98 citations · 107 across the 17 of their papers we have counts for
22 papers
A Mechanistic Study of Automated Failure Attribution in LLM-based Agentic Systems
Wendy Zheng, Liang Wu, Yinhan He +3
Agentic systems have demonstrated strong capabilities on complex tasks by enabling multiple interacting large language model (LLM)-based agents to collaborate and execute subtasks…
Mining the Self-Rewriting System: A Vision for Data Mining on AI-Native Infrastructure
Mayank Darbari, Chiman Salavati, Liang Wu +5
Classical data mining treats data as generated outside the learner. Emerging AI-native infrastructure invalidates this assumption: as agents observe system state, act, update retri…
Capability Scaling-Down Laws for LLM Compression
Xueqi Cheng, Liang Wu, Kelly Wan +2
LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions can produ…
AnyJev Technical Report
Jiamu Zhang, Tianze Yang, Yucheng Shi +6
A typed decision is a choice among a fixed set of options, returned as a probability rather than as text. Systems that need typed decisions today use models trained for that purpos…
Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations
Guanghui Min, Liang Wu, Mingjia Shi +4
Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing pr…
Field-Aware Agent Skill Retrieval
Paimon Goulart, Liang Wu, Kelly Wan +2
As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill retrieval methods…
Self-Evolving Code-with-Image Reasoning
Tianze Yang, Liang Wu, Ruitong Sun +6
Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The central challenge is one of p…
SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding
Jiamu Zhang, Liang Wu, Kelly Wan +2
Large language models (LLMs) increasingly read long inputs in the agentic era, from whole documents and codebases to conversations across many turns. Their inference memory is then…
Towards Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability
Guanghui Min, Liang Wu, Mayank Darbari +2
Recurrent context compression controls context growth in long-horizon agents, but its behavioral effects remain poorly understood. In this preliminary empirical study, we show that…
Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn
Dan Xu, Baofen Zheng, Jianqiang Shen +11
Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job postings into standardized or…
A Unified Structured Query Understanding Framework for Industrial Semantic Search
Ping Liu, Qianqi Shen, Jianqiang Shen +15
Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components. While individually optimizable, this frag…
WiSP: A Working-Set View of Mixture-of-Experts Serving on Extremely Low-Resource Hardware
Jiamu Zhang, Liang Wu, Mayank Darbari +1
Modern local and agentic workloads often need large-model capacity at low concurrency, but run on GPUs that cannot keep a frontier-scale model resident. Mixture-of-Experts (MoE) mo…
Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G
Liang Wu, Kelly Wan, Mayank Darbari +1
The proliferation of emerging applications, such as autonomous driving and immersive experiences, demands cellular networks that are not only faster, but fundamentally more resilie…
SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
Zaiyi Zheng, Liang Wu, Guanghui Min +4
Generative recommendation treats next-item prediction as autoregressive item-identifier generation. Specifically, items are encoded as semantic identifiers (SIDs), which are short…
LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation
Zhoutong Fu, Yihan Cao, Yi-Lin Chen +16
Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applica…
SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens
Yinhan He, Wendy Zheng, Yaochen Zhu +6
The verbosity of Chain-of-Thought (CoT) reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reas…
Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems
Ping Liu, Jianqiang Shen, Qianqi Shen +9
Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on mult…
A Scalable and Efficient Signal Integration System for Job Matching
Ping Liu, Rajat Arora, Xiao Shi +13
LinkedIn, one of the world's largest platforms for professional networking and job seeking, encounters various modeling challenges in building recommendation systems for its job ma…
Understanding and Modeling Job Marketplace with Pretrained Language Models
Yaochen Zhu, Liang Wu, Binchi Zhang +5
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job…
Learning to Retrieve for Job Matching
Jianqiang Shen, Yuchin Juan, Shaobo Zhang +21
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often in…
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
Xinyuan Wang, Liang Wu, Liangjie Hong +2
Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend a…
Collaborative Large Language Model for Recommender Systems
Yaochen Zhu, Liang Wu, Qi Guo +2
Recently, there has been growing interest in developing the next-generation recommender systems (RSs) based on pretrained large language models (LLMs). However, the semantic gap be…