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Jincheng Zhang

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17 papers indexed here

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#reinforcement learning Open access Oct 2026

Adaptive Self-Reinforcement and Mutual Reinforcement in World Self-and-Mutual Models: A Unified Architecture for Switching Between Independent and Interdependent World-Model Evolution

World models provide internal representations of environmental states, dynamics, and potential future outcomes. However, improving a world model can follow different learning regimes. A model may improve independently through its own prediction errors, internal consistency checks, counterfactual simulation, and interac...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Switching Between Self-Reinforcement and Mutual Reinforcement in Multi-Agent Systems: A Dynamic Framework for Individual and Collective Intelligence Optimization

Multi-agent systems can improve their performance through individual learning and inter-agent cooperation. However, relying exclusively on individual reinforcement may limit the exploitation of complementary knowledge, whereas persistent mutual reinforcement may introduce communication overhead, correlated errors, exce...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Switching Between Self-Selection and Collaborative Selection in Multi-Agent Systems: A Unified Decision-Making Framework

Multi-agent systems must repeatedly select actions, solutions, resources, partners, strategies, or organizational configurations while operating under uncertainty and limited computational resources. Existing approaches often emphasize individual decision-making, collaborative decision-making, or predefined coordinatio...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Self–Mutual Error Correction in World Models: A Unified Framework for State-Dependent Switching Between Internal Repair and Cross-Model Correction

World models provide internal representations of environmental dynamics and support prediction, planning, simulation, and decision-making. However, errors in world models may persist when a system relies exclusively on its own internal representations, while external correction can introduce communication costs, confli...

Jincheng Zhang · 0 citations
#large language models Open access Oct 2026

Adaptive Learning and Collaborative Self-Adaptive Learning Between Self-and-Interaction Models: A Unified Framework for Adaptive Mode Switching

Learning systems often face a fundamental design choice between adapting independently and adapting through collaboration. Independent adaptation allows a model to preserve its autonomy, exploit its own experience, and avoid unnecessary coordination costs. Collaborative adaptation allows multiple models to exchange inf...

Jincheng Zhang · 0 citations
#large language models Open access Oct 2026

Adaptive Self-Selection and Collaborative Selection in Self-and-Other Models: A General Framework for Dynamic Selection-Mode Switching

Selection is a fundamental operation in intelligent systems, computational systems, optimization processes, and model-based reasoning. Existing approaches commonly rely on independent selection, centralized decision-making, voting, ranking, or collaborative selection mechanisms. However, a system that consistently uses...

Jincheng Zhang · 0 citations
#large language models Open access Oct 2026

Adaptive Self–Mutual Correction Between Self-and-Interactive Models: A General Framework for Error Detection, Correction, and Mode Switching

Correction is a fundamental requirement for intelligent systems that must maintain consistency, adapt to changing environments, and improve their internal representations. Existing approaches investigate error correction through self-correction, feedback control, iterative refinement, peer interaction, distributed syst...

Jincheng Zhang · 0 citations
#large language models Open access Oct 2026

World Self-and-Mutual Models: Adaptive Switching Between Self-Diagnosis and Mutual Diagnosis for World-State Understanding and Model Reliability

World models provide computational representations of environmental states, transitions, and potential outcomes. However, a world model may contain internal inconsistencies, inaccurate state estimates, incorrect transition predictions, or assumptions that no longer correspond to the environment. Diagnosing these proble...

Jincheng Zhang · 0 citations
#large language models Open access Oct 2026

Adaptive Self-Play and Multi-Model Play in Self-and-Interaction Models: A Unified Framework for Dynamic Switching Between Internal and Inter-Model Strategic Exploration

Self-play and multi-model interaction provide different mechanisms for exploring complex problem spaces. Self-play enables a model to generate, evaluate, and challenge its own strategic alternatives, whereas multi-model play enables multiple models to interact through competition, cooperation, adversarial examination,...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Parallel-World Self-Rewarding and Cross-Model Mutual Rewarding for Large Language Models

Reward generation is a central component of reinforcement learning, preference optimization, and self-improvement in large language models (LLMs). Existing approaches have demonstrated that an LLM can evaluate its own outputs and use the resulting judgments to improve its behavior. However, self-rewarding systems may s...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Switching Between Self-Reinforcement and Mutual Reinforcement Learning for Multiple Large Language Models

Large language models (LLMs) can generate candidate answers, evaluate their quality, and use evaluation signals to improve subsequent responses. However, self-evaluation may reinforce a model's own biases and errors, whereas evaluation by other models may introduce evaluator disagreement, capability mismatches, and cor...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Oct 2026

Adaptive Switching between Parallel-World Self-Reinforcement and Mutual Reinforcement for Multiple Large Language Models

Large language models (LLMs) can be improved through self-generated feedback, reward-guided optimization, self-play, and interactions among multiple agents. However, a system that consistently relies on one reinforcement mode may fail to exploit complementary learning opportunities. Self-reinforcement enables an indivi...

Jincheng Zhang · 0 citations

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