research

DynaTE: Accelerating Diffusion LLMs via Dynamic Token Execution

arXiv:2610.11284v1 Announce Type: cross Abstract: Diffusion-based LLMs (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs by enabling bidirectional parallel refinemen

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Easy to anticipate, hard to compute: boundary dependence finds the computed outputs that entropy patching misses

DGX agent

arXiv:2610.11790v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) group bytes into patches and run their large global model once per patch. BLT start

researcharxiv-cs-cl

9 Oct 2026

Efficient Multi-Granularity Knowledge Transfer for Radiology Report Generation

arXiv:2610.11303v1 Announce Type: new Abstract: Radiology report generation can automatically generate clinical descriptions from X-ray images, thereby significantly improving the efficiency of radiol

Efficient Reasoning via Constrained Optimization in Latent Space

arXiv:2609.34181v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they still suffer from overthinking, generating redundant reasoning

EgoPhys: Estimating Peak Contact Force and Mechanical Work from Egocentric Manipulation Video

arXiv:2610.11347v1 Announce Type: new Abstract: Physically grounded manipulation of articulated objects requires understanding both the maximum forces encountered during contact and the work performed

Enabling Quantum Natural Language Processing for Hindi Language

arXiv:2312.01221v2 Announce Type: replace Abstract: Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) technique

Epistemic Disturbance in the Graph Model for Conflict Resolution: State-Preserving Actions, Four-Valued Assessments, and the Distinction between Capability and Intention

arXiv:2610.11690v1 Announce Type: cross Abstract: In the graph model for conflict resolution (GMCR), a decision maker (DM) either moves the conflict to another state or does nothing. The basic definit

Equal Path Cost, Unequal Output Effects: Understanding Perturbation Propagation in Diffusion Models

arXiv:2610.11380v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in generative modeling, with their sampling procedures routinely modified to control generation and im

Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks

arXiv:2610.10666v1 Announce Type: new Abstract: Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer vision, saliency maps, whic

Explanation Multiplicity in SHAP: Characterization and Assessment

arXiv:2601.12654v3 Announce Type: replace-cross Abstract: SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even w

FastJEV: Understanding Redundancy for Compact JEV Inference

arXiv:2610.11379v1 Announce Type: new Abstract: JEV models make multimodal decisions by directly scoring candidates. Although the common context is encoded once, candidate evaluation can still repeat

Feature Space Adaptation for Effortless Gaussian Process Flows

arXiv:2610.11459v1 Announce Type: cross Abstract: Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging. Recent methods such as FlowGP (Moss et al., (20

Few-Step Generation via Data-Space Iteration

arXiv:2610.12102v1 Announce Type: cross Abstract: Flow matching has emerged as a scalable paradigm for training high-quality generative models, but sampling from the learned probability flow requires

Fixed-Reference Pose Residuals for Measuring Cross-Dataset Cue Transfer in Human-Robot Interaction Anticipation

arXiv:2610.12245v1 Announce Type: new Abstract: Social and service robots in public spaces need to anticipate which nearby person is about to approach and touch them, so that a response can be prepare

researcharxiv-cs-ro

Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size

arXiv:2510.04440v2 Announce Type: replace Abstract: We develop a source-driven fractional heat-kernel framework for semi-super-vised graph learning that combines nonlocal propagation with sustained la

Fresco++: Frequency-Guided and Canonical-Consistent Optimization for Fine-Grained Head Avatar Modeling

arXiv:2610.11412v1 Announce Type: new Abstract: We propose Fresco++, a unified optimization framework for fine-grained and view-consistent head avatar reconstruction. Head avatar optimization is typic

FreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding

arXiv:2602.01683v2 Announce Type: replace-cross Abstract: Transitioning Multimodal Large Language Models (MLLMs) from offline to online streaming video understanding is essential for continuous percep

From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers

arXiv:2610.11472v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit C

From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots

arXiv:2610.12241v1 Announce Type: new Abstract: Microscopic robots require accurate task geometry despite changes in language, parts, and focus. We present a semantic-to-physical framework that maps i

researcharxiv-cs-ro

From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier

arXiv:2610.10642v1 Announce Type: cross Abstract: This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the mo

From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction

arXiv:2610.11818v1 Announce Type: cross Abstract: While massive, closed-source Vision-Language Models (VLMs) set strong benchmarks for document understanding, their dependence on commercial APIs limit

From Sparse Representations to Behavioral Insights for Multimodal Depression Assessment

arXiv:2610.11787v1 Announce Type: new Abstract: Multimodal depression assessment offers a promising approach to analyzing behavioral patterns associated with depression. However, existing methods ofte

From Video Clips to Creation Trajectory: Sora100K for AI-Native Video Creation

arXiv:2610.11770v1 Announce Type: new Abstract: AI-Native video creation is shifting from isolated video clips toward iterative video creation workflows. However, existing datasets remain largely vide

From What to Which: Decoding Modifier Grounding in Frozen MLLMs

arXiv:2610.12305v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) can describe increasingly complex visual scenes, token-level grounding becomes crucial. Yet, when an MLLM ge

Ghost tasking for parametrized Gaussian Processes solving linear differential equations

arXiv:2610.12009v1 Announce Type: cross Abstract: Physics-informed machine learning has gained significant attention in recent years. In regimes of limited data, parametrized Gaussian processes have b

GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification

arXiv:2512.21476v2 Announce Type: replace-cross Abstract: Colonoscopic Polyp Re-Identification (ReID) aims to match the same polyp across a large gallery of images captured from different viewpoints a

Graphons of Line Graphs

arXiv:2409.01656v4 Announce Type: replace-cross Abstract: We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper w

Group Invariant Spectral Embedding

arXiv:2607.08987v2 Announce Type: replace Abstract: Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional s

Harness Compilation: Which Decisions Should a Small Vision-Language Model Keep?

arXiv:2610.11231v1 Announce Type: new Abstract: Small vision-language models may be able to read external evidence yet struggle to obtain it. We introduce Harness Compilation (HC), an offline procedur

Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification

arXiv:2610.12147v1 Announce Type: new Abstract: False negatives remain a critical limitation of computer-aided diagnosis (CAD) systems for breast cancer screening due to delayed detection and treatmen

How Is Automated Research Evaluated? A Survey of Benchmarks and Evaluation Practices

arXiv:2610.11877v1 Announce Type: new Abstract: Automated research systems support literature synthesis, ideation, experiments, writing, and peer review, but their evaluation is dispersed across tasks

How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra

arXiv:2610.10640v1 Announce Type: cross Abstract: Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise. How many direct

How Many Repeated Pairwise Comparisons Are Needed for Ranking under Heterogeneity?

arXiv:2610.10795v1 Announce Type: new Abstract: We study ranking models by population-average utility from pairwise comparisons when preferences vary across users and tasks. Prior work shows that a si

How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders

arXiv:2610.12250v1 Announce Type: cross Abstract: Pretrained audio encoders are reused for downstream tasks that are often unknown when the encoder is trained, so their usefulness depends partly on wh

How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation

arXiv:2610.05671v2 Announce Type: replace Abstract: Automatic prompt optimization (APO) has been widely employed to adapt large language models without updating their weights, yielding promising resul

Hybrid Cinematography: Previsualizing and Managing Hallucination Risk in Generative Video Reshooting

arXiv:2610.12455v1 Announce Type: cross Abstract: On a film set, the camera move is committed during a take. Generative video reshooting lets filmmakers change it afterward, but may require hallucinat

ILM: An AI-Powered Storytelling Educational Tool

arXiv:2610.12064v1 Announce Type: new Abstract: Digital technologies have made Islamic narratives more accessible, but existing platforms provide limited support for structured learning and comprehens

Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and Recovery

arXiv:2610.11144v1 Announce Type: cross Abstract: Single-image nutrition estimation can fail silently when visible foods are missed. Even when a food is correctly identified, its proposed region may n

In-Ride Alcohol-Impairment Detection in E-Scooterists with False-Alarm Control

arXiv:2610.11783v1 Announce Type: new Abstract: Shared e-scooter services have become a widely adopted urban transport mode. While most users ride responsibly, alcohol intoxication stands out among th

Incremental Open-Ended Deep Research with Structured Harness

arXiv:2610.11566v1 Announce Type: new Abstract: Existing Open-Ended Deep Research (OEDR) systems primarily generate reports from scratch, making them inefficient for scenarios where research reports n

Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization

arXiv:2610.12142v1 Announce Type: new Abstract: Electromagnetic spectrum monitoring increasingly requires flexible analysis beyond task-specific recognition and detection. Multimodal large language mo

Intent Graph: Navigating the Analytical Reasoning Space for Exploratory Data Analysis

arXiv:2610.11025v1 Announce Type: cross Abstract: Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can

Interesting paper from Sakana AI on memory for recurrent models. Recurrent models are good at state tracking, but they usually keep everythi…

Interesting paper from Sakana AI on memory for recurrent models. Recurrent models are good at state tracking, but they usually keep everything in one hidden vector, so short-term computation and long-

researchdair-ai--x

Interpretable Memory Models for Spaced Repetition

arXiv:2610.10548v1 Announce Type: cross Abstract: Spaced repetition software schedules reviews with a memory model fit to review logs. Accuracy on a test set is not sufficient evidence of quality sinc

Interval-valued SHAP in Tree-Based Models

arXiv:2610.11953v1 Announce Type: new Abstract: Shapley values are among the most popular feature-attribution explanations. Efficient approaches for computing/estimating Shapley values for tree-based

InterviewPlayground: A Simulation Environment for Evaluating AI Interviewers

arXiv:2610.12023v1 Announce Type: new Abstract: Increasingly, AI interviewers are being developed to elicit open-ended responses in applications like market research, public polling, preference elicit

IntrinSync: Joint Intrinsic Decomposition and Reciprocal Rendering

arXiv:2610.11138v1 Announce Type: new Abstract: Inverse rendering decomposes an image into intrinsic properties such as appearance, illumination, geometry, and material, yet these properties are inher

Invariant-Measure Reasoners: Stable Representations for Latent Reasoning

arXiv:2610.10996v1 Announce Type: new Abstract: Latent reasoning models repeatedly update a latent state using the same recurrent block. As the recurrent depth increases, the sequence of latent states