Search: “benchmark”

arXiv:2606.29013v1 Announce Type: new Abstract: Leveraging capabilities of large language models (LLMs) in text-to-image (T2I) synthesis is an important research direction. In this work we investigate

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Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

DGX agent

arXiv:2606.29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires con

agentsarxiv-cs-ai

30 Jun 2026

Agents

Neural Stereo Video Compression with Hybrid Disparity Compensation

arXiv:2504.20383v3 Announce Type: replace Abstract: Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can

agentsarxiv-cs-cv

Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management

arXiv:2606.30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces. Instead of treating

Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision

model-releasesarxiv-cs-lg

arXiv:2505.12526v2 Announce Type: replace Abstract: Temporal graph networks suffer from irregular supervision in realworld dynamic graphs, as most minibatches contain few labeled events. The lack of l

Nonlinear mixture model motivated subspace clustering

arXiv:2606.29261v1 Announce Type: cross Abstract: We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separatio

Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions

arXiv:2602.21160v4 Announce Type: replace-cross Abstract: In safety-critical classification, the cost of failure is often asymmetric, yet Bayesian deep learning summarises epistemic uncertainty with a

safetyarxiv-cs-lg

Obliviate: Erasing Concepts from Autoregressive Image Generation Models

arXiv:2606.28643v1 Announce Type: new Abstract: The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitiga

On Test-Time Scaling for Vision-Language Models

arXiv:2606.28864v1 Announce Type: new Abstract: Test-time scaling is a paradigm where large models use additional compute at inference to achieve better performance, without changing model weights. Wh

Applications

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606.30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color. To bridge the gap between opaqu

applicationsarxiv-cs-ai

Local Ai

One Forward Beats Two: InnerZoom for Accurate and Efficient GUI Grounding

arXiv:2606.30084v1 Announce Type: new Abstract: MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong

local-aiarxiv-cs-cv

One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models

arXiv:2606.29600v1 Announce Type: cross Abstract: A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid

OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments

arXiv:2606.29786v1 Announce Type: new Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments. Although advances in foundation models have enabled open-vocabu

OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale

arXiv:2407.19633v4 Announce Type: replace Abstract: Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuri

PCGD: Physics-Guided Conditional Graph Diffusion for TCAD Device Simulation

model-releasesarxiv-cs-lg

arXiv:2606.29272v1 Announce Type: new Abstract: Technology computer-aided design (TCAD) semiconductor device simulation is fundamentally constrained by the high computational cost of iteratively solvi

Personalizing MLLMs via Reinforced Multimodal Reference Game

arXiv:2606.28845v1 Announce Type: new Abstract: Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Alt

PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation

arXiv:2606.30477v1 Announce Type: new Abstract: Segment Anything Model (SAM) has revolutionized promptable image segmentation with strong zero-shot generalization. However, its performance degrades su

Phonological Perception of Sign Language Models

arXiv:2606.28667v1 Announce Type: new Abstract: Sign languages are compositional systems where meaning arises by combining sublexical phonological parameters, such as handshape, location, and movement

safetyarxiv-cs-cl

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

arXiv:2505.22391v2 Announce Type: replace-cross Abstract: Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate div

researcharxiv-cs-ai

Agents

PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection

arXiv:2507.02393v2 Announce Type: replace Abstract: Monocular 3D object detection is crucial for scalable perception across fields like autonomous driving, robotics, and surveillance. However, progres

agentsarxiv-cs-cv

PoseShield: Neural Collision Fields for Human Self-Collision Resolution

arXiv:2606.29686v1 Announce Type: new Abstract: Self-collision remains a persistent challenge in SMPL-based human pose estimation and motion generation. Under extreme articulations or stochastic motio

Predict, Reuse, and Repair: Accelerating Dynamic Sparse Attention for Long-Context LLM Decoding

arXiv:2606.30389v1 Announce Type: new Abstract: Dynamic sparse attention (DSA) accelerates long-context LLM decoding by attending to only the top-K KV blocks relevant to each query, but it introduces

researcharxiv-cs-lg

Predicting Metastatic Risk from Primary Tissue Architecture via Distance-Aware Spatial Modeling

arXiv:2606.28676v1 Announce Type: cross Abstract: Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology. Multiple

researcharxiv-cs-ai

Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection

arXiv:2601.12033v2 Announce Type: replace Abstract: Quantization is widely adopted to reduce the computational cost of large language models (LLMs); however, its implications for fairness and safety,

safetyarxiv-cs-cl

Projected Exploitability Descent for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games

arXiv:2606.29169v1 Announce Type: cross Abstract: Many important games have more than two players and imperfect information. Existing approaches for computing Nash equilibrium, the central game-theore

ProSpec RL: Plan Ahead, then Execute

arXiv:2407.21359v2 Announce Type: replace-cross Abstract: Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental

PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF

arXiv:2606.29758v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) for Large Language Models increasingly relies on critic-free methods as a practical alternative to a

Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization

arXiv:2602.11351v2 Announce Type: replace Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond

Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System

arXiv:2606.18112v3 Announce Type: replace-cross Abstract: Agentic navigation systems require a base navigation model whose observation strategy can be externally reconfigured at inference time, becaus

Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation

arXiv:2606.29464v1 Announce Type: cross Abstract: Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently trai

RBE-Flow: Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration

arXiv:2606.30492v1 Announce Type: new Abstract: Cross-modal image registration is essential for multi-sensor perception but remains fundamentally challenging due to severe non-linear radiometric discr

ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control

arXiv:2606.30362v1 Announce Type: cross Abstract: While current Behavior Foundation Models (BFMs) provide robust control priors for humanoids, they only execute pre-defined reference motions. As a res

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

arXiv:2606.29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification

RefAlign: Representation Alignment for Reference-to-Video Generation

arXiv:2603.25743v2 Announce Type: replace Abstract: Reference-to-video (R2V) generation is a controllable video synthesis paradigm that constrains the generation process using both text prompts and re

Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts

arXiv:2606.30518v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language models by grounding generation in external context. However, it can be fragile when the retrieved

safetyarxiv-cs-cl

Rehearsed Multi-Agent Live Product Demonstrations with Real-Time Voice Question Answering

arXiv:2606.30294v1 Announce Type: new Abstract: Live product demonstrations are a recurring, high-cost activity in software organizations: a human presenter must select features, dispatch the correspo

Agents

Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

arXiv:2606.28403v1 Announce Type: cross Abstract: Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of

agentsarxiv-cs-ai

Reliability-Prioritized Fine-Grained Generation in Multimodal Large

arXiv:2606.29573v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theo

Reported Confidence in LLMs Tracks Commitment More Than Correctness

arXiv:2606.29490v1 Announce Type: cross Abstract: Confidence is an estimate of the probability that a chosen answer is correct. Verbal confidence reports are widely used as uncertainty measures in lar

Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models

model-releasesarxiv-cs-cl

arXiv:2606.29196v1 Announce Type: cross Abstract: Do language models know when they are being tested? This question matters for AI safety: a model that recognises an evaluation context could alter its

Residual-Guided Dictionary Learning for Spectrally Accurate Koopman Approximation

model-releasesarxiv-cs-lg

arXiv:2606.29083v1 Announce Type: cross Abstract: Koopman theory promises linear structure in nonlinear dynamics, but numerical Koopman spectra are easy to compute and hard to trust. A finite EDMD mat

Resonant Brane Splatting for Arbitrary-Scale Super-Resolution

arXiv:2606.29453v1 Announce Type: cross Abstract: Arbitrary-Scale Super-Resolution (ASR) reconstructs images at continuous magnification factors. Recent methods accelerate inference by replacing compu

researcharxiv-cs-ai

Rethinking Generative Reconstruction Attacks against Graph Neural Network Models

arXiv:2606.29748v1 Announce Type: new Abstract: The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sen

Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation

arXiv:2602.09305v2 Announce Type: replace Abstract: Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning (

safetyarxiv-cs-lg

Reweighting Framewise Attention in Video Transformers for Facial Expression Understanding

arXiv:2606.30611v1 Announce Type: new Abstract: Understanding facial expressions in videos requires modeling subtle and localized facial dynamics under unconstrained conditions. Although recent Vision

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606.28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is chal

RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought

arXiv:2606.15753v2 Announce Type: replace Abstract: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding th

S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence

arXiv:2606.20515v2 Announce Type: replace Abstract: Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely rema