Author

Zhenglin Wan

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#artificial intelligence Preprint Oct 2026

Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions

An agent whose tool keeps returning nothing useful should stop relying on it. In a retrieval environment with controlled source failures, we separate how agents judge results from what they do. We compare stopping at the same step after longer and shorter runs of results the agent judged useless; this contrast is zero...

Chubin Zhang, Zheng-Lin Wan, Xing-Rui Yu et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs

Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing vision token pruning methods mitigate this overhead, they implicitly assume that a single fixed pruning strategy can be applied uniformly across all inputs. Our analys...

Hai-Jin Liang, P. Zhou, Zheng-Lin Wan et al. · 0 citations

Preprint Aug 2026

Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models

This work proposes Reinforcement Learning with Human-Engine Verification (RLHEV), a post-training paradigm that combines dense engine signals with implicit human acceptance feedback from the development process to support RL post-training.

P. Zhou, Hesong Wang, Zhengfeiyang Zhang et al. · 0 citations

Preprint Aug 2026

Improving Generalization Robustness of Multimodal RLVR

Reinforcement Learning with Verifiable Rewards (RLVR) makes Multimodal Large Language Models more accurate, but the gains are brittle: simply paraphrasing a question or changing the prompt template can degrade them, which challenges reliable deployment in high-stakes scenarios like medical VQA. We trace this to two iss...

P. Zhou, Zhiwei Tang, Xiaopeng Peng et al. · 0 citations

#artificial intelligence Preprint Aug 2026

SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models

Spiking neural networks (SNNs) offer a path to energy-efficient language modeling through sparse encoding and event-driven computation, but training capable spiking language models from scratch remains difficult. A practical alternative is ANN-to-SNN migration through knowledge distillation (KD), where a pretrained art...

En-Qiao Lu, Xingrui Yu, Yi-Wei Fu et al. · 0 citations

Preprint Aug 2026

SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks

CRM+RCCR, an architecture-agnostic cost-aware objective that encodes cost preference into continuous relevance targets through per-pair independent scoring, eliminating multi-positive dilution while regularizing queries with similar routing preferences to be closer in the routing space.

Tao Yu, Yi-Fei Qu, Zhi-Qing Cui et al. · 1 citation