Search: “benchmark”

arXiv:2509.10656v2 Announce Type: replace-cross Abstract: For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. Rather than relying

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Research

Separating Representation from Reconstruction Enables Scalable Text Encoders

DGX agent

arXiv:2607.04011v1 Announce Type: cross Abstract: While decoders have rapidly scaled, encoders have remained largely unchanged since BERT. We revisit this disparity by frozen backbone evaluation via p

researcharxiv-cs-ai

7 Jul 2026

SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests

arXiv:2510.09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning. In particular, tree detection and taxonomic classificati

SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits

arXiv:2607.05187v1 Announce Type: new Abstract: As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of B

SMF-VO: Direct Ego-Motion Estimation via Sparse Motion Fields

arXiv:2511.09072v2 Announce Type: replace-cross Abstract: Traditional Visual Odometry (VO) and Visual Inertial Odometry (VIO) methods rely on a 'pose-centric' paradigm, which computes absolute camera

SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation

arXiv:2607.03103v1 Announce Type: cross Abstract: Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding k

Agents

Social Networks of LLM Agents

arXiv:2607.03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe co

agentsarxiv-cs-lg

SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards

arXiv:2511.07403v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but continue to struggle with spatial rea

Safety

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity

arXiv:2607.04189v1 Announce Type: new Abstract: Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift

safetyarxiv-cs-lg

Spectral Rewiring for Exploration, Purification, and Model Merging

arXiv:2607.03065v1 Announce Type: cross Abstract: Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-re

SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery

arXiv:2602.17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known clas

Applications

SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness

arXiv:2607.02886v1 Announce Type: cross Abstract: Deploying AI-generated video detectors in real-world services demands an ultra-low false positive rate (FPR) on real videos to avoid falsely rejecting

applicationsarxiv-cs-ai

Stable Global Weighting of Flow Mixtures using Simplex Exponential Moving Average

arXiv:2607.03809v1 Announce Type: new Abstract: Normalising flows provide a powerful variational family for approximate inference, yet individual architectures often fail to generalise across heteroge

researcharxiv-cs-lg

STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation

arXiv:2607.02922v1 Announce Type: new Abstract: Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense sp

researcharxiv-cs-cv

Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding

arXiv:2607.03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effecti

Applications

Steering Optimisation Trajectories in Diffusion Representation Learning

arXiv:2607.05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviou

applicationsarxiv-cs-ai

STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition

arXiv:2607.03089v1 Announce Type: cross Abstract: HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, clo

Streaming Model Cascades for Semantic SQL

arXiv:2604.00660v2 Announce Type: replace-cross Abstract: Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference o

Structured Prompting and Automated Evaluation in Fixed Synthetic Japanese-Language Counseling Dialogues

arXiv:2507.02950v3 Announce Type: replace-cross Abstract: Large language models (LLMs) may support counseling training, yet evidence from Japanese-language interactions and automated quality ratings r

TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding

model-releasesarxiv-cs-cl

arXiv:2607.03236v1 Announce Type: new Abstract: Diffusion language models (DLLMs) generate text by iteratively denoising masked positions, exposing a trajectory of predictive distributions rather than

Agents

Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving

arXiv:2607.04661v1 Announce Type: cross Abstract: Reconstructing 3D scene structures from sparse, low-overlap observations remains a fundamental challenge in autonomous driving. Recent state-of-the-ar

agentsarxiv-cs-ai

Taste-aware music retrieval from audio embeddings

arXiv:2607.03296v1 Announce Type: cross Abstract: Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimed

Teaching Code LLMs to Reason with Intermediate Formal Specifications

model-releasesarxiv-cs-cl

arXiv:2607.04232v1 Announce Type: cross Abstract: Unlike natural-language specifications, executable formal specifications provide machine-checkable constraints for verifying, debugging, and repairing

TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model

arXiv:2607.03810v1 Announce Type: new Abstract: Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Cur

The Multipath Blind Spot: K-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations

arXiv:2607.04101v1 Announce Type: new Abstract: Monocular depth foundations predict domain-general relative depth but lack absolute scale; a handful of sparse metric anchors from a range sensor can ca

The Role of Rigor in Artificial Intelligence

arXiv:2607.03634v1 Announce Type: new Abstract: Artificial intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with m

Hardware

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

arXiv:2607.03411v1 Announce Type: cross Abstract: Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability,

hardwarearxiv-cs-ai

Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens

model-releasesarxiv-cs-cl

arXiv:2602.13517v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). Howev

TimeThink: Reasoning with Time for Video LLMs

arXiv:2607.05089v1 Announce Type: new Abstract: Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Vi

researcharxiv-cs-cv

Token-Based Affordance Grounding with Large Vision-Language Models

arXiv:2607.03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodie

Token-level Response-visual Attention Guidance for Multimodal LLMs Knowledge Distillation

arXiv:2607.02593v1 Announce Type: cross Abstract: While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying sole

researcharxiv-cs-cl

Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models

arXiv:2607.04199v1 Announce Type: new Abstract: The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on

TORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language Models

arXiv:2607.04593v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large

Toward Trustworthy Large Language Model Agents in Healthcare

arXiv:2607.05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high admini

Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation

arXiv:2603.17673v2 Announce Type: replace-cross Abstract: LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to repro

Towards transferable lightweight neuromorphic computing through a model-free temporal-switch framework

arXiv:2607.02608v1 Announce Type: cross Abstract: Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. Howev

Local Ai

Track the Noise, Move the World:3D-Grounded Motion-Consistent Noise for Controllable Video Generation

arXiv:2607.02798v1 Announce Type: new Abstract: Modern image-and-text-to-video diffusion models can synthesize highly realistic videos by iteratively denoising an initial Gaussian noise tensor conditi

local-aiarxiv-cs-cv

Safety

Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment

arXiv:2607.04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of 'rollout then update', which inevitably res

safetyarxiv-cs-ai

U-Joint CAAMS: Experimental Evaluation of a Universal-Joint Continuum Manipulator for Aerial Manipulation

model-releasesarxiv-cs-ro

arXiv:2607.03321v1 Announce Type: new Abstract: Continuum manipulators mounted on multi-rotor UAVs enable compliant aerial manipulation, but payloads and propeller downwash amplify out-of-plane bendin

Safety

Uncertainty-Aware Abstention in Large Language Models with Provable Alignment Guarantees

arXiv:2607.04430v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in question answering (QA) systems, yet they may generate hallucinated or misaligned responses wi

safetyarxiv-cs-cl

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching

arXiv:2603.27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and

Tutorials

Verifier-free Test-Time Sampling for Vision-Language-Action Models

arXiv:2510.05681v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in

tutorialsarxiv-cs-ai

Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models

arXiv:2607.02819v1 Announce Type: cross Abstract: Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers.

VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs

arXiv:2511.20272v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have become adept at recognizing objects, they often lack the intuitive, human-like understanding of

VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining

arXiv:2607.02707v1 Announce Type: new Abstract: Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provid

researcharxiv-cs-cv

Wasserstein Residuals: Learning Gradient Flows from Population Dynamics

arXiv:2607.04738v1 Announce Type: cross Abstract: Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient f

When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents

arXiv:2607.05189v1 Announce Type: cross Abstract: Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proac

When Does High-CFG Diffusion Inversion Fail? A Controlled Study of Prompt--Latent Interactions

arXiv:2607.04731v1 Announce Type: new Abstract: Text-guided diffusion inversion is central to image editing, where an image is mapped to an initial latent and then edited by replaying the denoising pr