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Ting Peng

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Published work

5 published item(s)

preprint2026arXiv

Hierarchical Preemptive Holistic Collaborative Systems for Embodied Multi-Agent Systems: Framework, Hybrid Stability, and Scalability Analysis

The coordination of Embodied Multi-Agent Systems in constrained physical environments requires a rigorous balance between safety, scalability, and efficiency. Traditional decentralized approaches, e.g., reactive collision avoidance, are prone to local minima or reciprocal yielding standoffs due to the lack of future intent awareness. In contrast, centralized planning suffers from intractable computational complexity and single-point-of-failure vulnerabilities. To address these limitations, we propose the Hierarchical Preemptive Holistic Collaborative (Prollect) framework, which generalizes the Preemptive Holistic Collaborative System (PHCS) by decomposing the global coordination problem into topologically connected subspace optimizations. We formalize the system as a Hybrid Automaton and introduce a three-stage receding horizon mechanism (frozen execution, preliminary planning, proactive look-ahead windows) with explicit padding to prevent races between coordination dissemination and intent updates. Notably, we design a robust timing protocol with a mandatory Idle Buffer that acts as a dwell-time constraint to eliminate Zeno behaviors and ensure computational stability under jitter. Furthermore, we formalize a Shadow Agent protocol to guarantee seamless trajectory consistency across subspace boundaries, which we treat as an Input-to-State Stability (ISS) problem.

preprint2026arXiv

Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair

Code-agent RL often receives weak feedback: rollout-time signals are reliable and executable, but capture only necessary or surface conditions for task success rather than the target semantic predicate. Using agentic compile-fix as the setting, we study signal reshaping for standard GRPO under such feedback. Our central claim is that GRPO's within-group comparison is meaningful only after three kinds of signals are reshaped: outcome rewards recover semantic ranking, process signals localize intra-trajectory credit, and rollouts from the same prompt remain execution-comparable. We operationalize these conditions with a minimal signal-reshaping construction that leaves GRPO's group-normalized advantage construction unchanged: compile-and-semantic layered rewards reshape trajectory ranking, step-level process scores outside group reward normalization reshape within-trajectory update strength, and failure-cause-aware rollout governance reshapes within-group comparability. Experiments show a clear end-to-end gain: full signal-reshaped GRPO improves strict compile-and-semantic accuracy from the base model's zero-shot $0.385$ to $0.535$. Controlled comparisons further explain the source of this gain: binary rewards remove the compile-only middle tier and degrade trajectory control; on top of layered rewards, process-score weighting further improves accuracy from $0.48$ to $0.53$ and reduces average evaluation steps from $23.50$ to $17.02$. As a boundary comparison, privileged-prompt token-level distillation mainly optimizes local distributional alignment; in long tool-use trajectories, this signal is diluted by non-critical tokens and cannot replace outcome semantics, process credit, or within-group comparability.

preprint2020arXiv

Matching model of flow table for networked big data

Networking for big data has to be intelligent because it will adjust data transmission requirements adaptively during data splitting and merging. Software-defined networking (SDN) provides a workable and practical paradigm for designing more efficient and flexible networks. Matching strategy in the flow table of SDN switches is most crucial. In this paper, we use a classification approach to analyze the structure of packets based on the tuple-space lookup mechanism, and propose a matching model of the flow table in SDN switches by classifying packets based on a set of fields, which is called an F-OpenFlow. The experiment results show that the proposed F-OpenFlow effectively improves the utilization rate and matching efficiency of the flow table in SDN switches for networked big data.

preprint2020arXiv

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.

preprint2015arXiv

Quantitative Analysis of Particles Segregation

Segregation is a popular phenomenon. It has considerable effects on material performance. To the author's knowledge, there is still no automated objective quantitative indicator for segregation. In order to full fill this task, segregation of particles is analyzed. Edges of the particles are extracted from the digital picture. Then, the whole picture of particles is splintered to small rectangles with the same shape. Statistical index of the edges in each rectangle is calculated. Accordingly, segregation between the indexes corresponding to the rectangles is evaluated. The results show coincident with subjective evaluated results. Further more, it can be implemented as an automated system, which would facilitate the materials quality control mechanism during production process.