23 citations · 24 across the 8 of their papers we have counts for
9 papers
Schema: Discovering Unknown Environments via Agentic Program Induction
Guanning Zeng, Jiani Wang, Wenjie Ma +8
Learning to complete tasks in unfamiliar environments with unknown rules remains a key challenge for LLM agents. Current LLM agents often record their discoveries in prose, which m…
BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
Yangzhen Wu, Aaron J. Li, Wenjie Ma +10
The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or pr…
optimize_anything: A Universal API for Optimizing any Text Parameter
Lakshya A Agrawal, Donghyun Lee, Shangyin Tan +11
Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are formulated as improving a tex…
RAG over Thinking Traces Can Improve Reasoning Tasks
Negar Arabzadeh, Wenjie Ma, Sewon Min +1
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as ma…
Delta Fair Sharing: Performance Isolation for Multi-Tenant Storage Systems
Tyler Griggs, Soujanya Ponnapalli, Dev Bali +8
Modern storage systems, often deployed to support multiple tenants in the cloud, must provide performance isolation. Unfortunately, traditional approaches such as fair sharing do n…
Reliable Fine-Grained Evaluation of Natural Language Math Proofs
Wenjie Ma, Andrei Cojocaru, Neel Kolhe +6
Recent advances in large language models (LLMs) for mathematical reasoning have largely focused on tasks with easily verifiable final answers while generating and verifying natural…
Reasoning Models Can Be Effective Without Thinking
Wenjie Ma, Jingxuan He, Charlie Snell +3
Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question wheth…
The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks
Alejandro Cuadron, Dacheng Li, Wenjie Ma +13
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…