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Qi-Liang Jiang

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Preprint Sep 2026

KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation

Generative recommendation, has been attracted a surge of attentions in industrial and academic research community, towards to build more smart system to build next-generation recommender. Under the significant developing wave of large language model, our team have been developed Semantic ID based OneRec/OneRec-V2. Thes...

Jiang-Xia Cao, Hao Peng, Wen-Long Xu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents

A runtime gate for an LLM tool agent is usually cast as a filter. In a ReAct loop a rejected proposal is followed by another at the same state, so the gate is a search operator over the proposal stream whose admission criterion shapes which trajectories are reachable. We study post-violation recovery admission, where p...

Chen-Yu Zhou, Qi-Liang Jiang, Shu-Ning Wu et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Coverage, Not Targeting: A Structural Regime in Multi-Turn Agent Credit Assignment

Multi-turn agentic RL increasingly treats credit assignment as a targeting problem: given a terminal verifiable reward, per-turn methods localize credit onto the turns that mattered. We identify the structural quantity that predicts when this is the right move, the verifier information density V_d = k/C (the fraction o...

Chen-Yu Zhou, Qi-Liang Jiang, Shu-Ning Wu et al. · 0 citations
#software testing Preprint Aug 2026

PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?

PLCBENCH is presented, to the authors' knowledge, the first real-PLC hardware-in-the-loop (HIL) framework for characterizing this cyber-to-physical capability and its boundaries and it combines vendor-native interaction, commercial PLC execution, closed-loop reduced-order process simulation, and independent outcome ver...

Yitian Zhou, Jing-Yu Zheng, Qi-Liang Jiang et al. · 0 citations
Jun 2026

Certified Speculative Execution for Untrusted AI Agents

Hard-constrained sequential decision systems have no certified way to spend the test-time compute of modern AI: executing the multi-step drafts of a learned policy or a frozen LLM forfeits the feasibility guarantee a trusted solver provides, while invoking the solver at every step forfeits the speed the AI offers. Cert...

Chenyu Zhou, Qiliang Jiang, Shuning Wu et al. · 1 citation

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