Search: “benchmark”

DICA: Dual-Indicator Guided Contrastive Alignment in Multimodal Large Language Models

arXiv:2607.23944v1 Announce Type: new Abstract: Human visual reasoning typically follows a coarse-to-fine attention process, starting from global scene understanding and gradually focusing on question

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Research

Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting

DGX agent

arXiv:2607.22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an obse

researcharxiv-cs-ai

28 Jul 2026

Disentangling Semantic Attention from Structural Bias in the Attention Manifold

arXiv:2607.24017v1 Announce Type: cross Abstract: The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws. Specifically, MLLM

Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splitting

arXiv:2607.23721v1 Announce Type: cross Abstract: Distributional random forests replace mean-based CART splitting with criteria that compare the full conditional response distribution in candidate chi

researcharxiv-cs-lg

Do LLMs Know Their Vulnerable Scenarios?

arXiv:2607.23496v1 Announce Type: new Abstract: Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their sa

DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification

arXiv:2607.22644v1 Announce Type: new Abstract: Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or typ

Does Faithfulness-Guided Alignment Hurt Accuracy? Unlocking Accurate and Faithful Post-Retrieval Reasoning

arXiv:2602.01348v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) can achieve strong answer accuracy on multi-hop questions, but outcome-level rewards often leave reasoning trac

safetyarxiv-cs-cl

Effective Receptive Field Ordering Matters for Infrared Small Target Detection

arXiv:2607.23994v1 Announce Type: new Abstract: In this work, we investigate a previously unexplored architectural dimension for infrared small target detection: the organization of effective receptiv

researcharxiv-cs-cv

Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures

arXiv:2607.24714v1 Announce Type: cross Abstract: Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given archite

EgoPlay: Event-Triggered Video Editing for Egocentric Streams

arXiv:2607.24560v1 Announce Type: cross Abstract: We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer

Evaluating Fuzz Testing for Reinforcement Learning Agents

arXiv:2607.24577v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents are increasingly deployed in safety-critical domains such as robotics, autonomous driving, and drone control, where u

Evaluating Large Language Models for Symbolic Security Protocol Analysis

arXiv:2607.20712v1 Announce Type: cross Abstract: Security protocol verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether Large Language Models (LLMs) can perform

Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures

model-releasesarxiv-cs-cv

arXiv:2406.13128v2 Announce Type: replace Abstract: Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability i

Agents

EviBack: Search-Agent Reinforcement Learning via Evidence-Constrained Teacher Backoff

arXiv:2607.23955v1 Announce Type: new Abstract: Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no c

agentsarxiv-cs-ai

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability

arXiv:2607.24177v1 Announce Type: cross Abstract: Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since exi

Explainable Reinforcement Learning via Physics-Aware Policy Distillation

arXiv:2607.24672v1 Announce Type: new Abstract: In safety-critical sectors such as robotics and automotive engineering, the deployment of Deep Reinforcement Learning (DRL) is often hindered by the bla

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs

arXiv:2607.23466v1 Announce Type: new Abstract: Parameterized and coupled partial differential equations (PDEs) are central to modeling phenomena in science and engineering, yet neural operator method

Fairness Interventions in Classification: A Study on AI Explainability

arXiv:2407.14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and

False Prophets: On the Security of World Models in Agentic Systems

arXiv:2607.23147v1 Announce Type: cross Abstract: Large language models now power autonomous agents capable of complex, multi-step tasks in different environments. Accurate and reliable execution of t

Flash-CNNCap: Capacitance Extraction via Image Mapping

arXiv:2607.23877v1 Announce Type: new Abstract: We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatia

Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow

arXiv:2602.14021v2 Announce Type: replace Abstract: Reconstructing and tracking dynamic 3D scenes is a fundamental challenge in computer vision. Existing methods typically decouple geometry from motio

researcharxiv-cs-cv

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference

arXiv:2607.24585v1 Announce Type: new Abstract: We present ELMOD - Efficient Language Model for On-Device Deployment - a compact (2.7B) German language model designed for efficient inference on resour

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

arXiv:2607.24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this

From transcription to semantic corpus analysis: unsupervised learning of sentence representations for ancient languages

arXiv:2607.24542v1 Announce Type: new Abstract: Automatic Text Recognition (ATR) now supplies digital humanities with large volumes of unstructured, heterogeneous, and often noisy text in ancient lang

Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias

arXiv:2607.22837v1 Announce Type: cross Abstract: Many organizations aim to adapt language models for internal use, both to improve performance on domain-specific tasks and to address privacy concerns

gamma-Bridge: A Look-Parametric Diffusion Bridge

model-releasesarxiv-cs-cv

arXiv:2607.22719v1 Announce Type: new Abstract: Multiplicative Gamma noise is a signal-dependent degradation in coherent imaging; synthetic aperture radar (SAR) despeckling is its most prominent real-

Gaze-Anchored Social Net: Decoding Implicit Relations via Joint Modeling

model-releasesarxiv-cs-cv

arXiv:2607.22847v1 Announce Type: new Abstract: Human gaze does more than point to visual targets; it serves as a subtle indicator of social intent within static images, whereas standard models typica

GEMCo: A Validated, Ethically Releasable Proxy for Inaccessible Counselling Data

arXiv:2607.23621v1 Announce Type: new Abstract: This paper presents GEMCo, a releasable, human-written proxy for inaccessible counselling data: 86 complete German e-mail counselling conversations (728

Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories

arXiv:2607.23454v1 Announce Type: new Abstract: Data-driven remaining useful life (RUL) prediction requires complete degradation trajectories for training, yet such run-to-failure data are scarce and

safetyarxiv-cs-lg

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607.24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly

researcharxiv-cs-ai

GOTS: Greedy Orthogonal Token Selection for High-Resolution Vision-Language Models

arXiv:2607.23913v1 Announce Type: new Abstract: Modern vision-language models (VLMs) increasingly rely on dynamic or high-resolution visual encoding, producing thousands of visual tokens that substant

Tutorials

Gradient-Free Continual Learning

arXiv:2504.01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting. Standard continual learni

tutorialsarxiv-cs-lg

Grounding latent algorithm routing in transformer reasoning

arXiv:2607.24471v1 Announce Type: new Abstract: A central question in the in-context learning literature is whether transformers can organize episode-level adaptation around different inductive-bias f

Hallucination Rates in Language Generation

arXiv:2607.23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algor

Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model

arXiv:2107.08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanc

How Context Attribution Handles What the Model Already Knows

arXiv:2607.23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response. Recent works show the ini

I built a tool to actually test which weights matter before quantizing, instead of guessing (Qwen3.6-27B, 3 builds: Bedrock/Tightrope/Gambit)

model-releasesr-localllama

Most quantization works like this: pick a bit depth, apply it everywhere, maybe let imatrix take a rough guess at what matters, ship it. Most don't check which specific weight groups can take a hit an

IJCB-AFMFR 2026: Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data

arXiv:2607.24422v1 Announce Type: new Abstract: This paper presents a summary of the Competition on Adapting Foundation Models for Face Recognition Using Synthetic Training Data (AFMFR), held at the 2

safetyarxiv-cs-cv

Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments

arXiv:2607.23503v1 Announce Type: cross Abstract: Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputat

Infinite-Precision Autoregressive Modeling for Vector Graphics and Layouts

arXiv:2601.05680v2 Announce Type: replace-cross Abstract: While Transformer-based autoregressive models excel in data generation, their token discretization strategy inherently limits their precision

Applications

Inter-Reflective Gaussian Splatting for Robust and Efficient Inverse Rendering

arXiv:2607.22780v1 Announce Type: cross Abstract: Faithful inverse rendering requires visibility and indirect radiance to explain secondary illumination and inter-reflection, yet rasterization-oriente

applicationsarxiv-cs-cv

Interesting. Grok 4.6 releases around August 7. This will be the 1.5T model with significantly improved SFT & RL. Grok 4.7 will be the 2.1T …

model-releaseselon-musk--x

Interesting. Grok 4.6 releases around August 7. This will be the 1.5T model with significantly improved SFT & RL. Grok 4.7 will be the 2.1T model released a few weeks later. This will be better than 4

K-Survival Means

arXiv:2607.24405v1 Announce Type: new Abstract: In this work, we propose K-SurvMeans, a novel extension of K-Means for clustering survival data. The method explicitly uses the survival outcome in the

Local Ai

KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability

arXiv:2607.24730v1 Announce Type: cross Abstract: Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In cl

local-aiarxiv-cs-ai

Applications

Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

arXiv:2607.22987v1 Announce Type: cross Abstract: Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settin

applicationsarxiv-cs-ai

Language Shapes Instruction Hierarchy Compliance in Multilingual LLMs

arXiv:2607.23545v1 Announce Type: new Abstract: Instruction hierarchy (IH) requires models to prioritize instructions by source, ensuring that higher-priority instructions override lower-priority ones

LanteRn: Latent Visual Structured Reasoning

arXiv:2603.25629v2 Announce Type: replace Abstract: While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs). As a result, m

researcharxiv-cs-cv

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning

arXiv:2607.23837v1 Announce Type: cross Abstract: Large language models generalize well to individual tasks but lack an inherent mechanism for learning them sequentially, leading to catastrophic forge

researcharxiv-cs-cl