Search: “model-releases”

BarkNet-Lite: A Lightweight Texture and Colour Network with the BarkBD Benchmark for Bark-Based Tree Species Recognition in Bangladesh

arXiv:2609.07600v1 Announce Type: new Abstract: Tree species recognition supports forest inventory and biodiversity monitoring but still depends on scarce taxonomic expertise. Bark is visible year-rou

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Model Releases

BEFORE THE FLIP: Measuring Hidden Score Shifts In Quantized Vision Language Models Before The Answer Changes for Visual Question Answering

DGX agent

arXiv:2609.06922v1 Announce Type: new Abstract: Quantization makes vision language models (VLMs) cheaper to store and run by using fewer bits to represent their weights. While unchanged answers on vis

model-releasesarxiv-cs-cv

9 Sept 2026

Benchmarking Dexterity of Multifingered Robot Hands: A Review and Perspective

model-releasesarxiv-cs-ro

arXiv:2609.05585v1 Announce Type: new Abstract: Robot hands are a key interface between AI and the physical world, making advances in robotic dexterity essential to realizing the vision of physical AI

Benchmarking Storage Systems for Machine Learning Workloads Using NIO Bench

arXiv:2609.05418v1 Announce Type: cross Abstract: Machine learning training workloads place unique demands on storage systems, yet most existing benchmarks focus on computational throughput rather tha

Beyond Retraining-Free MoE Compression: A Cost-Normalized Study of Post-Compression Adjustment

arXiv:2609.06076v1 Announce Type: new Abstract: Retraining-free MoE compression reduces deployment memory by pruning or merging experts, but often treats the compressed checkpoint as the final artifac

Beyond the Verdict: Evidence-Aligned Evaluation of Visual Prompt-Injection Guardrails

arXiv:2609.05535v1 Announce Type: new Abstract: Verdict-only evaluation does not reveal whether a vision-language model (VLM) used the visual evidence that should support its decision. We study this p

Beyond Worst-Case Coreset Bounds for k-Clustering via Determinantal Sampling

arXiv:2609.06394v1 Announce Type: cross Abstract: Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computati

Bi-HYCO: Bi-Objective Cooperative Learning for PDE Parameter Identification under Fragmented Observations

arXiv:2609.06511v1 Announce Type: new Abstract: Physical and synthetic models may describe complementary aspects of the same PDE-governed system while receiving different, possibly fragmented, observa

Bigger Text Encoders Can Hurt CLIP Zero-Shot Performance

arXiv:2609.05730v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) is a building block of many machine learning applications. Scaling laws have guided resource allocation fo

BrachistoneLR: A Brachistochrone-Inspired Learning-Rate Schedule and a Controlled Benchmark of Scheduling Policies

arXiv:2609.08069v1 Announce Type: new Abstract: The learning-rate schedule is a consequential choice in training deep networks, yet the policies in common use are heuristic, and published comparisons

Bridging Language and Physics: Automated Design of Continuum Robots with Large Language Models

model-releasesarxiv-cs-ro

arXiv:2609.08220v1 Announce Type: new Abstract: Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineff

Budgeted Task-Aware Acquisition of Dynamic Networks

arXiv:2609.05862v1 Announce Type: new Abstract: Learning on dynamic graphs is difficult when changes in the underlying network are only partially observed. Acquiring current graph information incurs o

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

arXiv:2609.06008v1 Announce Type: cross Abstract: We present Cadence, an error-bounded lossy compressor for numeric time series pairing a 330M-parameter time-series foundation model (Google TimesFM-3)

CALIPER: Clean Scenes Cannot Rank Physical Inference in Pretrained Visual Representations

arXiv:2609.08250v1 Announce Type: cross Abstract: How far a pushed object slides depends on its mass and friction, which no single image reveals. Pretrained visual encoders are increasingly used as th

CALM: Class-wise Agreement and Label-gated Disagreement Modulation for Decentralized Federated Learning

arXiv:2609.05884v1 Announce Type: new Abstract: Conventional federated learning relies on parameter averaging, which forces clients to be doubly homogeneous: all must run an identical architecture, an

CARE: Confounder-Aware Aggregation for Reliable LLM Evaluation

arXiv:2603.00039v2 Announce Type: replace Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they im

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

arXiv:2404.06349v3 Announce Type: replace Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual re

CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices

arXiv:2510.25323v4 Announce Type: replace Abstract: Normalizing flows are deep generative models that enable efficient likelihood estimation and sampling through invertible transformations. A key chal

Characterizing Privacy-Audit Alignment in Behavioral Audit of Machine Unlearning

arXiv:2606.14518v2 Announce Type: replace Abstract: The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there is no agreed-upon s

Charts Are Beyond Pixels: Probing for Layer-Wise Chart Understanding and Editing

arXiv:2609.08657v1 Announce Type: new Abstract: Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This struct

ChatBEV: Empowering Traffic Scene Understanding and Simulation via Vision-Language Model

arXiv:2503.13938v3 Announce Type: replace Abstract: Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such a

CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment

arXiv:2609.07188v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new par

ChildGaze: A Benchmark Dataset for Collaborative Behavior Understanding in Children

arXiv:2609.06353v1 Announce Type: new Abstract: Understanding collaborative behavior in children is important for analyzing social participation, peer interaction, shared attention, and engagement dur

Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning

arXiv:2609.08709v1 Announce Type: new Abstract: Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings

CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation

arXiv:2609.07058v1 Announce Type: new Abstract: Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imagin

Companion-style QA Assistance in Ego-Vision

arXiv:2609.06721v1 Announce Type: new Abstract: AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companio

Compressed Recurrent Feedback in Tsetlin Machines: A Reproducible Boolean-FSM Study

arXiv:2609.06133v1 Announce Type: new Abstract: Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin M

ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics

model-releasesarxiv-cs-ro

arXiv:2609.07838v1 Announce Type: new Abstract: Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Lan

Condition aware learning enables robust prediction of oligonucleotide melting behavior across diverse chemistries and assay conditions

arXiv:2609.05454v1 Announce Type: cross Abstract: Oligonucleotide melting temperature is a fundamental determinant of nucleic acid hybridization and underpins the design of molecular diagnostics, poly

Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models

arXiv:2609.06880v1 Announce Type: new Abstract: Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated perspec

CoordFormer: Give Me Any Coordinates and I Will Give You Labels

arXiv:2609.08660v1 Announce Type: new Abstract: Semantic segmentation on very-high-resolution images remains challenging due to the high computational cost and the difficulty of capturing fine-grained

CoRe-SAM3: Conditional Semantic--Visual Reconciliation for SAM3 Crack Segmentation

arXiv:2609.05816v1 Announce Type: new Abstract: Crack segmentation requires a model to recognize target semantics while accurately recovering thin, low-contrast, and topologically continuous local str

CoRL: Co-Evolutionary Reinforcement Learning for Adaptive Indirect Prompt-Injection Attacks and Defenses

arXiv:2609.07529v1 Announce Type: new Abstract: Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions in

CoSA: Correlation-Guided Change A ttention with Learnable Residual Gating for Remote Sensing Change Detection

arXiv:2609.08914v1 Announce Type: new Abstract: Pixel-level annotation of fixed traffic-camera imagery is expensive, while crosswalk models trained from street-level imagery face a substantial viewpoi

Cost-Optimal LLM Routing with Limited User Feedback under User Satisfaction Guarantees

arXiv:2606.19376v2 Announce Type: replace Abstract: Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost. Users expe

Counterfactual Tests for Measuring Chain-of-Thought Faithfulness in Visual Language Models

arXiv:2609.06704v1 Announce Type: new Abstract: Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model's decision-making process. While methods for measuring the

CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline

arXiv:2609.07336v1 Announce Type: new Abstract: Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for i

CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation

arXiv:2609.05518v1 Announce Type: new Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, moti

CrowdTraj: A Benchmark for Dense Crowd Trajectory Prediction in Realistic Crowded Environments

arXiv:2609.07685v1 Announce Type: new Abstract: In real-world applications, pedestrian trajectory prediction models rely on inputs from detection and tracking systems. Prior trajectory prediction benc

Damage-Aware Bandit Pruning for Vision and Language Transformers

arXiv:2609.05448v1 Announce Type: cross Abstract: Structured post-training pruning of transformers requires selecting complete functional units whose suppression causes limited degradation. We formula

Data Scout: Targeted Web Crawling for Domain-Specific Pretraining Corpora

arXiv:2609.05766v1 Announce Type: new Abstract: The dominant approach to building domain-specific pretraining corpora is to filter large web archives such as CommonCrawl. This works well for popular d

DataFlex-RL: An Evaluation Platform for RLVR Data Policies

arXiv:2609.06107v1 Announce Type: new Abstract: Data policies for reinforcement learning with verifiable rewards (RLVR) determine which rollouts are used, how strongly they are weighted, and which dom

DDPM Score Matching and Distribution Learning

arXiv:2504.05161v2 Announce Type: replace-cross Abstract: Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key r

Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction

arXiv:2609.07756v1 Announce Type: new Abstract: Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one mi

Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors

arXiv:2507.05134v2 Announce Type: replace Abstract: We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimensi

Deposon: An Auditable, Conservation-Guaranteed, Game-Theoretically Tested Scattering Layer over LLM Reasoning Paths

arXiv:2609.09001v1 Announce Type: cross Abstract: Multi-step LLM reasoning lacks a machine-recheckable ledger: discarded reasoning paths leave no auditable record. We propose the Deposon scattering la

DF26: We Cannot Tell Fake From Real Anymore

arXiv:2609.07369v1 Announce Type: new Abstract: We introduce DF26, a novel benchmark for detecting AI-generated videos containing fully synthetic clips produced by recent text-to-video and image-to-vi

Disparity Has a Sign: Stereo Matching Beyond the Zero-Disparity Plane

arXiv:2609.06809v1 Announce Type: new Abstract: Modern stereo matching models fail when disparity crosses zero, with end-point error (EPE) rising by 4.6-37imes. Yet stereoscopic content, from cinema 3