Skip to content

Author

Wei-Chen Liu

We have 5 of 36 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Conference Aug 2026

Online Failure Detection for Robot Manipulation with Time-Aware Metrics and Efficient Models

Vision-Language-Action (VLA) models have recently achieved strong performance in robot manipulation, with promising generalization across diverse tasks and environments. However, long-horizon executions and unseen scenarios can still lead to failures, highlighting the need for online failure detection to enable timely...

Tian-Rui Ma, Min-Hao Fan, Wei-Chen Liu · 0 citations
#artificial intelligence Preprint May 2026

BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization

BitsMoE is proposed, a cost-aware mixed-precision quantization framework built on two complementary techniques that separates expert weights into a shared basis and expert-specific spectral components, defining structural quantization units while exploiting cross-expert redundancy.

Jiayu Zhao, Zi-Han Teng, Min-Hao Fan et al. · 0 citations
Jul 2026

Mapping Without Graphs: Learning Coherence Traffic for Task Placement

CoTM is proposed, a coherence-aware task mapping framework that constructs task graphs by inferring inter-task dependencies from dynamic coherence behavior, guided by a coherence-aware penalty function that jointly considers coherence traffic and NoC performance metrics.

Guochu Xiong, Tian-Rui Ma, Wei-Chen Liu · 0 citations
Preprint Jul 2026

Coherence in Control: Bridging Many-Core Mapping and Routing through Cost Unification

Co is proposed, a coherence-aware co-optimization framework that jointly integrates task mapping and routing under a unified cost model for realistic scenarios, enabling coherence-aware decision-making and effective trade-offs among optimization goals.

Guochu Xiong, Xiangzhong Luo, Weichen Liu · 0 citations
Open access Aug 2026

Retention-Based Energy-Efficient and High-Core-Utilization Scheduling for Arbitrary-Deadline DAGs

A retention-oriented scheduling framework for always-on arbitrary-deadline Directed Acyclic Graph workloads, consisting of two algorithms with a clear progression, and shows that, compared with a representative work-conserving baseline with automatic retention/PG, PREHS reduces static energy consumption.

Xiangzhen Xiao, Weijie Wang, Wei-Chen Liu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.