arXiv subjects

Qingsong Li

Publications and source records attributed to Qingsong Li.

3 recordsLinked to original sources

arXiv2026

Entanglement islands of 2D charged scalar-hairy black holes on the brane

We investigate the entanglement dynamics and Page curve of a 2D brane Maxwell-Scalar (MS) black hole coupled to thermal baths in double holography. Computing the entanglement entropy via the island formula, we find that injecting electric charge and scalar source into the system produce opposite effects: the former increases the degrees of freedom (DOF) on the brane by enhancing the effective global tension, thereby raising the saturation entropy of the system; conversely, the latter reduces the DOF on the brane by lowering the effective local tension near the IR geometry, ultimately suppressing the entanglement. Despite the non-trivial backreaction from the matter fields, the pre-saturation entanglement dynamics remain robust. We identify an early-time quadratic growth corresponding to the pre-local-thermalization phase, which smoothly transitions into a late-time linear growth. Furthermore, the scale of the Page time is governed by a competition between thermal excitations and the brane DOF. Higher temperatures accelerate the onset of the Page time by exciting more Hawking modes, while abundant DOF delay it by increasing the saturated entropy.

hep-th↗

arXiv2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

cs.AI↗

arXiv2023

Pareto Invariant Representation Learning for Multimedia Recommendation

Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences. Existing works attempt to alleviate this problem by learning invariant representations, but overlook the balance between independent and identically distributed (IID) and out-of-distribution (OOD) generalization. In this paper, we propose a framework called Pareto Invariant Representation Learning (PaInvRL) to mitigate the impact of spurious correlations from an IID-OOD multi-objective optimization perspective, by learning invariant representations (intrinsic factors that attract user attention) and variant representations (other factors) simultaneously. Specifically, PaInvRL includes three iteratively executed modules: (i) heterogeneous identification module, which identifies the heterogeneous environments to reflect distributional shifts for user-item interactions; (ii) invariant mask generation module, which learns invariant masks based on the Pareto-optimal solutions that minimize the adaptive weighted Invariant Risk Minimization (IRM) and Empirical Risk (ERM) losses; (iii) convert module, which generates both variant representations and item-invariant representations for training a multi-modal recommendation model that mitigates spurious correlations and balances the generalization performance within and cross the environmental distributions. We compare the proposed PaInvRL with state-of-the-art recommendation models on three public multimedia recommendation datasets (Movielens, Tiktok, and Kwai), and the experimental results validate the effectiveness of PaInvRL for both within- and cross-environmental learning.

cs.IR↗