arXiv AI
Jun 19
Which Pairs to Compare for LLM Post-Training?
arXiv:2606. 19607v1 Announce Type: new Abstract: Preference-based post-training has become a central paradigm for aligning language models.
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv AI
Jun 19
A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models
arXiv:2606. 19868v1 Announce Type: new Abstract: Although large language models (LLMs) have shown strong capabilities across a wide range of tasks, their outputs often remain unreliable and may contain hallucinations, making uncertainty estimation (UE) essential for building trustworthy LLMs.
arXiv AI
Jun 19
UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
arXiv:2501. 17015v2 Announce Type: replace Abstract: Simulation plays a crucial role in assessing autonomous driving systems, where the generation of realistic multi-agent behaviors is a key aspect.
arXiv AI
Jun 19
CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions
arXiv:2604. 05435v2 Announce Type: replace Abstract: Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions.
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.
arXiv AI
Jun 19
Diffusion Language Models: An Experimental Analysis
arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.
arXiv Machine Learning
Jun 19
Granularity-Regulated Adaptive Computational Efficiency for Optimal Verification in Test-Time Scaling
arXiv:2606. 19354v1 Announce Type: cross Abstract: Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning performance of large language models (LLMs) by investing additional compute at inference time.
arXiv AI
Jun 19
CRAX: Fast Safe Reinforcement Learning Benchmarking
arXiv:2606. 20376v1 Announce Type: cross Abstract: Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving.
arXiv AI
Jun 19
DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis
arXiv:2604. 13416v2 Announce Type: replace-cross Abstract: Advances in radiance fields have enabled photorealistic novel view synthesis.
arXiv AI
Jun 19
Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring
arXiv:2606. 20138v1 Announce Type: new Abstract: LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines.
arXiv AI
Jun 19
Neural Additive and Basis Models with Feature Selection and Interactions
arXiv:2606. 19850v1 Announce Type: cross Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability.
arXiv AI
Jun 19
Hybrid ANN-SNN Pipeline with Local Plasticity
arXiv:2606. 20151v1 Announce Type: cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs).
arXiv AI
Jun 19
Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU
arXiv:2606. 20074v1 Announce Type: cross Abstract: Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs).
arXiv AI
Jun 19
When, Where, and How: Adaptive Binning for Tabular Self-Supervised Learning
arXiv:2606. 19827v1 Announce Type: cross Abstract: Medical tabular data are ubiquitous in clinical research, but deep learning for tables remains underexplored because reliable labels often require costly expert adjudication, even though structured clinical variables are routinely available in tabular form.
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?
arXiv AI
Jun 19
ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?
arXiv:2606. 19787v1 Announce Type: new Abstract: Large language models are increasingly deployed as autonomous agents for multi-step tasks in executable environments, yet their ability to perform realistic operations research (OR) work remains unclear.
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.
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.
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.
arXiv AI
Jun 19
IHUBERT: Vector-Based Semantic Deduplication and Domain-Balanced Pretraining for Persian Resources
arXiv:2606. 20089v1 Announce Type: cross Abstract: Persian pretrained language models (PLMs) are still limited by the scarcity of large-scale, high-quality pretraining corpora and by insufficient evaluation beyond standard classification and NER tasks.