Search: “evaluation”

How Reliable are LLMs for Reasoning on the Re-ranking task?

arXiv:2508.18444v2 Announce Type: replace-cross Abstract: With the improving semantic understanding capability of Large Language Models (LLMs), they exhibit a greater awareness and alignment with huma

Local Ai

Incremental Gauss-Newton Descent for Machine Learning

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DGX agent

arXiv:2408.05560v2 Announce Type: replace Abstract: Stochastic gradient updates are widely used for their efficiency and scalability, but their effective step sizes can depend strongly on feature scal

local-aiarxiv-cs-lg

27 May 2026

Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

arXiv:2605.26307v1 Announce Type: cross Abstract: Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly

It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty

arXiv:2605.27288v1 Announce Type: cross Abstract: Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behav

Hardware

JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search

arXiv:2605.26636v1 Announce Type: cross Abstract: We introduce JetViT, a novel family of hybrid-architecture Vision Transformer (ViT) models that match the accuracy of state-of-the-art full-attention

hardwarearxiv-cs-ai

Joint Instance Segmentation and Geometric Attribute Regression for Roof Structures in Aerial Imagery

arXiv:2605.26370v1 Announce Type: new Abstract: We present a method for jointly predicting instance-level roof segment masks together with three continuous geometric attributes -- building height, roo

researcharxiv-cs-cv

Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation

arXiv:2511.14993v3 Announce Type: replace-cross Abstract: This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis.

KARMA: Karma-Aligned Reward Model Adaptation

arXiv:2605.26738v1 Announce Type: new Abstract: Human communication depends on implicit social signals where effectiveness is shaped by tone, context, and conversational norms rather than semantic con

safetyarxiv-cs-cl

LaRe: Latent Refocusing for Multimodal Reasoning

model-releasesarxiv-cs-cl

arXiv:2511.02360v4 Announce Type: replace-cross Abstract: Chain of Thought (CoT) reasoning enhances logical performance by decomposing complex tasks, yet its multimodal extension faces a trade-off. Th

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2505.21285v4 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effecti

LearnedCache: An eBPF-Integrated Perceptron-Based Eviction Policy for the Linux Page Cache

arXiv:2605.26168v1 Announce Type: cross Abstract: Linux is the foundation of the digital age, accounting for the majority of the cloud and mobile OS markets. Any device that runs Linux uses the Linux

safetyarxiv-cs-lg

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming

arXiv:2605.26828v1 Announce Type: new Abstract: Learning from Demonstration~(LfD) should capture not only how a task is executed, but also its high-level task structure that explains the demonstrated

agentsarxiv-cs-ro

Learning GUI Grounding with Spatial Reasoning from Visual Feedback

arXiv:2509.21552v2 Announce Type: replace-cross Abstract: Graphical User Interface (GUI) grounding is commonly framed as a coordinate prediction task -- given a natural language instruction, generate

agentsarxiv-cs-cl

Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data

arXiv:2605.26271v1 Announce Type: cross Abstract: We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This settin

researcharxiv-cs-lg

Left-Right Symmetry Breaking in CLIP-style Vision-Language Models Trained on Synthetic Spatial-Relation Data

arXiv:2601.12809v2 Announce Type: replace-cross Abstract: Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired,

Lessons from Penetration Tests on Large-Scale Agent Systems

arXiv:2605.27042v1 Announce Type: cross Abstract: As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of

agentsarxiv-cs-ai

Tutorials

Leveraging Text-to-Image Diffusion Models for Unsupervised Visual Object Tracking

arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio

tutorialsarxiv-cs-cv

Applications

Leveraging Visual Signals for Robust Token-Level Uncertainty in Vision-Language Generation

arXiv:2605.27136v1 Announce Type: new Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment.

applicationsarxiv-cs-cv

LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring

model-releasesarxiv-cs-cl

arXiv:2605.27088v1 Announce Type: new Abstract: Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimiza

LLMs versus the Halting Problem: Characterizing Program Termination Reasoning

arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecid

Local Ai

Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences

arXiv:2605.26756v1 Announce Type: new Abstract: Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorizati

local-aiarxiv-cs-lg

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding

arXiv:2605.27365v1 Announce Type: cross Abstract: Vision-language models (VLMs) commonly formulate visual grounding and detection as a coordinate-token generation problem, serializing each 2D box into

LongCat-Video-Avatar 1.5 Technical Report

model-releasesarxiv-cs-cv

arXiv:2605.26486v1 Announce Type: new Abstract: Despite advances in audio-driven video generation, achieving commercial-grade stability remains challenging. We present LongCat-Video-Avatar 1.5, an upg

LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models

arXiv:2605.27254v1 Announce Type: cross Abstract: Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context sele

Applications

LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes

arXiv:2601.15283v2 Announce Type: replace Abstract: We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative

applicationsarxiv-cs-cv

Measuring Prediction Uncertainty in Neural Cellular Automata

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

safetyarxiv-cs-lg

MemFail: Stress-Testing Failure Modes of LLM Memory Systems

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph St

MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration

arXiv:2605.26546v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems foc

Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

arXiv:2603.25415v2 Announce Type: replace Abstract: Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Org

MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

model-releasesarxiv-cs-lg

arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates

Local Ai

MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv:2605.26842v1 Announce Type: cross Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to pro

local-aiarxiv-cs-cl

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations

arXiv:2605.26647v1 Announce Type: cross Abstract: Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLM

Hardware

Morphling: Fast, Fused, and Flexible GNN Training at Scale

arXiv:2512.01678v5 Announce Type: replace Abstract: Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-inten

hardwarearxiv-cs-lg

Multi-Agent Causal Discovery Using Large Language Models

arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical c

Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery

arXiv:2605.26381v1 Announce Type: new Abstract: We present a multi-modal classification framework that fuses satellite and street-level imagery through a Perceiver IO architecture operating on spatial

researcharxiv-cs-cv

Hardware

Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control

arXiv:2605.26430v1 Announce Type: new Abstract: Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post di

hardwarearxiv-cs-ro

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

arXiv:2605.26895v1 Announce Type: cross Abstract: Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the

Neural Bayesian Sequential Routing

arXiv:2605.26147v1 Announce Type: new Abstract: Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited vis

agentsarxiv-cs-lg

Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

arXiv:2605.26942v1 Announce Type: new Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce una

Applications

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values

arXiv:2512.19602v2 Announce Type: replace Abstract: Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. Howe

applicationsarxiv-cs-cv

Normalizing Flows on Quotient Manifolds via Boundary Quotients

arXiv:2511.22882v3 Announce Type: replace Abstract: We introduce boundary quotients and present a framework for learning densities on manifolds that arise as boundary quotients of simpler domains. We

researcharxiv-cs-lg

Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation

arXiv:2605.27194v1 Announce Type: new Abstract: Distilling demonstration effects into hidden-space interventions offers a lightweight alternative to full finetuning. However, existing multimodal varia

researcharxiv-cs-cl

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

On the Error-Correcting Effects of Stochasticity in Discrete Diffusion

arXiv:2605.26582v1 Announce Type: cross Abstract: Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampli

On the Robustness of Machine Unlearning for Vision-Language Models

arXiv:2605.26992v1 Announce Type: new Abstract: Vision-language models (VLMs) may memorize undesirable information from training data, motivating growing interest in machine unlearning. In this work,

researcharxiv-cs-cv

On the Sensitivity of Instruction-tuned LLMs to Harmful Sentences in Long Inputs

model-releasesarxiv-cs-cl

arXiv:2510.05864v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly operate on long inputs, yet their behavior when harmful sentences are sparsely embedded within such inputs