Global Environmental Institute

Current research efforts are focused on enhancing the thinking and reasoning capability of large language model (LLM) by prompting, data-driven emergence and inference-time computation. In this study, we consider stimulating language model’s thinking and cognitive abilities from a modular perspective, which mimics the human brain architecture. We select a specific intermediate attention layer with newly implemented language heads. We conduct dual-layer fine-tuning by annotated (query, thought, answer) samples and show that the intermediate layer can also learn to decode fluent and reasonable language tokens. A two-pass inference mechanism is designed to generate thoughts then formal responses. The entire framework is called modularized thinking language model (MeTHanol) which can enhance LLM’s cognitive behaviors as indicated by Theory of Mind (ToM) and Vignette-based experiments. Case studies also show that MeTHanol can plan and self-reflect and generate human-like thoughts and answers, even on unseen and open-domain tasks. MeTHanol can also adapt to a personalized prompt and behave as the specified character. Our study holds promise for significant cognitive gains from a modular perspective. Our code, model and data are available at https://bachozean.github.io/methanol-page/.

The integration of satellite communication into 5G has been formalized in 3GPP Release 17 through the specification of Non-Terrestrial Networks (NTN), marking a significant step toward achieving global connectivity. However, the early-stage maturity of 5G NTN standards and the lack of commercial NTN-capable equipment hinder extensive performance validation and system prototyping. To address this gap, this paper proposes a software-defined radio (SDR) test platform with General-Purpose Processor (GPP) processing, leveraging Amarisoft's 5G NTN protocol stack software while performing custom system integration and adaptation for real satellite operation. The platform supports bidirectional communication between an SDR-based NTN gNB and UE emulator through a Geostationary Earth Orbit (GEO) satellite link, with full compliance to 3GPP NTN specifications. We provide detailed insights into the system architecture, SDR hardware-software co-design, and satellite gateway adaptations. Through field trials, we evaluate the performance metrics including downlink throughput and round-trip time. Results validate the feasibility and effectiveness of SDR-based platforms for NTN testing, and highlight their potential in bridging current implementation gaps before widespread commercial deployment.