collaborators

8 papers

cs.AI2026

Multi-Channel Mitigation of Source-Trust Shortcuts in Fact-Checking RL Agents

Jianchang Su, Yiwei Yang, Wei Zhang

Retrieval-augmented fact-checkers often receive a reliability label, such as HIGH or LOW trust, for each evidence source. These labels should adjust the model's confidence and its…

cs.AI2026

Constraints Are Graphs, Not Chains: Exact Decoding for Diffusion Language Models

Jianchang Su, Wei Zhang

Diffusion language models (dLLMs) predict masked positions in arbitrary order, but their exact constrained decoders still encode constraints as sequential languages, whose state mu…

cs.LG2026

Refreshing Less, Selecting Better: Reusing Stale Gradient Features for Efficient Influence-Based Data Selection

Jianchang Su, Yifan Zhang, Wei Zhang

Gradient-based data selection methods such as LESS score each candidate by the alignment between its gradient and a target validation gradient, and recomputing per-example gradient…

cs.AI2026

When Can First-Order Models of Fine-Tuning Bound Forgetting?

Jianchang Su, Wei Zhang

Fine-tuning a language model on new data can make it forget facts that it should keep. We ask whether measurements taken at the start of a fine-tuning run can bound, for each prote…

cs.NI2026

Libra: Accelerating Socket I/O via Programmable Selective Data Copying

Kairui Zhou, Shengkai Lin, Wei Zhang +1

Layer-7 (L7) proxies are critical to modern cloud-native systems, yet their performance is increasingly bottlenecked by copying entire payloads across the kernel-user boundary. Exi…

cs.OS2026

ReFlux: Reversible Compute Placement for CXL-Enabled Storage

Yanpeng Hu, Yiwei Yang, Yusheng Zheng +4

Static offload to computational storage devices proves brittle because device-side processors throttle under sustained thermal load, while opaque, vendor-specific interfaces inflat…

cs.LG2026

SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning

Jianchang Su, Yifan Zhang, Shengkai Lin +4

Multi-stage ML inference pipelines are difficult to autoscale due to heterogeneous resources, cross-stage coupling, and dynamic bottleneck migration. We present SAIR, an autoscalin…

cs.OS2025

gpu_ext: Extensible OS Policies for GPUs via eBPF

Yusheng Zheng, Tong Yu, Yiwei Yang +8

Performance in modern GPU-centric systems increasingly depends on resource management policies, including memory placement, scheduling, and observability. However, uniform policies…