Search: “benchmark”

Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges

arXiv:2605.09702v1 Announce Type: cross Abstract: Multi-judge evaluation is increasingly used to assess LLMs and reward models, and the prevailing heuristic is to curate: keep the most accurate judges

researcharxiv-cs-cl

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Can LLMs Predict Polymer Physics Just by Reading Synthesis and Processing Prose?

DGX agent

arXiv:2605.08255v1 Announce Type: cross Abstract: Can large language models predict physical and mechanical polymer properties simply by reading unstructured scientific prose? Polymer performance is r

model-releasesarxiv-cs-ai

12 May 2026

Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key

arXiv:2605.06638v2 Announce Type: replace Abstract: Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with t

Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology

arXiv:2512.06949v3 Announce Type: replace Abstract: Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics, but modeling spatial context and inter-

CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG

arXiv:2605.08333v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries

CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing

arXiv:2605.09350v1 Announce Type: new Abstract: Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close th

Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs

arXiv:2605.09975v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are a promising approach for solving partial differential equations (PDEs). Their training, however, is often d

researcharxiv-cs-lg

CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal

arXiv:2603.21901v2 Announce Type: replace Abstract: Video subtitle removal aims to distinguish text overlays from background content while preserving temporal coherence. Existing diffusion-based metho

researcharxiv-cs-cv

CLEF: EEG Foundation Model for Learning Clinical Semantics

arXiv:2605.10817v1 Announce Type: new Abstract: Clinical EEG interpretation requires reasoning over full EEG sessions and integrating signal patterns with clinical context. Existing EEG foundation mod

CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics

arXiv:2605.09584v1 Announce Type: cross Abstract: Inpatient clinical reasoning is a sequential decision under partial observability: the clinician sees the admission so far and must choose the next ac

CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs

arXiv:2605.10510v1 Announce Type: cross Abstract: Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequenc

CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis

model-releasesarxiv-cs-cl

arXiv:2510.11233v3 Announce Type: replace Abstract: Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce a

Agents

CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents

arXiv:2605.08399v1 Announce Type: new Abstract: Tool-augmented language models can extend small language models with external executable skills, but scaling the tool library creates a coupled challeng

agentsarxiv-cs-ai

Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs

arXiv:2602.11181v2 Announce Type: replace Abstract: Code-mixing and code-switching (CSW) remain challenging phenomena for large language models (LLMs). Despite recent advances in multilingual modeling

safetyarxiv-cs-cl

CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization

arXiv:2605.08873v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has emerged as a powerful algorithm for improving the reasoning capabilities of language models, but often fai

COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing

arXiv:2511.06894v3 Announce Type: replace-cross Abstract: Reconstruction-based methods are a dominant paradigm in time series anomaly detection (TSAD), however, their near-universal reliance on Mean S

CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization

arXiv:2605.08802v1 Announce Type: new Abstract: Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perfo

Complex-Valued Phase-Coherent Transformer

arXiv:2605.10123v1 Announce Type: new Abstract: Complex-valued Transformers have largely inherited softmax attention from real-valued architectures. However, row-normalised token competition is not ne

Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation

arXiv:2605.08810v1 Announce Type: cross Abstract: We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learnin

Concordia: Self-Improving Synthetic Tables for Federated LLMs

arXiv:2605.09855v1 Announce Type: new Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-II

Confidence-Guided Diffusion Augmentation for Enhanced Bangla Compound Character Recognition

arXiv:2605.10916v1 Announce Type: cross Abstract: Recognition of handwritten Bangla compound characters remains a challenging problem due to complex character structures, large intra-class variation,

ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking

model-releasesarxiv-cs-cl

arXiv:2605.09760v1 Announce Type: new Abstract: A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a l

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce

Agents

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability

arXiv:2605.10516v1 Announce Type: new Abstract: This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under sem

agentsarxiv-cs-ai

Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation

arXiv:2605.09085v1 Announce Type: new Abstract: Density estimation is a central primitive in probabilistic modeling, yet continuous, discrete, and mixed-variable domains are often treated by separate

Continuity Laws for Sequential Models

arXiv:2605.08539v1 Announce Type: cross Abstract: Inductive biases influence the behavior and performance of sequential models. In this work, we study an underexplored inductive bias in sequential mod

safetyarxiv-cs-ai

CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings

arXiv:2605.10337v1 Announce Type: new Abstract: Intracranial electrocorticography (ECoG) offers high-signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient

Applications

Count Anything at Any Granularity

arXiv:2605.10887v1 Announce Type: new Abstract: Open-world object counting remains brittle: despite rapid advances in vision-language models (VLMs), reliably counting the objects a user intends is far

applicationsarxiv-cs-cv

CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving

arXiv:2605.10426v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms sti

Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning

arXiv:2605.08839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world sc

Crosslingual On-Policy Self-Distillation for Multilingual Reasoning

arXiv:2605.09548v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in mathematical reasoning, but this ability is not equally accessible across languages. E

safetyarxiv-cs-cl

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models

arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa

CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification

arXiv:2605.09002v1 Announce Type: cross Abstract: In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLI

CTQWformer: A CTQW-based Transformer for Graph Classification

arXiv:2605.09486v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to captur

cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transport

arXiv:2605.08793v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as a fundamental tool in modern machine learning, yet its computational cost remains a significant bottleneck for l

Local Ai

Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding

arXiv:2605.08808v1 Announce Type: cross Abstract: Accurate 3D scene description is fundamental to robotic navigation and augmented reality, yet current dense captioning methods face significant limita

local-aiarxiv-cs-ai

D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement

arXiv:2605.09400v1 Announce Type: new Abstract: Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch se

researcharxiv-cs-lg

DAPE: Dynamic Non-uniform Alignment and Progressive Detail Enhancement Techniques for Improving the Performance of Efficient Visual Language Models

arXiv:2605.08902v1 Announce Type: cross Abstract: In recent years, pre-trained visual-linguistic models have demonstrated tremendous potential, becoming a crucial foundational framework for numerous d

safetyarxiv-cs-ai

DARE: Diffusion Language Model Activation Reuse for Efficient Inference

arXiv:2605.08134v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to auto-regressive (AR) models, offering greater expressive capacity a

Applications

Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning

arXiv:2605.10166v1 Announce Type: new Abstract: Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or e

applicationsarxiv-cs-ro

Agents

DataMaster: Towards Autonomous Data Engineering for Machine Learning

arXiv:2605.10906v1 Announce Type: cross Abstract: As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasing

agentsarxiv-cs-ai

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving

arXiv:2605.10564v1 Announce Type: new Abstract: End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual rea

DegBins: Degradation-Driven Binning for Depth Super-Resolution

arXiv:2605.09628v1 Announce Type: new Abstract: Depth super-resolution (DSR) aims to recover a high-resolution (HR) depth map from its low-resolution (LR) counterpart. With color image guidance, this

researcharxiv-cs-cv

Dependency-Aware Discrete Diffusion for Scene Graph Generation

arXiv:2605.09065v1 Announce Type: new Abstract: Scene graphs (SGs) represent objects and their relationships as structured graphs, enabling applications in image generation, robotics, and 3D understan

safetyarxiv-cs-cv

Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation

arXiv:2605.09222v1 Announce Type: cross Abstract: Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hinderi

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

arXiv:2604.01151v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human o

DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization

arXiv:2605.10863v1 Announce Type: new Abstract: Although Large Language Models (LLMs) have made remarkable progress, current preference optimization methods still struggle to align directional consist

safetyarxiv-cs-cl

Direct Bethe Free Energy Minimization for Bayesian Neural Ne twork

arXiv:2605.08446v1 Announce Type: new Abstract: We propose training Bayesian neural networks by directly minimizing the Bethe free energy rather than maximizing a variational lower bound. On tree-stru