3 citations · 4 across the 11 of their papers we have counts for
18 papers
EnvOpt: Automated Optimization for Agent Environments
Mingxuan Li, Huanzhi Mao, Qiuyang Mang +3
An LLM agent's cost and success depend on its model, its harness, and its environment. In addition to the widely studied directions of parameter tuning and harness optimization, we…
SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
Mingxuan Li, Huanzhi Mao, Ruitao Zheng +5
AI Agents are becoming digital coworkers that can browse the web, write code, run experiments, and assist humans over extended horizons. Yet existing benchmarks fall short of real…
Two Fast Radio Burst Candidates Detected in the RATAN-600 Blind Survey
S. A. Trushkin, D. O. Kudryavtsev, P. G. Tsybulev +1
We report the detection of two single dispersed pulses with widths of about 6.7\,ms and 15.6\,ms and flux densities of about 0.8 and 1.7\,Jy. These were found in a blind survey of…
I-PTC: Interactive Programmatic Tool Calling for Stateful Tool-Augmented Agents
Huanzhi Mao, Chengkun Cao, Shuo Yuan +1
Large language models are increasingly capable of writing and executing code, suggesting a natural paradigm for tool-augmented agents: expressing tool use as programs rather than i…
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph
Jiaxin Fang, Runyuan He, Arnav Gupta +3
High-quality coding problems are expensive to collect. Benchmarks such as SWE-bench rely on substantial human curation, while synthetic alternatives typically rely on costly model…
EasyPPO: Stabilizing the Critic Is Key
Xuanyi Zhou, Qiuyang Mang, Huanzhi Mao +7
A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning to estimate expected returns…
DeployBench: Benchmarking LLM Agents for Research Artifact Deployment
Yuanli Wang, Yaoyao Qian, Yue Zhang +8
LLM agents have made rapid progress on software engineering and ML research tasks, but these advances often assume access to a working runnable environment. For research artifacts…
OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
Shang Zhou, Wenhao Chai, Kaiyuan Liu +3
Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. Scaling breadth by sampling m…
Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs
Guangyu Feng, Huanzhi Mao, Prabal Dutta +1
Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. Under these semantics, LLM deco…
FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale
Runyuan He, Qiuyang Mang, Shang Zhou +14
Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implement…
SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing
Xuanyi Zhou, Qiuyang Mang, Shuo Yang +7
Diffusion Transformers (DiTs) have become a leading backbone for video generation, yet their quadratic attention cost remains a major bottleneck. Sparse attention reduces this cost…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents
Daud Waqas, Aaryamaan Golthi, Erika Hayashida +1
Multi-turn tool-calling LLMs (models capable of invoking external APIs or tools across several user turns) have emerged as a key feature in modern AI assistants, enabling extended…
FrontierCS: Evolving Challenges for Evolving Intelligence
Qiuyang Mang, Wenhao Chai, Zhifei Li +48
We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…
Measuring Agents in Production
Melissa Z. Pan, Negar Arabzadeh, Riccardo Cogo +22
LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful. We present the first syst…
Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live
Hanchen Li, Runyuan He, Qiuyang Mang +8
KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests' KV cache if new requests are waiting. Thi…
AutoCode: LLMs as Problem Setters for Competitive Programming
Shang Zhou, Zihan Zheng, Kaiyuan Liu +18
Writing competitive programming problems is exacting. Authors must: set constraints, input distributions, and edge cases that rule out shortcuts; target specific algorithms (e.g.,…
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…