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Mengquan Liu

Publications and source records attributed to Mengquan Liu.

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Discovery of a millisecond pulsar with a CO white dwarf companion

We report the discovery and characterization of PSR J1810-0623, a fully recycled millisecond pulsar with a spin period of 4.55 ms, discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) and followed up with FAST and the Green Bank Telescope (GBT). A phase-connected timing solution spanning over 6.5 years reveals a 15.4-day binary orbit with extremely low eccentricity (about 1.5E-5). Assuming a neutron star mass of 1.4 Msun, the inferred companion median mass (about 0.64 Msun) is consistent with a carbon-oxygen white dwarf, indicating an evolutionary origin in an intermediate mass Xray binary. The system's properties closely resemble those of other massive white dwarf binaries thought to form via Case A Roche lobe overflow, suggesting a prolonged accretion phase during which the neutron star was efficiently recycled. Polarimetric analysis of FAST data yields a moderate degree of linear polarization and a rotation measure of 86.6 pm 0.6 rad/m^2, offering constraints on the Galactic magnetic field. The inferred characteristic age (about 32 Gyr) and low surface magnetic field (about 1E8 G) indicate a highly recycled pulsar. Proper-motion measurements imply a modest transverse velocity, consistent with those of recycled millisecond pulsars in the Galactic field. Although the proximity of the globular cluster Pal 7 raises the possibility of a dynamical origin, discrepancies in dispersion measure and proper motion argue against a physical association. PSR J1810-0623 adds to the rare class of long-orbital period MSP-COWD systems and provides a valuable laboratory for studying pulsar recycling, binary evolution, and Galactic structure.

astro-ph.HE

GraphMASAL: A Graph-based Multi-Agent System for Adaptive Learning

The advent of Intelligent Tutoring Systems (ITSs) has marked a paradigm shift in education, enabling highly personalized learning pathways. However, true personalization requires adapting to learners' complex knowledge states (multi-source) and diverse goals (multi-sink); existing ITSs often lack the necessary structural-reasoning capability and knowledge dynamism to generate genuinely effective learning paths, and they lack scientifically rigorous validation paradigms. In this paper we propose GraphMASAL (A Graph-based Multi-Agent System for Adaptive Learning), which integrates (i) a dynamic knowledge graph for persistent, stateful learner modeling; (ii) a LangGraph-orchestrated trio of agents (Diagnostician, Planner, Tutor); (iii) a knowledge-graph-grounded two-stage neural IR component (dual-encoder dense retrieval with cross-encoder listwise re-ranking and calibrated score fusion); and (iv) a multi-source multi-sink (MSMS) planning engine with a cognitively grounded cost and an approximation guarantee via greedy set cover. Under blinded automated evaluations with matched inputs and inference settings across diverse student profiles, GraphMASAL consistently outperforms LLM prompting and structured ablations in planning--achieving stronger structural/sequence alignment of learning paths, higher coverage of weak concepts, and lower learning cost--while also surpassing prompt-based baselines in cognitive diagnosis. Agreement with expert/LLM-proxy ratings further supports the validity of our evaluation protocol. These findings indicate that grounding LLM agents in a dynamic knowledge graph, coupled with optimization under educational constraints, yields reliable, interpretable, and pedagogically plausible learning plans, advancing personalized and goal-oriented education.

cs.MA