activity
20242026
most citedI Can Tell What I am Doing: Toward Real-World Natural Language Grounding of Robot Experiences

2 citations · 4 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CL2026

Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASR

Zilai Wang, Natarajan Balaji Shankar, Kaiyuan Zhang +2

Self-supervised learning (SSL) models have achieved impressive results across many speech tasks, yet child automatic speech recognition (ASR) remains challenging due to limited dat…

cs.CL2026

SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature

Yiming Ren, Junjie Wang, Yuxin Meng +11

Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic "Needle-In-A-Haystack" tests…

cs.CR2026

Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks

Hoagy Cunningham, Jerry Wei, Zihan Wang +26

We introduce enhanced Constitutional Classifiers that deliver production-grade jailbreak robustness with dramatically reduced computational costs and refusal rates compared to prev…

cs.LG2025

Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations

Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang +1

The integration of graphs with Goal-conditioned Hierarchical Reinforcement Learning (GCHRL) has recently gained attention, as intermediate goals (subgoals) can be effectively sampl…

cs.SE20251 cited

AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions

Zihan Wang, Jiaze Chen, Zhicheng Liu +25

Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on exi…

cs.AI2025

Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods

Boris Sedlak, Alireza Furutanpey, Zihang Wang +2

Edge computing breaks with traditional autoscaling due to strict resource constraints, thus, motivating more flexible scaling behaviors using multiple elasticity dimensions. This w…