Selected work

Representative Papers

Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit

Aug 14, 2026

This study investigates whether AI agent-based social platforms can replicate the structural characteristics of human social networks. By comparing Moltbook with early Reddit through network analysis, natural language processing, and motif analysis, we systematically quantify differences in semantic coherence and interaction structures. Results indicate that AI-generated platforms exhibit significantly weaker semantic organization and reciprocal interactions compared to human networks; specifically, Reddit demonstrates high semantic diversity and reciprocity, whereas Moltbook is characterized by severe homophily and predominantly unidirectional communication. This work provides the first empirical evidence that current AI agent networks fail to reproduce core organizational features of human social systems, thereby establishing a critical benchmark for evaluating generative social simulations.

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smallNet: Implementation of a convolutional layer in tiny FPGAs

Sep 29, 2025

Deploying the first layer of CNNs on resource-constrained embedded platforms (e.g., FPGAs, SoMs, SoCs, ASICs) faces challenges including high power consumption, limited real-time performance, and dependence on Python-based or HLS toolchains. To address these, this paper proposes smallNet—a compact, hand-coded Verilog convolutional layer architecture. smallNet employs fixed-point arithmetic and a filter-like polynomial structure, requiring no Xilinx IP cores, VLSI design tools, or external Python libraries, thereby enhancing hardware portability and deployment flexibility. Evaluated on a single-core Xilinx Zynq-7000 Cora Z7 platform, smallNet achieves 81.2% classification accuracy, delivers a 5.1× speedup over CPU-based inference, and consumes only 1.5 W total system power. This work establishes a lightweight, self-contained, and synthesizable hardware implementation paradigm for low-power, real-time edge intelligence.

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Recent publications

Latest Papers

Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Reddit

Aug 14, 2026

This study investigates whether AI agent-based social platforms can replicate the structural characteristics of human social networks. By comparing Moltbook with early Reddit through network analysis, natural language processing, and motif analysis, we systematically quantify differences in semantic coherence and interaction structures. Results indicate that AI-generated platforms exhibit significantly weaker semantic organization and reciprocal interactions compared to human networks; specifically, Reddit demonstrates high semantic diversity and reciprocity, whereas Moltbook is characterized by severe homophily and predominantly unidirectional communication. This work provides the first empirical evidence that current AI agent networks fail to reproduce core organizational features of human social systems, thereby establishing a critical benchmark for evaluating generative social simulations.

0 citationsRead paper

smallNet: Implementation of a convolutional layer in tiny FPGAs

Sep 29, 2025

Deploying the first layer of CNNs on resource-constrained embedded platforms (e.g., FPGAs, SoMs, SoCs, ASICs) faces challenges including high power consumption, limited real-time performance, and dependence on Python-based or HLS toolchains. To address these, this paper proposes smallNet—a compact, hand-coded Verilog convolutional layer architecture. smallNet employs fixed-point arithmetic and a filter-like polynomial structure, requiring no Xilinx IP cores, VLSI design tools, or external Python libraries, thereby enhancing hardware portability and deployment flexibility. Evaluated on a single-core Xilinx Zynq-7000 Cora Z7 platform, smallNet achieves 81.2% classification accuracy, delivers a 5.1× speedup over CPU-based inference, and consumes only 1.5 W total system power. This work establishes a lightweight, self-contained, and synthesizable hardware implementation paradigm for low-power, real-time edge intelligence.

0 citationsRead paper