1 citations · 1 across the 6 of their papers we have counts for
7 papers
Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices
Avyay Sadhu, Alvaro Velasquez, Lekai Chen
Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on such devices are unreliable…
Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents
Le Chen, Zishen Wan, Baixi Sun +6
Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles and exhibit different size, re…
CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action
Lekai Chen, Alvaro Velasquez, Ashutosh Trivedi
Natural-language tasking of embodied agents is rarely just goal specification: users also impose constraints that must persist while the world changes. Code-generating LLM agents c…
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning
Chih-Hsuan Yang, Jingyan Jiang, Vikram Vasudevan +7
Many math- and science-oriented agent systems use hierarchical designs with specialized reviewer roles, assuming that a dedicated review stage should help turn wrong candidates int…
Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
Chih-Hsuan, Yang, Tanwi Mallick +5
Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation…
Evaluating the Safety and Skill Reasoning of Large Reasoning Models Under Compute Constraints
Adarsha Balaji, Le Chen, Rajeev Thakur +2
Test-time compute scaling has demonstrated the ability to improve the performance of reasoning language models by generating longer chain-of-thought (CoT) sequences. However, this…
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning
Lekai Chen, Ashutosh Trivedi, Alvaro Velasquez
The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning. This paper introduces the probabilistic Min…