Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Const

arXiv:2605.19376v1 Announce Type: new Abstract: How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising

arXiv:2609.30836v1 Announce Type: new Abstract: Deploying small language models (SLMs) on offline, resource-constrained edge devices such as remote sensing satellites presents a

arXiv:2604.00025v1 Announce Type: cross Abstract: Standard evaluation protocols reveal a counterintuitive phenomenon: on 7.7% of benchmark problems spanning five datasets, larger

arXiv:2603.03686v2 Announce Type: replace Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional

arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines

arXiv:2603.27169v1 Announce Type: new Abstract: Recent research has demonstrated the effectiveness of large language models (LLMs) in solving combinatorial optimization problems

arXiv:2605.15333v1 Announce Type: new Abstract: Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence

arXiv:2603.22942v1 Announce Type: new Abstract: Translating Natural Language to SQL (NL2SQL) remains a critical bottleneck for democratization of data in enterprises. Although