VeriMoA: A Mixture-of-Agents Framework for Spec-to-HDL Generation
arXiv:2510.27617v2 Announce Type: replace Abstract: Automation of Register Transfer Level (RTL) design can help developers meet increasing computational demands. Large…
GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology
arXiv:2604.15495v1 Announce Type: new Abstract: Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial…
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
arXiv:2604.16241v1 Announce Type: cross Abstract: Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it…
Targeted Exploration via Unified Entropy Control for Reinforcement Learning
arXiv:2604.14646v2 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models…
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
arXiv:2604.13882v1 Announce Type: cross Abstract: The evaluation of supervised machine learning models is a critical stage in the development of…
CodeTracer: Towards Traceable Agent States
arXiv:2604.11641v3 Announce Type: replace-cross Abstract: Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate…
FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction
arXiv:2508.05153v2 Announce Type: replace-cross Abstract: Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due…
Exploration and Exploitation Errors Are Measurable for Language Model Agents
arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding…
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
arXiv:2604.13924v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity,…