Search: “framework”

VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

model-releasesarxiv-cs-cv

arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VI

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

VisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision

DGX agent

arXiv:2610.00666v1 Announce Type: cross Abstract: Qualitative comparison figures are central evidence in computer vision papers, and vision-language models (VLMs) are increasingly used to judge them.

model-releasesarxiv-cs-ai

2 Oct 2026

VISPA: Pluralistic Alignment via Automatic Value Selection and Activation

arXiv:2601.12758v2 Announce Type: replace-cross Abstract: As large language models are increasingly used in high-stakes domains, it is essential that their outputs reflect not average} human preferenc

VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC-Metal Benchmark

model-releasesarxiv-cs-cv

arXiv:2610.01512v1 Announce Type: new Abstract: Metal artifacts in postoperative musculoskeletal CT obscure bone-implant and adjacent soft-tissue interfaces. Many metal artifact reduction (MAR) method

Watch Your Speech: Text-aware Video-to-Speech Synthesis with Textual Conditioning

arXiv:2610.01012v1 Announce Type: new Abstract: Video-to-speech synthesis aims to generate natural-sounding speech from silent talking-face videos while ensuring phonetic accuracy. A fundamental chall

When the Judge Acts: Auditing VLM-Guided Image Selection on Culturally Situated Prompts

arXiv:2610.01243v1 Announce Type: cross Abstract: Vision-language models (VLMs) increasingly act as judges that pick the best of several generated images, so their choices decide what users see. Such

XOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization

arXiv:2610.00432v1 Announce Type: cross Abstract: Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentiall

A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees

arXiv:2609.38194v1 Announce Type: cross Abstract: Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by b

Hardware

A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling

arXiv:2609.38438v1 Announce Type: cross Abstract: Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeli

hardwarearxiv-cs-lg

A Virtuous AI is an Existential Risk

arXiv:2606.13739v2 Announce Type: replace-cross Abstract: This paper examines trade-offs between AI safety and well-being relative to (i) one of the most promising methods for finetuning super-capable

Adaptive Self-Consistency: From Black-Box Sampling to Distribution-Valued Feedback

arXiv:2609.38931v1 Announce Type: cross Abstract: Self-consistency samples many reasoning trajectories and aggregates their final answers, treating the LLM as a black box that returns one answer per t

Tutorials

Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions

arXiv:2607.11555v2 Announce Type: replace Abstract: Learning neural set functions is pivotal to a wide range of important applications, including compound selection in AI-driven drug discovery and pro

tutorialsarxiv-cs-lg

Local Ai

Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning

arXiv:2609.38339v1 Announce Type: cross Abstract: Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly trai

local-aiarxiv-cs-ai

Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

arXiv:2609.39183v1 Announce Type: new Abstract: This paper studies extbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target

Always-On Experimentation

arXiv:2609.38695v1 Announce Type: cross Abstract: Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be concei

Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations

arXiv:2609.38486v1 Announce Type: cross Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomeno

applicationsarxiv-cs-cl

Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures

model-releasesarxiv-cs-cl

arXiv:2609.39225v1 Announce Type: new Abstract: Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. While

Audible World Models: Spatially Aware Sound Generation for 3D Worlds

arXiv:2609.38444v1 Announce Type: new Abstract: Text- and image-conditioned world generators can create visually rich 3D environments, yet these worlds often remain silent or rely on soundtracks synth

safetyarxiv-cs-cv

Autoresearch in Mixed-Integer Linear and Nonlinear Programming

arXiv:2609.39360v1 Announce Type: new Abstract: Despite recent progress in autoresearch, applying it to practical operations research problems, typically formulated as NP-hard mixed-integer linear or

BELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence

arXiv:2609.39139v1 Announce Type: new Abstract: Adaptive RAG uses signals such as confidence, relevance, support, and retrieval quality to decide when to search or correct evidence. In multi-step retr

Beyond Prediction: Steering VLM Agents with Retrospective World Modeling

arXiv:2609.39101v1 Announce Type: new Abstract: Equipping VLM agents with world modeling capabilities has shown strong potential for complex reasoning and long-horizon planning, while reducing the dep

Beyond Simulation: Retain-and-Repair Neural Operators for Real-World Adaptation

arXiv:2609.39387v1 Announce Type: new Abstract: Neural operators increasingly benefit from pretraining on numerical simulations, yet adapting them for real-world prediction remains challenging. We int

applicationsarxiv-cs-lg

Agents

Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents

arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabil

agentsarxiv-cs-ai

Can Computation from Earlier Problems Help LLMs Solve New Ones?

arXiv:2609.39394v1 Announce Type: new Abstract: Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answ

CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data

arXiv:2609.39124v1 Announce Type: new Abstract: Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many spec

applicationsarxiv-cs-lg

CellMSA: Context Modeling for Single-Cell Representation Learning

arXiv:2609.38908v1 Announce Type: cross Abstract: Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical bat

Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners

arXiv:2609.40208v1 Announce Type: new Abstract: Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challe

researcharxiv-cs-ro

Certification-Based Differentially Private Learning

model-releasesarxiv-cs-lg

arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private

CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes

arXiv:2609.39523v1 Announce Type: new Abstract: Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experime

applicationsarxiv-cs-lg

Co-Evolving Agents: Learning from Failures as Hard Negatives

arXiv:2511.22254v5 Announce Type: replace Abstract: Self-evolving agents improve their performance on long-horizon tasks by learning from their own interactions with an environment. A common approach

CoEvoWhen: Policy-Tool Coevolution for Ultra-Long Video Temporal Grounding

arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget, y

safetyarxiv-cs-cv

Concept Subspaces Compute Beyond the Logit Lens: A Weights-Only Test for Locating Representations Upstream of Readout

arXiv:2609.39263v1 Announce Type: new Abstract: A concept subspace's effect on model behavior does not establish how it relates to the output readout. We introduce a two-sided geometric diagnostic tha

researcharxiv-cs-cl

Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection

arXiv:2609.38747v1 Announce Type: new Abstract: Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliar

Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data

arXiv:2601.09717v2 Announce Type: replace-cross Abstract: Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but als

ContextAdapt: Evaluating Contextual Adaptation and Value Alignment in LLMs

arXiv:2609.38260v1 Announce Type: cross Abstract: Values such as honesty, autonomy, and confidentiality are often regarded as general principles underpinning AI alignment. However, what it means to ac

Tutorials

CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems

arXiv:2609.39784v1 Announce Type: new Abstract: Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system r

tutorialsarxiv-cs-lg

CraftSPH: A high-accuracy and composable differentiable SPH solver implemented in PyTorch

model-releasesarxiv-cs-lg

arXiv:2609.38208v1 Announce Type: cross Abstract: Smoothed Particle Hydrodynamics (SPH) is well suited to a range of problems, particularly those involving large deformations in fluid dynamics. In rec

Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection

arXiv:2609.39035v1 Announce Type: cross Abstract: Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging becau

D-Scope: Decomposing and Steering Diffusion Transformers with Sparse Autoencoders

arXiv:2609.39625v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) reveal visual structure in diffusion transformers (DiTs), but interpreting a feature does not establish whether it can be u

Tutorials

Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis

arXiv:2505.05855v4 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pai

tutorialsarxiv-cs-cv

Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

arXiv:2603.10210v2 Announce Type: replace-cross Abstract: While Diffusion Models excel in text-to-image synthesis, they frequently suffer from catastrophic concept omission when generating complex mul

Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions

arXiv:2609.38555v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within po

Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars

arXiv:2609.38471v1 Announce Type: cross Abstract: Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level si

Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model

arXiv:2609.39756v1 Announce Type: new Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV

Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving

model-releasesarxiv-cs-lg

arXiv:2609.38472v1 Announce Type: cross Abstract: Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstration

Diversity Combining for Multi-Path LLM Reasoning

arXiv:2609.38829v1 Announce Type: new Abstract: Multi-path reasoning methods such as self-consistency (SC) sample K reasoning paths and choose the most frequent answer. However, their gains quickly pl

Does a Shared Temperature Imply a Shared Angular Scale in Probabilistic Contrastive Learning?

arXiv:2609.38784v1 Announce Type: new Abstract: In probabilistic contrastive learning, a shared temperature is commonly interpreted as a shared similarity scale, but this interpretation does not hold

researcharxiv-cs-lg

Hardware

DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling

arXiv:2605.21603v1 Announce Type: cross Abstract: Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different re

hardwarearxiv-cs-lg