Yuqing Yang @ ACL 2026

@yyqcode

Second-year PhD student @CSatUSC @nlp_usc.

ayyyq.github.io

Joined June 2023

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Yuqing Yang @ ACL 2026
@yyqcode
Jul 3
In San Diego for #ACL2026! ☀️ Happy to chat about LLM memory and agents. Our oral talk: "When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors". LLMs make careless reading mistakes just like humans do. Mon 7/6, Oral Session D
arxiv.org
When LLMs Read Tables Carelessly: Measuring and Reducing Data...
While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understanding the table...
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Yuqing Yang @ ACL 2026
@yyqcode
May 20
Excited to share that I've started my summer internship at SystemsResearch@Google in Sunnyvale, working on agentic environment generation! Always happy to chat about coding agents or LLM memory too. If you're around the Bay Area, would love to meet up.
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Yuqing Yang @ ACL 2026
@yyqcode
Apr 23
🧵 1/8 What should an LLM assistant remember across conversations? Existing memory work studies this one task at a time. But real-world assistants see all kinds of conversations, and that changes the problem. Introducing BEHEMOTH 🦣 + CluE 🌱: a benchmark & self-evolving method
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Yuqing Yang @ ACL 2026
@yyqcode
Mar 25
Coding agents running 24/7 will unlock a lot of breakthroughs 🚀. Easy to feel like we're being replaced 😨. But the real question: What can we learn from this, and where do they still fall short? New blog ⬇️
Tengxiao Liu
@TengxiaoLiu
Mar 25
Auto research is on 🔥 We give algorithmic problems (like circle packing) to general coding agents, let it run overnight. 🌙 Agents reach SoTA. But more importantly: we analyze 100+ hours of trajectories to understand how it gets there 🧵
Yuqing Yang @ ACL 2026
@yyqcode
Nov 26, 2025
A practical insight!
Tengxiao Liu
@TengxiaoLiu
Nov 25, 2025
🏧Giving your agent unlimited tool calls doesn't make it smarter. 💡Why? It lacks 'Budget Awareness'! Introducing Budget Tracker, a simple plug-in that enables more effective scaling behaviors: higher performance, lower cost. Paper: arxiv.org/pdf/2511.17006