3 citations · 3 across the 4 of their papers we have counts for
5 papers
Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents
Nijesh Upreti, Chris Sypherd, Vaishak Belle
Agentic large language model (LLM) systems are commonly implemented as an LLM in a loop with Planning, Memory, Tools, and Control Flow. This application-focused view connects agent…
HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation
Weizhi Tang, Yixuan Li, Chris Sypherd +2
Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs.…
Incorporating Token Usage into Prompting Strategy Evaluation
Chris Sypherd, Sergei Petrov, Sonny George +1
In recent years, large language models have demonstrated remarkable performance across diverse tasks. However, their task effectiveness is heavily dependent on the prompting strate…
Practical Considerations for Agentic LLM Systems
Chris Sypherd, Vaishak Belle
As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstr…
Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction
Sonny George, Chris Sypherd, Dylan Cashman
Large language model (LLM) agents show promise in an increasing number of domains. In many proposed applications, it is expected that the agent reasons over accumulated experience…