arXiv:2604.07798v3 Announce Type: replace
Abstract: Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable information in long-horizon interactions. However, retrieva…
arXiv:2604.20595v1 Announce Type: cross
Abstract: We establish a mathematical correspondence between state space models, a state-of-the-art architecture for capturing long-range dependencies in data, and an exactly solvable nonlinear oscillator networ…
arXiv:2604.19859v1 Announce Type: new
Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we study how to train a strong…
arXiv:2604.20659v1 Announce Type: new
Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Language Models (LLMs) by leveraging direct outcome verification instead of learned reward models. B…
arXiv:2604.19877v1 Announce Type: new
Abstract: We release Super Apriel, a 15B-parameter supernet in which every decoder layer provides four trained mixer choices — Full Attention (FA), Sliding Window Attention (SWA), Kimi Delta Attention (KDA), and …
arXiv:2604.16813v2 Announce Type: replace
Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insufficiently characterized. To address this gap, we int…
arXiv:2604.19903v1 Announce Type: new
Abstract: Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx/year. The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low…
arXiv:2604.20688v1 Announce Type: new
Abstract: Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent trends of rapid intensification and increasing nearshore …
arXiv:2604.20468v1 Announce Type: cross
Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different inte…
arXiv:2411.00585v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) have become foundational in modern language-driven software applications, profoundly influencing daily life. A critical technique in leveraging their potential is r…