Search: “benchmark”

Physics-Informed Spectral Modeling for Hyperspectral Imaging

arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ

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PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

DGX agent

arXiv:2603.04165v3 Announce Type: replace-cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retr

researcharxiv-cs-ai

10 Apr 2026

Planning with Minimal Disruption

arXiv:2508.15358v2 Announce Type: replace Abstract: In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to thi

Plug-and-Play Logit Fusion for Heterogeneous Pathology Foundation Models

arXiv:2604.07779v1 Announce Type: new Abstract: Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagn

safetyarxiv-cs-cv

PLUME: Latent Reasoning Based Universal Multimodal Embedding

arXiv:2604.02073v2 Announce Type: replace Abstract: Universal multimodal embedding (UME) maps heterogeneous inputs into a shared retrieval space with a single model. Recent approaches improve UME by g

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards

arXiv:2512.01236v2 Announce Type: replace Abstract: Personalized generation models for a single subject have demonstrated remarkable effectiveness, highlighting their significant potential. However, w

PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning

model-releasesarxiv-cs-ai

arXiv:2510.24058v2 Announce Type: replace-cross Abstract: Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor

Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing

model-releasesarxiv-cs-ai

arXiv:2601.15356v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Qua

QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks

model-releasesarxiv-cs-lg

arXiv:2604.07013v1 Announce Type: cross Abstract: Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expre

Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning

model-releasesarxiv-cs-ai

arXiv:2508.07299v2 Announce Type: replace-cross Abstract: The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby

Hardware

RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing

arXiv:2512.06774v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a leading representation for high-fidelity 3D assets, yet protecting these assets via digital watermarking r

hardwarearxiv-cs-cv

Reason in Chains, Learn in Trees: Self-Rectification and Grafting for Multi-turn Agent Policy Optimization

arXiv:2604.07165v1 Announce Type: new Abstract: Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group

safetyarxiv-cs-ai

Agents

Reasoning Graphs: Deterministic Agent Accuracy through Evidence-Centric Chain-of-Thought Feedback

arXiv:2604.07595v1 Announce Type: cross Abstract: Language model agents reason from scratch on every query: each time an agent retrieves evidence and deliberates, the chain of thought is discarded and

agentsarxiv-cs-cl

Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space

arXiv:2512.12623v3 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced cross-modal understanding and reasoning by incorpo

safetyarxiv-cs-cl

Rectifying LLM Thought from Lens of Optimization

arXiv:2512.01925v2 Announce Type: replace-cross Abstract: Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain

ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework

arXiv:2604.07506v1 Announce Type: cross Abstract: Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment qual

safetyarxiv-cs-cl

Local Ai

Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification

arXiv:2604.07298v1 Announce Type: cross Abstract: Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, c

local-aiarxiv-cs-ai

Region-R1: Reinforcing Query-Side Region Cropping for Multi-Modal Re-Ranking

arXiv:2604.05268v2 Announce Type: replace-cross Abstract: Multi-modal retrieval-augmented generation (MM-RAG) relies heavily on re-rankers to surface the most relevant evidence for image-question quer

safetyarxiv-cs-ai

Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition

arXiv:2604.07884v1 Announce Type: new Abstract: High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory an

RewardFlow: Generate Images by Optimizing What You Reward

arXiv:2604.08536v1 Announce Type: new Abstract: We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward La

safetyarxiv-cs-cv

RoboAgent: Chaining Basic Capabilities for Embodied Task Planning

arXiv:2604.07774v1 Announce Type: cross Abstract: This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accompli

safetyarxiv-cs-cv

$S^3$: Stratified Scaling Search for Test-Time in Diffusion Language Models

arXiv:2604.06260v1 Announce Type: cross Abstract: Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without a

SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

arXiv:2604.07922v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive 'overthinking', generating unnecessarily long

researcharxiv-cs-cl

Local Ai

Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction

arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro

local-aiarxiv-cs-cv

Tutorials

Search-R3: Unifying Reasoning and Embedding in Large Language Models

arXiv:2510.07048v2 Announce Type: replace Abstract: Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We p

tutorialsarxiv-cs-cl

Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

arXiv:2604.08541v1 Announce Type: cross Abstract: Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon t

safetyarxiv-cs-cl

SeLaR: Selective Latent Reasoning in Large Language Models

arXiv:2604.08299v1 Announce Type: new Abstract: Chain-of-Thought (CoT) has become a cornerstone of reasoning in large language models, yet its effectiveness is constrained by the limited expressivenes

researcharxiv-cs-cl

Self-Improving 4D Perception via Self-Distillation

arXiv:2604.08532v1 Announce Type: new Abstract: Large-scale multi-view reconstruction models have made remarkable progress, but most existing approaches still rely on fully supervised training with gr

researcharxiv-cs-cv

Applications

SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio

arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, whil

applicationsarxiv-cs-ai

Sensitivity-Positional Co-Localization in GQA Transformers

arXiv:2604.07766v1 Announce Type: new Abstract: We investigate a fundamental structural question in Grouped Query Attention (GQA) transformers: do the layers most sensitive to task correctness coincid

SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning

arXiv:2604.06636v1 Announce Type: cross Abstract: Process supervision has emerged as a promising approach for enhancing LLM reasoning, yet existing methods fail to distinguish meaningful progress from

SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training

arXiv:2601.23155v2 Announce Type: replace-cross Abstract: Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monot

Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

arXiv:2604.07725v1 Announce Type: cross Abstract: We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates col

researcharxiv-cs-cl

Stabilizing Unsupervised Self-Evolution of MLLMs via Continuous Softened Retracing reSampling

arXiv:2604.03647v2 Announce Type: replace-cross Abstract: In the unsupervised self-evolution of Multimodal Large Language Models, the quality of feedback signals during post-training is pivotal for st

Stacked from One: Multi-Scale Self-Injection for Context Window Extension

arXiv:2603.04759v2 Announce Type: replace Abstract: The limited context window of contemporary large language models (LLMs) remains a primary bottleneck for their broader application across diverse do

Applications

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Diagnostic Reasoning

arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM

applicationsarxiv-cs-cl

Agents

SubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval

arXiv:2604.07415v1 Announce Type: cross Abstract: Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often

agentsarxiv-cs-cl

SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction

arXiv:2604.08370v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has demonstrated impressive performance in 3D scene reconstruction. Beyond novel view synthesis, it shows great potential f

researcharxiv-cs-cv

Tabular GANs for uneven distribution

arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation

arXiv:2604.07916v1 Announce Type: new Abstract: Referring Expression Segmentation (RES) aims to segment image regions described by natural-language expressions, serving as a bridge between vision and

researcharxiv-cs-cv

Tensor-Augmented Convolutional Neural Networks: Enhancing Expressivity with Generic Tensor Kernels

arXiv:2604.08072v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) excel at extracting local features hierarchically, but their performance in capturing complex correlations hinges h

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben

Towards Accurate and Calibrated Classification: Regularizing Cross-Entropy From A Generative Perspective

model-releasesarxiv-cs-lg

arXiv:2604.06689v1 Announce Type: new Abstract: Accurate classification requires not only high predictive accuracy but also well-calibrated confidence estimates. Yet, modern deep neural networks (DNNs

Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval

arXiv:2512.08410v2 Announce Type: replace Abstract: Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose

Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation

arXiv:2604.06831v1 Announce Type: cross Abstract: Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substan

safetyarxiv-cs-ai

Training Data Size Sensitivity in Unsupervised Rhyme Recognition

arXiv:2604.08156v1 Announce Type: new Abstract: Rhyme is deceptively intuitive: what is or is not a rhyme is constructed historically, scholars struggle with rhyme classification, and people disagree

Applications

TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding

arXiv:2511.19693v3 Announce Type: replace-cross Abstract: Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling

applicationsarxiv-cs-ai

TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation

arXiv:2604.07894v1 Announce Type: new Abstract: Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preference