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Self-evolving Agents with reflective and memory-augmented abilities

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arxiv 2409.00872 v2 pith:H2ASNZNT submitted 2024-09-01 cs.CL

Xuechen Liang , Yangfan He , Yinghui Xia , Xinyuan Song , Jianhui Wang , Meiling Tao , Li Sun , Xinhang Yuan
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Jiayi Su Keqin Li Jiaqi Chen Jinsong Yang Siyuan Chen Tianyu Shi
This is my paper
classification cs.CL
keywords agentslanguagereflectiveabilitiesadvancescapabilitieschallengescontinuous
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this research, we propose a novel framework by integrating iterative feedback, reflective mechanisms, and a memory optimization mechanism based on the Ebbinghaus forgetting curve, it significantly enhances the agents' capabilities in handling multi-tasking and long-span information.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems

    cs.MA 2025-06 conditional novelty 6.0 of 10

    G-Memory stores past multi-agent teamwork in a three-tier graph and retrieves it to boost performance on five benchmarks.

  2. HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization

    cs.MA 2025-06 conditional novelty 4.0 of 10

    A hierarchical AI agent framework that dynamically hires and fires specialist models and creates tools reports strong benchmark numbers, though its gains may come from tool use rather than the architecture.

  3. A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture

    cs.LG 2025-09 reject novelty 3.0 of 10

    The paper claims a BiLSTM-AM-VMD model achieves AUC 0.963 for early HCC diagnosis, but the evidence is undermined by contradictory dataset descriptions and missing artifacts.

  4. Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers

    cs.LG 2025-09 reject novelty 3.0 of 10

    XGBoost combining MRI radiomics and clinical biomarkers reportedly reaches C-index 0.782 for early brain tumor recurrence, but the paper's methods describe a liver-cancer cohort and no evaluation of its claimed tempor...

  5. Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Enhanced Model Architectures

    cs.LG 2025-08 unverdicted novelty 3.0 of 10

    Memory-augmented Transformer research is organized into a three-axis taxonomy bridging neuroscience memory concepts to network designs, but no new result is produced.

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