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Xiaoyang Li

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#artificial intelligence Preprint Sep 2026

A Benchmark and Diagnostic Study of Epistemic Admission in Shared Agent Memory

The Correlated Promotion Benchmark (CPB), which evaluates whether candidate claims should be admitted to shared memory, shows that policies which deduplicate sources reject many true claims alongside false ones, whereas policies preserving answer coverage admit nearly as many false claims as unrestricted sharing.

Xiao-Yang Li, Yi-Qi Wang, Chen-Cheng Zhu et al. · 1 citation
#artificial intelligence Preprint Sep 2026

IndustrialVLA-Bench: A Traceable Multi-Axis Evaluation of Open Robot Policy Models

IndustrialVLA-Bench is presented, an evidence-aware evaluation of six released VLA and WAM systems under a unified reporting schema that evaluates clean capability on LIBERO, non-language robustness on LIBERO-Plus, instruction sensitivity on LIBERO-Para, and observed execution cost.

Yi-Qi Wang, Zhi-Feng Rao, Jia-Qi Zhang et al. · 1 citation
Book Open access Aug 2026

BiVCoder: A Multi-Agent Framework for Code Generation via Bidirectional Code-Test Diagnosis

This work proposes BiVCoder, a diagnosis-driven multi-agent framework featuring a novel bidirectional code-test diagnosis mechanism, and introduces BiVCoder-SFT, a role-specific instruction fine-tuning scheme.

Xiaoyang Li, Jin-Hao Dong, Wenhang Shi et al. · 0 citations
Jul 2026

MemTX: Transactional Belief Commit for Stateful Agent Memory

This work argues that a memory write is not a belief commit, and presents MemTX, a transactional belief-commit protocol, a transactional belief-commit protocol that leads all eight baselines with paired-McNemar significance on four backbones and statistically ties the best baseline on the fifth and strongest, while rem...

Xiaoyang Li, Yi-Qi Wang, Haohui Lu et al. · 5 citations · ⚡1
Book Open access Aug 2026

BiVCoder: A Multi-Agent Framework for Code Generation via Bidirectional Code-Test Diagnosis

Large Language Models (LLMs) have demonstrated remarkable potential in automated code generation. However, existing test-driven code generation and refinement frameworks are often hindered by the tests' quality: they typically treat self-generated tests as ground truth, leading to ineffective debugging loops where code...

Xiaoyang Li, Jinhao Dong, Wenhang Shi et al. · 0 citations
Jul 2026

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications

A one-round study provides initial evidence for PRD-guided self-evolution, motivating validation at larger scales and in industrial settings, and presents AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), a...

Hao Jiang, Gang-Tao Xin, Ying Huang et al. · 0 citations

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