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Researcher profile

Lian Wen

Lian Wen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceLogic in Computer ScienceMachine LearningNeural and Evolutionary Computing

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Published work

3 published item(s)

preprint2026arXiv

Limited Math: Aligning Mathematical Semantics with Finite Computation

Classical mathematical models used in the semantics of programming languages and computation rely on idealized abstractions such as infinite-precision real numbers, unbounded sets, and unrestricted computation. In contrast, concrete computation is inherently finite, operating under bounded precision, bounded memory, and explicit resource constraints. This discrepancy complicates semantic reasoning about numerical behavior, algebraic properties, and termination under finite execution. This paper introduces Limited Math (LM), a bounded semantic framework that aligns mathematical reasoning with finite computation. Limited Math makes constraints on numeric magnitude, numeric precision, and structural complexity explicit and foundational. A finite numeric domain parameterized by a single bound \(M\) is equipped with a deterministic value-mapping operator that enforces quantization and explicit boundary behavior. Functions and operators retain their classical mathematical interpretation and are mapped into the bounded domain only at a semantic boundary, separating meaning from bounded evaluation. Within representable bounds, LM coincides with classical arithmetic; when bounds are exceeded, deviations are explicit, deterministic, and analyzable. By additionally bounding set cardinality, LM prevents implicit infinitary behavior from re-entering through structural constructions. As a consequence, computations realized under LM induce finite-state semantic models, providing a principled foundation for reasoning about arithmetic, structure, and execution in finite computational settings.

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preprint2026arXiv

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning

Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In this work, we introduce MindWatcher, a TIR agent integrating interleaved thinking and multimodal chain-of-thought (CoT) reasoning. MindWatcher can autonomously decide whether and how to invoke diverse tools and coordinate their use, without relying on human prompts or workflows. The interleaved thinking paradigm enables the model to switch between thinking and tool calling at any intermediate stage, while its multimodal CoT capability allows manipulation of images during reasoning to yield more precise search results. We implement automated data auditing and evaluation pipelines, complemented by manually curated high-quality datasets for training, and we construct a benchmark, called MindWatcher-Evaluate Bench (MWE-Bench), to evaluate its performance. MindWatcher is equipped with a comprehensive suite of auxiliary reasoning tools, enabling it to address broad-domain multimodal problems. A large-scale, high-quality local image retrieval database, covering eight categories including cars, animals, and plants, endows model with robust object recognition despite its small size. Finally, we design a more efficient training infrastructure for MindWatcher, enhancing training speed and hardware utilization. Experiments not only demonstrate that MindWatcher matches or exceeds the performance of larger or more recent models through superior tool invocation, but also uncover critical insights for agent training, such as the genetic inheritance phenomenon in agentic RL.

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preprint2026arXiv

Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks

Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper introduces ASTDP-GAD, a novel Adaptive Spiking Temporal Dynamics Plasticity framework for Graph Anomaly Detection that integrates spiking graph neural networks with STDP learning for energy-efficient neuromorphic detection in dynamic networks. Our framework unifies spiking neural computation, STDP learning, and graph-based anomaly detection through the following key innovations: temporal spike graph encoding with adaptive Leaky Integrate-and-Fire (LIF) dynamics; LIF-based graph attention with lateral inhibition; event-driven hypergraph memory with STDP-inspired prototype updates; spike rate contrast pooling based on spiking irregularity; adaptive STDP layers capturing causal temporal relationships; and multi-scale temporal convolution with multi-factor anomaly fusion. Theoretical analysis provides rigorous guarantees: spike encoding preserves input information with resolution scaling linearly in simulation steps and hidden dimension; LIFGAT approximates any continuous attention function; hypergraph memory converges to optimal prototypes; contrast pooling achieves provable anomaly selection bounds; STDP learning converges stably; and multi-factor fusion produces calibrated scores with up to $5\times$ variance reduction. Extensive experiments on nine datasets on both dynamic and static graphs demonstrate superior anomaly detection accuracy while maintaining biological plausibility and energy efficiency for neuromorphic deployment.

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