement in short-video recommender systems. However, current methods of watch tim

FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo

arXiv:2606.02365v1 Announce Type: cross Abstract: Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical

FocusDiT: Masking Queries in Diffusion Transformers for Fine-grained Image Generation

arXiv:2606.02090v1 Announce Type: new Abstract: Diffusion transformer (DiT) has been widely adopted in the generative diffusion field, advancing the denoising of query tokens through attention and Fee

ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution

arXiv:2602.03203v2 Announce Type: replace Abstract: Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length

researcharxiv-cs-cl

Forget Attention: Importance-Aware Attention Is All You Need

arXiv:2606.02332v1 Announce Type: new Abstract: Combining attention's global retrieval with the sequential importance signal of state space models (SSMs) is the open challenge of hybrid language model

Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization

arXiv:2606.00132v1 Announce Type: cross Abstract: While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraini

FOVI: A biologically-inspired foveated interface for deep vision models

arXiv:2602.03766v2 Announce Type: replace Abstract: Human vision is foveated, with variable resolution peaking at the center of a large field of view; this reflects an efficient trade-off for active s

French parsing enhanced with a word clustering method based on a syntactic lexicon

arXiv:2606.00634v1 Announce Type: new Abstract: This article evaluates the integration of data extracted from a French syntactic lexicon, the Lexicon-Grammar (Gross, 1994), into a probabilistic parser

researcharxiv-cs-cl