Benchmarks and evaluation

Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.

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arXiv AI

Sep 10

Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility

arXiv:2603.05581v2 Announce Type: replace-cross Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobili...

By Olaf Yunus Laitinen Imanov

multimodal benchmarks

arXiv AI

Sep 10

Aes3D: Aesthetic Assessment in 3D Gaussian Splatting

arXiv:2605.05155v4 Announce Type: replace-cross Abstract: As 3D Gaussian Splatting (3DGS) gains attention in immersive media and digital content creation, assessing the aesthetics of 3D scenes become...

By Chuanzhi Xu, Boyu Wei, Haoxian Zhou, Xuanhua Yin, Zihan Deng, Haodong Chen, Qiang Qu, Weidong Cai

benchmarks

arXiv AI

Sep 10

LitSeg: Narrative-Aware Document Segmentation for Literary RAG

arXiv:2605.27156v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail do...

By Ruikang Zhang, Zhanni Chen, Yiqiao Cai, Qi Su

llms rag computer-vision fine-tuning efficiency benchmarks

arXiv AI

Sep 10

Improving Federated Graph Recommendation with Semantic Guidance

arXiv:2606.15277v2 Announce Type: replace-cross Abstract: Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning...

By Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen, Van Ho-Long, Thanh Trung Huynh, Zhao Ren

llms rag benchmarks safety

arXiv Machine Learning

Sep 10

Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity

arXiv:2609.05650v1 Announce Type: new Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-...

By Armando Vieira

agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 10

One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning

arXiv:2609.05885v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR...

By Huiyi Wang, Daijiao Liu, Lina Yao, Dong Gong

llms fine-tuning benchmarks

arXiv Machine Learning

Sep 10

A dictionary learning framework for graphs via filters and optimal transport

arXiv:2609.05919v1 Announce Type: new Abstract: We propose a graph dictionary learning (GDL) framework where each graph is represented as a zero-mean Gaussian distribution derived from its filtered L...

By Jinchuan Liao, Dai Hai Nguyen

rag benchmarks

arXiv Machine Learning

Sep 10

Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning

arXiv:2609.08034v1 Announce Type: new Abstract: Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but indepe...

By Mrigank Dhingra, Jordan Stout, Omer San

fine-tuning benchmarks

arXiv Machine Learning

Sep 10

FMMO: Detecting the Divergence Between Local Attribution and Global Drift

arXiv:2609.06173v1 Announce Type: new Abstract: Post-deployment drift poses a critical risk to algorithmic accountability, particularly when ground truth labels are delayed and performance degradatio...

By Muhammad Rehman Zafar, Ali El-Sharif, Naimul Khan

benchmarks safety

arXiv Machine Learning

Sep 10

MetaRSI / RSI2: A Meta-Recursive Self-Improving System for Recursive Self-Improving Systems Themselves

arXiv:2609.06396v2 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet...

By Zihan Tan, Leixin Sun, Zitong Shi, Yitao Liu, Jiajun Wu, Nathaniel Brooks, Jiaru Qian, Xiaoran Shang, Suyuan Huang, Yi Ding, Yangxu Liao, Mukai Li, Qiushi Sun, Shudong Liu, Xuankun Rong, Xiaohang Yu, Zhuo Chen, Hejia Geng, Chenxin Li, Aozhou Wang, Zengji Tu, Robert Tang, Yuxin Zhan, Eric Jiang, Yuxin Wu, Jianqing Zhang, Xiao Liang, Fang Wu, Haochi Zhang, Alexander Marlow, Guancheng Wan

benchmarks

arXiv Machine Learning

Sep 10

Hidden in Plain Sight: The Overlooked Significance of Canonical Elements for Extreme LLM Sparsity

arXiv:2609.06557v1 Announce Type: new Abstract: Large language models (LLMs) are often considered fragile under aggressive sparsification, and maintaining reliable performance typically requires stic...

By Hyeondo Jang, Kwanhee Lee, Dongyeop Lee, Namhoon Lee

llms efficiency benchmarks

arXiv Machine Learning

Sep 10

MOLE: Detecting Insider Threats in AI Agents

arXiv:2609.06966v1 Announce Type: new Abstract: Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison train...

By Aashiq Muhamed, Virginia Smith

agents benchmarks

arXiv Machine Learning

Sep 10

HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care

arXiv:2609.06976v1 Announce Type: new Abstract: As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients...

By Yuchen Niu, Yanan Ma, Srinivasan Nandakumar, Maolin Chen, Viktor Schlegel, Kexin Wei, Ling Cheng, Anna Bird, Anil Anthony Bharath, Siew-Kei Lam

llms agents nlp benchmarks safety

arXiv Machine Learning

Sep 10

NeuCME: Toward Dynamic Multimodal Continual Learning via Neural Combinatorics of Multiple Experts

arXiv:2609.07009v1 Announce Type: new Abstract: Multimodal continual learning has recently shown great potential for developing agents with human-like intelligence by continuously learning new tasks...

By Kai Guo, Chuanbin Liu, Peng Hu, Hao Wang, Xi Peng

agents efficiency multimodal benchmarks

arXiv Machine Learning

Sep 10

PhysSAE: Mechanistic Interpretability with Sparse Autoencoders

arXiv:2609.07061v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unkn...

By Nandita N. Patil, Eshwar R. A., Gajanan V. Honnavar

benchmarks safety

arXiv Machine Learning

Sep 10

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

arXiv:2609.07814v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of s...

By Hanwen Wang, Paris Perdikaris

benchmarks

arXiv Machine Learning

Sep 10

$\alpha$-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling

arXiv:2609.07961v1 Announce Type: new Abstract: Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. Howev...

By Thanh-Dat Truong, Sarah Alharbi, Susan Gauch, Xinghui Zhao, Marios Savvides, Khoa Luu

benchmarks

arXiv Machine Learning

Sep 10

HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting

arXiv:2609.08286v1 Announce Type: new Abstract: Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained l...

By Namwoo Kim, Hyungryul Baik, Yoonjin Yoon

rag benchmarks

arXiv Machine Learning

Sep 10

EMBLEM: Enhancing Multi-script Table Detection through Masking

arXiv:2609.08330v1 Announce Type: new Abstract: Table detection is a core task in document analysis, supporting downstream applications such as information retrieval, document reconstruction, and vis...

By Dhruv Kudale, Udhay Brahmi, Ganesh Ramakrishnan

computer-vision nlp fine-tuning benchmarks

arXiv Machine Learning

Sep 10

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, pol...

By Danielle L. Ferreira, Lifeng Lin, Adam Aslam, Nicholas Chang, Rebekah G. Baig, Edgar Baculi, Zoey Cao, Melanie Senn

llms benchmarks

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