activity
20222026
most citedNeighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints

3 citations · 3 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Architecture Alignment With Sparse Priors in Tabular Foundation Models

Tianqi Zhao, Tianyi Zhuang, Shuo Duan +3

Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without task-specific training or ext…

cs.LG2026

Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents

Haoze Wu, Chuqiao Kuang, Tianyi Zhuang +1

Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sp…

cs.AI2026

UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking

Chang Liu, Chuqiao Kuang, Tianyi Zhuang +4

Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on sea…

cs.AI2025

TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios

Shaohang Wei, Wei Li, Feifan Song +5

Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend the real world. However, existing works neglect the real-world challenges for temporal reasoning: (1) i…

cs.AI2025

DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities

Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li +4

We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-lev…

cs.CL2022★ 3 cited

Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints

Chun Zeng, Jiangjie Chen, Tianyi Zhuang +5

However, current autoregressive approaches suffer from high latency. In this paper, we focus on non-autoregressive translation (NAT) for this problem for its efficiency advantage.…