Search: “benchmark”

From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG

arXiv:2605.07273v1 Announce Type: cross Abstract: Multimodal RAG systems increasingly rely on vision-language retrievers to ground visual queries in external textual evidence. Existing adversarial stu

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Applications

From Feasible to Practical: Pareto-Optimal Synthesis Planning

DGX agent

arXiv:2605.07521v1 Announce Type: new Abstract: Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing prima

applicationsarxiv-cs-ai

11 May 2026

Tutorials

From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting

arXiv:2605.07203v1 Announce Type: new Abstract: Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D a

tutorialsarxiv-cs-cv

From Pixels to Prompts: Vision-Language Models

arXiv:2605.07544v1 Announce Type: new Abstract: When you read a paper about a new Vision-Language Model today, it can be easy to forget how strange this idea would have sounded not so long ago. Teachi

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems

arXiv:2506.04565v2 Announce Type: replace-cross Abstract: Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retriever

researcharxiv-cs-cl

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth

arXiv:2605.07607v1 Announce Type: new Abstract: Image-to-point cloud registration is often challenged by viewpoint changes, cross-modal discrepancies, and repetitive textures, which induce scale ambig

researcharxiv-cs-cv

Future-proof your data strategy: AlloyDB adds PostgreSQL 18 and new Extended Support

As you look out at your 2026 infrastructure roadmap, your goal is to balance the need for rapid innovation with operational stability. You shouldn't have to choose between adopting the latest database

safetygoogle-cloud-ai

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

arXiv:2605.06734v1 Announce Type: cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (

Hardware

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control

arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject

hardwarearxiv-cs-ro

Agents

GEM: Generating LiDAR World Model via Deformable Mamba

arXiv:2605.07326v1 Announce Type: new Abstract: World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, p

agentsarxiv-cs-cv

Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions

arXiv:2512.20974v3 Announce Type: replace-cross Abstract: Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation

GESR: Graph-Based Edge Semantic Reconstruction for Stealthy Communication Detection with Benign-Only Training

arXiv:2605.07536v1 Announce Type: cross Abstract: Detecting stealthy malicious communications from flow logs under benign-only training remains a critical challenge in network security. Malicious comm

local-aiarxiv-cs-lg

GLiGuard: Schema-Conditioned Classification for LLM Safeguard

model-releasesarxiv-cs-cl

arXiv:2605.07982v1 Announce Type: new Abstract: Ensuring safe, policy-compliant outputs from large language models requires real-time content moderation that can scale across multiple safety dimension

GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval

arXiv:2509.23370v2 Announce Type: replace Abstract: The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributiona

safetyarxiv-cs-cv

GraphReAct: Reasoning and Acting for Multi-step Graph Inference

arXiv:2605.07357v1 Announce Type: new Abstract: Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, ex

Head Similarity: Modeling Structured Whole-Head Appearance Beyond Face Recognition

model-releasesarxiv-cs-cv

arXiv:2605.07766v1 Announce Type: new Abstract: Many vision applications require identity consistency beyond strict biometric recognition, especially under non-frontal views or when facial cues are mi

HiDream-O1-Dev vs ZImage Base (style comparison)

local-air-stablediffusion

A Reddit discussion comparing HiDream-O1-Image-Dev (the newer, 8B pixel-native distilled model) with ZImage Base, examining stylistic and performance differences between the two text-to-image generati

Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.07512v1 Announce Type: new Abstract: Class-incremental learning aims to continuously acquire new knowledge while preserving previously learned information, thereby mitigating catastrophic f

Hierarchical Task Network Planning with LLM-Generated Heuristics

arXiv:2605.07707v1 Announce Type: new Abstract: HTN planning is a variation of classical planning where, instead of searching for a linear sequence of actions, an algorithm decomposes higher-level tas

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study

arXiv:2605.05340v2 Announce Type: replace-cross Abstract: As Vision-Language Models (VLMs) are increasingly deployed as autonomous cognitive cores for embodied assistants, evaluating their privacy awa

How Value Induction Reshapes LLM Behaviour

arXiv:2605.07925v1 Announce Type: new Abstract: Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and em

safetyarxiv-cs-cl

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training

arXiv:2605.07316v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning

Inference Time Causal Probing in LLMs

arXiv:2605.07631v1 Announce Type: new Abstract: Causal probing methods aim to test and control how internal representations influence the behavior of generative models. In causal probing, an intervent

InfoGeo: Information-Theoretic Object-Centric Learning for Cross-View Generalizable UAV Geo-Localization

arXiv:2605.07099v1 Announce Type: new Abstract: Cross-view geo-localization (CVGL) is fundamental for precise localization and navigation in GPS-denied environments, aiming to match ground or UAV imag

safetyarxiv-cs-cv

Applications

Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure

arXiv:2605.07057v1 Announce Type: new Abstract: Online reinforcement learning (RL) relies on the Markov property for guaranteed performance, but real-world applications often lack well-defined states

applicationsarxiv-cs-lg

InvThink: Premortem Reasoning for Safer Language Models

arXiv:2510.01569v3 Announce Type: replace Abstract: We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before gener

Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies

arXiv:2510.22944v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a c

It Just Takes Two: Scaling Amortized Inference to Large Sets

arXiv:2605.07972v1 Announce Type: cross Abstract: Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In ma

KL for a KL: On-Policy Distillation with Control Variate Baseline

arXiv:2605.07865v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OP

Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs

model-releasesarxiv-cs-lg

arXiv:2605.05957v2 Announce Type: replace Abstract: LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply r

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting

arXiv:2602.02832v3 Announce Type: replace Abstract: Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and

researcharxiv-cs-lg

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

arXiv:2605.06955v1 Announce Type: cross Abstract: Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the g

local-aiarxiv-cs-ai

LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation

arXiv:2605.07517v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances the factual grounding of Large Language Models by conditioning their outputs on external documents. Howe

Latent Reasoning VLA: Latent Thinking and Prediction for Vision-Language-Action Models

arXiv:2602.01166v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models benefit from chain-of-thought (CoT) reasoning, but existing approaches incur high inference overhead and rely on

researcharxiv-cs-ro

LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification

arXiv:2605.07315v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves large language models (LLMs) on difficult tasks, but it also makes inference expensive because every intermedi

researcharxiv-cs-cl

Learning Agent Routing From Early Experience

model-releasesarxiv-cs-cl

arXiv:2605.07180v1 Announce Type: new Abstract: LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capa

Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning with LLM Agents

arXiv:2605.06957v1 Announce Type: new Abstract: We present a dynamic policy-learning approach that combines generalized planning and hierarchical task decomposition for LLM-based agents. Our method, H

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation

model-releasesarxiv-cs-lg

arXiv:2605.07038v1 Announce Type: new Abstract: Risk-aware navigation should be selective: a policy should expose evasive degrees of freedom only when the local scene admits a lower-risk feasible mane

Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis

arXiv:2511.09907v5 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-q

Learning to Track Instance from Single Nature Language Description

arXiv:2605.07064v1 Announce Type: new Abstract: How to achieve vision-language (VL) tracking using natural language descriptions from a video sequence extbf{without relying on any bounding-box ground

safetyarxiv-cs-cv

LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

arXiv:2605.07019v1 Announce Type: cross Abstract: Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into lo

LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning

arXiv:2605.07505v1 Announce Type: new Abstract: Developing lightweight, on-device vision-language GUI agents is essential for efficient cross-platform automated interaction. However, current on-device

local-aiarxiv-cs-ai

Tutorials

LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

arXiv:2605.06676v1 Announce Type: cross Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compress

tutorialsarxiv-cs-cl

Lossy Common Information in a Learnable Gray-Wyner Network

arXiv:2601.21424v3 Announce Type: replace-cross Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and in

researcharxiv-cs-cv

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

arXiv:2605.05225v2 Announce Type: replace-cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP)

Mage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate

arXiv:2605.07342v1 Announce Type: cross Abstract: Compile-pass rate is the dominant evaluation signal for LLM code generation, yet for multi-component domain-specific artifacts it can be actively misl

MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge

arXiv:2507.21183v5 Announce Type: replace-cross Abstract: As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with

MAS-Algorithm: A Workflow for Solving Algorithmic Programming Problems with a Multi-Agent System

arXiv:2605.05949v2 Announce Type: replace Abstract: Algorithmic problem solving serves as a rigorous testbed for evaluating structured reasoning in AI coding systems, as it directly reflects a model's