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University of North Carolina

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Research library222linked papers
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Selected work

Representative Papers

SimpleMem: Efficient Lifelong Memory for LLM Agents

Jan 05, 2026arXiv.org

This work addresses the challenge of inefficient historical experience utilization in large language model (LLM) agents during prolonged, complex interactions, where redundant memory or excessive reasoning overhead often impede performance. To tackle this, the authors propose a semantically lossless, high-efficiency memory framework that enhances memory density while preserving information fidelity through a three-stage mechanism: structured compression, recursive integration, and query-aware adaptive retrieval. Key innovations include entropy-aware filtering, multi-perspective indexed memory units, asynchronous recursive abstraction, and a dynamic retrieval strategy driven by query complexity. Experimental results demonstrate that the proposed approach achieves an average F1 score improvement of 26.4% on benchmark tasks and reduces token consumption during inference by up to 30×, substantially outperforming existing methods.

5 citationsRead paper

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent

Sep 13, 2024arXiv.org

Stein variational gradient descent (SVGD) suffers from slow convergence rates under finite-particle regimes, particularly with respect to kernelized Stein discrepancy (KSD) and Wasserstein-2 distance. Method: We conduct a rigorous theoretical analysis of SVGD’s convergence behavior, leveraging relative entropy methods, Matérn kernel constructions, propagation-of-chaos theory, and particle-system dynamics modeling. We further propose an extended SVGD framework based on bilinear kernels to enable continuous-time analysis. Results: We establish, for the first time, an explicit $O(1/sqrt{N})$ convergence rate in KSD, achieving double-exponential acceleration over prior bounds. Moreover, we provide the first Wasserstein-2 convergence guarantee for the continuous-time dynamics of SVGD. Our analysis reveals polynomial dependence of the KSD rate on dimensionality and rigorously proves long-term convergence of marginal distributions and propagation of chaos in the mean-field limit.

3 citationsRead paper

Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization

Oct 07, 2023

This work investigates the implicit strategic neural computation mechanisms underlying large language models (LLMs) in text tasks. To this end, we propose NPEFF—the first non-negative per-sample Fisher decomposition framework—which attributes model decisions to interpretable rank-1 positive semidefinite strategic components, enabling both modeling and intervention at the strategy level. NPEFF integrates non-negative matrix factorization, per-sample Fisher information estimation, and strategy-directed parameter perturbation. We benchmark against baselines including sparse autoencoders and gradient clustering. Empirical evaluation across multilingual LLMs and diverse tasks demonstrates that: (i) the extracted strategies exhibit high human interpretability; (ii) they support selective strategic interference; and (iii) they provide novel analytical tools for studying catastrophic forgetting side effects and the mechanisms of in-context learning.

1 citationsRead paper
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