Search: “benchmark”

SCOPE: Sequential Causal Optimization of Process Interventions

arXiv:2512.17629v4 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KP

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Segment and Select: Vision-Language Segmentation in 3D Scenarios

DGX agent

arXiv:2606.10594v1 Announce Type: new Abstract: 3D vision-language segmentation aims to segment target objects in 3D scenarios according to the linguistic instructions and visual observations. Prior a

researcharxiv-cs-cv

10 Jun 2026

Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts

arXiv:2606.10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are ex

SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs

arXiv:2606.09886v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the

Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling

arXiv:2606.10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints. While Mixed-Intege

Small Data, Big Noise: Adversarial Training for Robust Parameter-Efficient Fine-Tuning

model-releasesarxiv-cs-cl

arXiv:2606.10610v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks. However, current PEFT methods often

SocraticPO: Policy Optimization via Interactive Guidance

arXiv:2606.09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewar

SPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting

arXiv:2606.09917v1 Announce Type: new Abstract: Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-spac

SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech

arXiv:2606.06037v2 Announce Type: cross Abstract: Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on m

safetyarxiv-cs-cl

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606.11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop i

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs

arXiv:2602.02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PD

Swivuriso: The South African Next Voices Multilingual Speech Dataset

arXiv:2512.02201v3 Announce Type: replace Abstract: This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the develo

applicationsarxiv-cs-cl

SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning

arXiv:2606.09853v1 Announce Type: new Abstract: A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities,

applicationsarxiv-cs-lg

Agents

TabClaw: An Interactive and Self-Evolving Agent for Spreadsheet Manipulation and Table Reasoning

arXiv:2606.10316v1 Announce Type: new Abstract: Spreadsheets and tables are widely used representations for structured data analysis, but effective analysis still requires substantial manual effort an

agentsarxiv-cs-cl

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

arXiv:2606.11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simu

The hyper-scaled NLP bound for maximum-entropy remote sampling

arXiv:2601.20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of s random variables from a set of n random variables, so as to max

Agents

The Model Lab vs Agent Lab distinction is one of the clearest frameworks I've seen for understanding where AI value actually lives right now…

The Model Lab vs Agent Lab distinction is one of the clearest frameworks I've seen for understanding where AI value actually lives right now. TL;DR from @latentspacepod: • Model Labs compete on capabi

agentsswyx--x

The Whale That Outswam Evolution: Swarm Intelligence Maximises Memory in Connectome Reservoirs

arXiv:2606.09902v1 Announce Type: cross Abstract: Reservoir computing exploits the fixed dynamics of a recurrent network for temporal processing, requiring only a trained linear readout. Biological ne

Agents

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

arXiv:2606.10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, a

agentsarxiv-cs-ai

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

arXiv:2606.11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. H

Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents

arXiv:2606.10457v1 Announce Type: new Abstract: Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved thr

applicationsarxiv-cs-ai

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509.25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination a

researcharxiv-cs-ai

UniSVQ: 2-bit Unified Scalar-Vector Quantization

arXiv:2606.10520v1 Announce Type: new Abstract: Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration for large language models (LLMs). Scalar quantizati

researcharxiv-cs-cl

Tutorials

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

arXiv:2606.10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence ope

tutorialsarxiv-cs-ai

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

arXiv:2606.10466v1 Announce Type: cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fail

applicationsarxiv-cs-ai

Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection

arXiv:2606.10393v1 Announce Type: new Abstract: Credit-card fraud detection is difficult because fraudulent transactions are rare, costly, and unevenly distributed. Strong gradient-boosted tree models

VeriSpace: Spatially Grounded Action Verification for Vision-Language-Action Models

arXiv:2606.10568v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong promise for robotic manipulation, but their reliability at test time remains limited by one-shot a

applicationsarxiv-cs-ro

What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents

arXiv:2606.10267v1 Announce Type: cross Abstract: Hierarchical vision-language-action (Hi-VLA) systems have emerged as a promising paradigm for complex robot manipulation, by using high-level VLM plan

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects

arXiv:2501.14717v2 Announce Type: replace Abstract: Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly t

researcharxiv-cs-cl

Local Ai

When Attribution Patching Lies: Diagnosis and a Second-Order Correction

arXiv:2606.09899v1 Announce Type: cross Abstract: A central goal of mechanistic interpretability is to identify which internal components causally drive a language model's behavior. Because these impo

local-aiarxiv-cs-ai

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

arXiv:2606.10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limit

When to Align, When to Predict: A Phase Diagram for Multimodal Learning

arXiv:2606.11190v1 Announce Type: new Abstract: Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic

safetyarxiv-cs-lg

Whisfusion: Parallel ASR Decoding with Masked Diffusion

arXiv:2508.07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency sca

researcharxiv-cs-ai

A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

arXiv:2606.08669v1 Announce Type: cross Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets. To in

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology

arXiv:2606.08093v1 Announce Type: new Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligenc

A Regret Minimization Framework on Preference Learning in Large Language Models

arXiv:2606.09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provi

researcharxiv-cs-ai

Tutorials

A Systematic Study of Behavioral Cloning for Scientific Data Annotation

arXiv:2606.07568v1 Announce Type: cross Abstract: Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the 'last mile' problem:

tutorialsarxiv-cs-ai

A Unifying Framework for Concept-Based Representational Similarity

arXiv:2606.09653v1 Announce Type: new Abstract: Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions

ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding

arXiv:2606.07524v1 Announce Type: cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison i

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

arXiv:2606.08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions. Exi

AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

arXiv:2606.09556v1 Announce Type: new Abstract: AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different

Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers

arXiv:2606.09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks. It has applications across social, biological, and financial domains

AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning

arXiv:2606.09447v1 Announce Type: new Abstract: We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles. Major cloud platforms encomp

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

arXiv:2510.18428v4 Announce Type: replace Abstract: Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

arXiv:2606.07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quanti

An Agency-Transferring Model-Free Policy Enhancement Technique

arXiv:2606.09825v1 Announce Type: cross Abstract: Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substan

AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation

arXiv:2603.25726v3 Announce Type: replace Abstract: We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with fou

applicationsarxiv-cs-cv

APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing

arXiv:2606.08761v1 Announce Type: cross Abstract: W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing systems to mixed