Institution profile

King's College London

Academic institutioneurope · gb
Official website
Research library1,092linked papers
Opportunities0open roles
Selected work

Representative Papers

When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

Jun 01, 2024IEEE Communications Magazine

To address the stringent requirements of ultra-low latency, ultra-high reliability, and fresh information (quantified by Age of Information, AoI) in xURLLC systems, this paper proposes a NOMA-assisted uplink architecture. It introduces stochastic network calculus (SNC) into the NOMA-xURLLC domain for the first time, establishing a unified theoretical framework that enables joint statistical QoS provisioning (SQP) for latency, AoI, and reliability tail distributions. Furthermore, we propose an SQP-driven power optimization paradigm, leveraging convex optimization and a customized power allocation algorithm to minimize uplink transmit power while satisfying multi-dimensional QoS constraints. Simulation results demonstrate that the proposed scheme outperforms conventional orthogonal multiple access across all key metrics—latency, AoI, reliability, and energy efficiency.

7 citationsRead paper

Non-Abelian qLDPC: TQFT Formalism, Addressable Gauging Measurement and Application to Magic State Fountain on 2D Product Codes

Jan 11, 2026

This work addresses the challenge of reconciling connectivity and universality in two-dimensional architectures for fault-tolerant quantum computation with qLDPC codes. By generalizing Kitaev’s non-Abelian topological code to non-Abelian qLDPC codes, the authors construct a combinatorial topological quantum field theory based on Poincaré CW complexes and introduce a spacetime path integral formulation to enable addressable gauge measurements. The key innovation lies in the first realization of native non-Clifford logical gates on constant-rate two-dimensional hypergraph product codes, achieved through an addressable measurement scheme rooted in 0-form subcomplex symmetries, which is further extended to higher-dimensional and higher-order symmetries. This approach is successfully applied to magic state distillation, enabling the parallel preparation of $O(\sqrt{n})$ disjoint CZ magic states, each with code distance $O(\sqrt{n})$, on $n$ physical qubits.

4 citations2 influentialRead paper

Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain

Feb 10, 2026

Existing self-evolution systems for large language models often plateau rapidly due to synthetic data lacking sufficient learnable information gain. This work proposes a triadic role-based self-evolution framework comprising a proposer, a solver, and a verifier, which sustains high information gain across iterative cycles through asymmetric weak–strong–weak co-evolution, dynamic model capacity expansion, and active incorporation of external knowledge. By centering the system design on learnable information gain, this study achieves, for the first time, sustainable self-evolution at the architectural level. Empirical validation on self-play programming tasks demonstrates the framework’s capability to consistently surpass performance plateaus and enable stable, continuous capability improvement.

4 citationsRead paper

Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients

Jan 30, 2026

This work elucidates the mechanism underlying standard deviation normalization in GRPO and the conditions under which it enhances training efficacy. By analyzing sequence-level policy gradients through the lens of local curvature, the study establishes—for the first time—a theoretical link between this normalization scheme and adaptive gradient updates. It proposes a three-stage training dynamics framework that clarifies the interplay between feature orthogonality and reward variance. Through rigorous convergence analysis, local curvature modeling, and empirical validation on the GSM8K and MATH benchmarks, the paper demonstrates that GRPO achieves faster convergence than unnormalized REINFORCE under mild conditions and identifies three distinct training phases governed by the interplay of orthogonality and reward variance.

4 citationsRead paper

Causal Explanations for Image Classifiers

Nov 13, 2024arXiv.org

Existing explanation methods for image classifiers lack rigorous formal definitions of causality and explanation, relying predominantly on heuristic strategies. Method: This paper introduces the Halpern–Pearl theory of actual causality to black-box image classification interpretability—the first systematic application of this causal framework to the domain. We propose REX, a causally grounded explanation generation framework that formally defines “cause” and “explanation,” designs a provably terminating algorithm for approximating minimal explanations, and implements an iterative solving mechanism with controllable computational complexity. Contribution/Results: The implemented tool REX outperforms state-of-the-art black-box explanation methods across explanation compactness, computational efficiency, and standard quality metrics (e.g., fidelity, stability, and comprehensibility). Experiments demonstrate that REX produces the most concise explanations and achieves the fastest convergence. This work establishes a rigorous causal foundation for explainable AI while delivering a practical, scalable technical solution.

4 citationsRead paper
Recent publications

Latest Papers