arXiv subjects

Yurui Li

Publications and source records attributed to Yurui Li.

4 recordsLinked to original sources

arXiv2026

Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding

Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central question: can sentence-level language be reliably recovered from such signals? Under realistic information constraints, this direct-recovery assumption may be too strong. We introduce a semantic compression hypothesis: non-invasive EEG may preserve recoverable semantic anchors rather than the full lexical--syntactic form of a sentence. From this perspective, direct sentence reconstruction is overly fine-grained relative to the recoverable information scale of EEG. To address this mismatch, we propose Brain-CLIPLM, a two-stage framework that decomposes EEG-to-text decoding into semantic-anchor recovery and anchor-guided sentence reconstruction. Stage 1 uses contrastive learning to align word-level EEG evidence with a fixed keyword vocabulary and recover ordered semantic anchors. Stage 2 uses a retrieval-grounded large language model with chain-of-thought reasoning prompts to reconstruct sentence meaning from these anchors, following a granularity matching principle that aligns decoding complexity with the recoverable neural information scale. On the combined Zurich Cognitive Language Processing (ZuCo) benchmark, Brain-CLIPLM achieves 67.6\% Top-5 and 85.0\% Top-25 sentence retrieval accuracy, with the strongest performance at intermediate anchor granularity. Control analyses show that EEG-derived anchors carry sentence-specific information beyond language-model priors. Within the constrained ZuCo sentence pool and fixed keyword-vocabulary settings, these findings suggest that EEG-to-text decoding is better framed as recovering compressed semantic content before anchor-guided sentence reconstruction.

cs.CL↗

arXiv2025

AgriGPT: a Large Language Model Ecosystem for Agriculture

Despite the rapid progress of Large Language Models (LLMs), their application in agriculture remains limited due to the lack of domain-specific models, curated datasets, and robust evaluation frameworks. To address these challenges, we propose AgriGPT, a domain-specialized LLM ecosystem for agricultural usage. At its core, we design a multi-agent scalable data engine that systematically compiles credible data sources into Agri-342K, a high-quality, standardized question-answer (QA) dataset. Trained on this dataset, AgriGPT supports a broad range of agricultural stakeholders, from practitioners to policy-makers. To enhance factual grounding, we employ Tri-RAG, a three-channel Retrieval-Augmented Generation framework combining dense retrieval, sparse retrieval, and multi-hop knowledge graph reasoning, thereby improving the LLM's reasoning reliability. For comprehensive evaluation, we introduce AgriBench-13K, a benchmark suite comprising 13 tasks with varying types and complexities. Experiments demonstrate that AgriGPT significantly outperforms general-purpose LLMs on both domain adaptation and reasoning. Beyond the model itself, AgriGPT represents a modular and extensible LLM ecosystem for agriculture, comprising structured data construction, retrieval-enhanced generation, and domain-specific evaluation. This work provides a generalizable framework for developing scientific and industry-specialized LLMs. All models, datasets, and code will be released to empower agricultural communities, especially in underserved regions, and to promote open, impactful research.

cs.AI↗

arXiv2025

CTC: The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning

The critical role of division of labor (DOL) in enhancing cooperation is well-recognized in real-world applications. Consequently, many cooperative multi-agent reinforcement learning (MARL) methods have incorporated DOL mechanisms to improve cooperation among agents. However, the lack of benchmark tasks specifically designed to evaluate and promote DOL and cooperation has limited the effective development and deployment of such mechanisms in cooperative MARL. This gap between current cooperative MARL methods and practical applications underscores the need for evaluation tasks that explicitly require DOL and cooperation. To address this gap, we propose the Composite Tasks Challenge (CTC), a suite of tasks explicitly designed to require both DOL and cooperation for successful task completion. The CTC tasks are constructed based on two core design principles: 1) DOL is a necessary condition for task success; 2) Failure in any atomic subtask results in failure of the overall task. The first principle emphasizes the necessity of DOL, while the second enforces the importance of cooperation, making both components essential for success in CTC tasks. We evaluate nine representative cooperative MARL methods on the proposed CTC tasks. Experimental results show that all methods consistently achieve zero test winning rates across all CTC tasks, highlighting the challenge of CTC tasks and the limitations of current methods. To facilitate future research, we also introduce a guiding solution that achieves non-zero test winning rates on all tasks, thereby demonstrating the solvability of the CTC tasks. However, the performance of this guiding solution remains suboptimal, further underscoring the value of CTC tasks as a challenging and meaningful testbed for advancing cooperative MARL research.

cs.LG↗

arXiv2024

Four-Channel WDM Graphene Optical Receiver

Silicon photonics with the advantages of low power consumption, low cost, and high yield is a crucial technology for facilitating high-capacity optical communications and interconnects. The graphene photodetectors (GPDs) featuring broadband operation, high speed, and low integration cost can be good additions to the conventional SiGe photodetectors, supporting silicon-integrated on-chip photodetection in new wavelength bands beyond 1.6 microns (e.g., U-band and 2 microns). Here we realize a silicon-integrated four-channel wavelength division multiplexing (WDM) optical receiver based on a micro-ring resonator (MRR) array and four p-n homojunction GPDs. These GPDs based on the photo-thermoelectric (PTE) effect operating under zero (current) bias exhibit responsivities of about 1.1 V/W and flat frequency responses up to 67 GHz which is set-up limited. The GPDs show good consistence benefiting from the compact active region array (0.006 mm^2) covered by a single mechanically exfoliated hBN/graphene/hBN stack. Moreover, the WDM graphene optical receiver realized the 4 x 16 Gbps non-return to zero (NRZ) optical signal transmission. To the best of our knowledge, it is the first GPD-array-based optical receiver using high-quality mechanically exfoliated graphene and edge graphene-metal conduct with low resistance. Apparently, our design is also compatible with CVD-grown graphene, which can also result in a good consistence of the GPDs. This work shed light on the large-scale integration of GPDs with high consistency and uniformity, enabling the application of high-quality mechanically exfoliated graphene, and promoting the development of the graphene photonic integrated circuits.

physics.optics↗