Benchmarks and evaluation

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

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arXiv AI

Jun 19

How LLMs Fail and Generalize in RTL Coding for Hardware Design?

arXiv:2606. 19347v1 Announce Type: cross Abstract: Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM).

By Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng, Zhongzhi Yu, Brucek Khailany, Yu-Chiang Frank Wang

llms benchmarks safety

arXiv Machine Learning

Jun 19

On the QUEST for Uncertainty Quantification via Highest Density Regions

arXiv:2606. 19569v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is essential for reliable decision-making in safety-critical applications in probabilistic machine learning.

By Sam Goring, Tom Kuipers, Nicola Paoletti, David S. Watson

benchmarks safety

arXiv Machine Learning

Jun 19

Do Vision-Language Models Understand 3D Scenes or Just Catalogue Objects?

arXiv:2605. 20448v2 Announce Type: replace-cross Abstract: Vision-language models reliably name objects in a scene, but do they represent the 3D layout those objects inhabit?

By Animesh Maheshwari, Divyansh Sahu, Nishit Verma

llms robotics multimodal benchmarks

arXiv Machine Learning

Jun 19

We Need to Rethink Benchmarking in Anomaly Detection

arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.

By Philipp R\"ochner, Simon Kl\"uttermann, Kevin Kammler, Franz Rothlauf, Emmanuel M\"uller, Daniel Schl\"or

benchmarks

arXiv AI

Jun 19

Too long; didn't solve

arXiv:2604. 07593v2 Announce Type: replace Abstract: Mathematical benchmarks consisting of a range of mathematics problems are widely used to evaluate the reasoning abilities of large language models, yet little is known about how their structural properties influence model behaviour.

By Luc\'ia M. Cabrera, Isaac Saxton-Knight, Jocelyn D'Arcy

llms benchmarks safety

arXiv AI

Jun 19

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think

arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.

By Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha, Khoa Vo, Philip Lund M{\o}ller, Quang T. Nguyen, Long Dinh, Tuan Dam, Vu Duong, Tung M. Luu, Trung Le, Tran Nguyen Le, Minh Vu, An Thai Le, Ngan Le, Daniel Sonntag, James Zou, Jan Peters, Duy M. H. Nguyen, Ngo Anh Vien

robotics fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 19

Spectral Retrieval-Augmented Time-Series Forecasting

arXiv:2606. 19412v1 Announce Type: new Abstract: Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are difficult to memorize during training.

By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

rag benchmarks

arXiv AI

Jun 19

ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

arXiv:2606. 20235v1 Announce Type: cross Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration.

By Tingyue Pan, Mingyue Cheng, Daoyu Wang, Yitong Zhou, Jie Ouyang, Qi Liu, Enhong Chen

llms agents benchmarks

arXiv Machine Learning

Jun 19

Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty

arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).

By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis

benchmarks

arXiv AI

Jun 19

Modularity-Free Conflict-Averse Training for Generalized PINNs

arXiv:2606. 20156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives.

By Heejo Kong, Beomchul Park, Sung-Jin Kim, Seong-Whan Lee

rag efficiency benchmarks

arXiv Machine Learning

Jun 19

Multi-Task Bayesian In-Context Learning

arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.

By Qingyang Zhu, Eric Karl Oermann, Kyunghyun Cho

llms benchmarks

arXiv AI

Jun 19

GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks

arXiv:2606. 19566v1 Announce Type: cross Abstract: Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks.

By Nanhong Liu, Mucun Sun, Jie Zhang

fine-tuning benchmarks safety

arXiv AI

Jun 19

NRITYAM: Language Models Meet Art and Heritage of Dance

arXiv:2606. 19727v1 Announce Type: cross Abstract: Language models have become essential tools in shaping modern workflows.

By Punit Kumar Singh, Niladri Ghosh, Advait Joshi{\i}nst, Shailee Choudhary, Michael F\"arber, Haiqin Yang

llms multimodal benchmarks

arXiv Machine Learning

Jun 19

EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning

arXiv:2510. 19893v2 Announce Type: replace Abstract: Medical AI systems demonstrated impressive diagnostic performance, yet they routinely show uneven accuracy across demographic groups, disadvantaging underrepresented populations.

By Shiqi Dai, Wei Dai, Jiaee Cheong, Paul Pu Liang

reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jun 19

On the Redundancy of Timestep Embeddings in Diffusion Models

arXiv:2606. 20416v1 Announce Type: new Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales.

By Jos\'e A. Ch\'avez

llms rag diffusion benchmarks

arXiv AI

Jun 19

The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse

arXiv:2606. 20255v1 Announce Type: cross Abstract: We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent.

By Celestine Achi

llms benchmarks

arXiv Machine Learning

Jun 19

MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery

arXiv:2606. 19624v1 Announce Type: new Abstract: Reliable benchmarking is critical for developing machine learning models for tandem mass spectrometry (MS/MS) based molecule discovery.

By Hongxuan Liu, Roman Bushuiev, Ivy Lightheart, Mrunali Manjrekar, Anton Bushuiev, Magdalena Lederbauer, Filip Jozefov, Yinkai Wang, Soha Hassoun, Josef Sivic, James Taylor, Runzhong Wang, David Healey, Tom\'a\v{s} Pluskal, Connor W. Coley

benchmarks

arXiv Machine Learning

Jun 19

Multimodal Concept Bottleneck Models

arXiv:2606. 19882v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts.

By Tongqing Shi, Ge Yan, Tuomas Oikarinen, Tsui-Wei Weng

rag multimodal benchmarks safety

arXiv AI

Jun 19

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search

arXiv:2509. 15927v5 Announce Type: replace-cross Abstract: Auto-bidding is a critical tool for advertisers to improve advertising performance.

By Zhiyu Mou, Yiqin Lv, Miao Xu, Qi Wang, Yixiu Mao, Jinghao Chen, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng

reinforcement-learning benchmarks

arXiv AI

Jun 19

Vero: An Open RL Recipe for General Visual Reasoning

arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?

By Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

llms reinforcement-learning efficiency multimodal benchmarks

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