1 citations · 3 across the 15 of their papers we have counts for
17 papers
Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
Haochen Shi, Tianwen Fu, Qianggang Ding +7
People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeatedly applying a learned update…
SecureVibe: Making Vibe Coding More Secure
Danqing Wang, Baolin Peng, Zhepei Wei +8
As vibe coding becomes increasingly capable and widespread, security vulnerabilities in even functionally correct solutions are a growing concern. When investigating functionally c…
FrogNano: Training a 4B Coding Agent via Online Task Synthesis
Minseon Kim, Zhengyan Shi, Emiliano Penaloza +14
We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-tra…
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
Orchard: An Open-Source Agentic Modeling Framework
Baolin Peng, Wenlin Yao, Qianhui Wu +11
Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with external envi…
Test-Time Learning with an Evolving Library
Weijia Xu, Alessandro Sordoni, Chandan Singh +4
We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instances without parameter updates…
Evolving Programmatic Skill Networks
Haochen Shi, Xingdi Yuan, Bang Liu
We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce th…
Learning to Extract Context for Context-Aware LLM Inference
Minseon Kim, Lucas Caccia, Zhengyan Shi +4
User prompts to large language models (LLMs) are often ambiguous or under-specified, and subtle contextual cues shaped by user intentions, prior knowledge, and risk factors strongl…
Gistify! Codebase-Level Understanding via Runtime Execution
Hyunji Lee, Minseon Kim, Chinmay Singh +10
As coding agents are increasingly deployed in large codebases, the need to automatically design challenging, codebase-level evaluation is central. We propose Gistify, a task where…
BugPilot: Complex Bug Generation for Efficient Learning of SWE Skills
Atharv Sonwane, Isadora White, Hyunji Lee +8
High quality bugs are key to training the next generation of language model based software engineering (SWE) agents. We introduce a novel method for synthetic generation of difficu…
TALES: Text Adventure Learning Environment Suite
Christopher Zhang Cui, Xingdi Yuan, Ziang Xiao +2
Reasoning is an essential skill to enable Large Language Models (LLMs) to interact with the world. As tasks become more complex, they demand increasingly sophisticated and diverse…
debug-gym: A Text-Based Environment for Interactive Debugging
Xingdi Yuan, Morgane M Moss, Charbel El Feghali +8
Large Language Models (LLMs) are increasingly relied upon for coding tasks, yet in most scenarios it is assumed that all relevant information can be either accessed in context or m…
Enhancing Agent Learning through World Dynamics Modeling
Zhiyuan Sun, Haochen Shi, Marc-Alexandre Côté +3
Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed…
Can Language Models Serve as Text-Based World Simulators?
Ruoyao Wang, Graham Todd, Ziang Xiao +4
Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language…
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Haochen Shi, Zhiyuan Sun, Xingdi Yuan +2
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural…
Language-guided Skill Learning with Temporal Variational Inference
Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin +4
We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajecto…
Policy Improvement using Language Feedback Models
Victor Zhong, Dipendra Misra, Xingdi Yuan +1
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instructio…