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University of Chinese Academy of Sciences

Academic institutionasia · cn
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Research library3,552linked papers
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Selected work

Representative Papers

TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage

Aug 07, 2023

To address LLMs’ weak task planning capability and brittle tool invocation in complex scenarios, this paper proposes TPTU—the first structured LLM agent framework that explicitly decouples and formalizes task planning and tool utilization as dual core competencies. It introduces a collaborative reasoning mechanism between single-step and sequential agents, supports extensible agent-type specialization, and integrates prompt engineering, dynamic tool selection, multi-step reasoning scheduling, and structured output parsing—ensuring compatibility with diverse mainstream LLMs. A systematic evaluation of 12 LLMs across representative tasks reveals three fundamental bottlenecks: insufficient planning depth, poor tool generalization, and weak error recovery. Based on these findings, we establish the first benchmark suite tailored for practical AI agents, providing both theoretical foundations and empirical pathways for advancing agent architecture design and capability enhancement. (149 words)

49 citations1 influentialRead paper

Dynamic and Adaptive Feature Generation with LLM

Jun 04, 2024arXiv.org

Existing feature engineering approaches suffer from three fundamental limitations: poor interpretability, weak generalizability, and inflexible strategies—hindering practical deployment across diverse scenarios. To address these challenges, this paper proposes the first large language model (LLM)-driven dynamic adaptive feature generation paradigm. Our method integrates task-aware prompting with semantic modeling of the feature space, enabling real-time, interpretable, and controllable feature generation tailored to both data characteristics and task requirements. It ensures cross-modal and cross-task generality while maintaining full transparency in the feature generation process. Extensive experiments on multiple structured and unstructured data tasks demonstrate that features generated by our approach improve feature quality by 23.6% and boost downstream model performance by an average of 11.4%, significantly outperforming conventional automated feature engineering methods.

21 citations1 influentialRead paper

Learning Critically: Selective Self-Distillation in Federated Learning on Non-IID Data

Dec 01, 2024IEEE Transactions on Big Data

To address the challenges of poor generalization, slow convergence, and local model divergence from the global optimum in federated learning caused by non-IID data, this paper proposes a selective self-distillation framework. The method introduces a dual-level credibility assessment—operating at both class- and sample-level—to dynamically generate fine-grained self-distillation weights, enabling adaptive integration of global knowledge into local training. Crucially, it requires no auxiliary teacher model or additional communication overhead, while providing theoretical guarantees on convergence. Extensive experiments on three standard non-IID benchmark datasets demonstrate that the proposed approach significantly improves model generalization and robustness, outperforming existing state-of-the-art methods with fewer communication rounds.

20 citations3 influentialRead paper

Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning

Mar 01, 2024The Physics of Fluids

Bionic robotic fish suffer from low propulsion efficiency and high energy consumption. Method: This study proposes an end-to-end deep reinforcement learning (DRL) control framework, introducing— for the first time in underwater bionic robotics—extended pressure sensing combined with temporal Transformer modeling, integrated with a policy transfer mechanism to enhance training stability and environmental adaptability. Training achieves autonomous, stable, and rapid convergence within CFD simulations (Re = 6000). Contribution/Results: The DRL policy improves propulsion efficiency by 37% and reduces specific energy consumption per unit thrust by 29% over conventional pre-programmed gaits. Flow-field analysis reveals that efficiency stems from embodied regulation of body deformation and vortex–body interactions. The core contribution is a novel bio-inspired locomotion control paradigm unifying perception, spatiotemporal modeling, and decision-making.

9 citationsRead paper

Continuous Speculative Decoding for Autoregressive Image Generation

Nov 18, 2024arXiv.org

Continuous autoregressive visual generation models suffer from high inference latency, while existing speculative decoding methods are restricted to discrete token spaces and lack theoretical foundations or practical techniques for continuous-valued outputs. Method: This work pioneers the extension of speculative decoding to continuous visual generation. We propose a diffusion-prior-based continuous acceptance criterion, design a denoising trajectory alignment mechanism and token pre-filling strategy to mitigate distribution mismatch, and establish a continuous accept-reject sampling framework with analytically derived upper bounds on approximation error. Contribution/Results: Our approach achieves a 2.33× inference speedup on standard diffusion-based autoregressive models while provably preserving the exact output distribution of the original model. The implementation is publicly available.

8 citationsRead paper
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