2 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
SoK: Honeypots & LLMs, More Than the Sum of Their Parts?
Robert A. Bridges, Thomas R. Mitchell, Mauricio Muñoz +1
The advent of Large Language Models (LLMs) promised to resolve the long-standing paradox in honeypot design: achieving high-fidelity deception with low operational risk. Since late…
Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation
Jackson Hassell, Dan Zhang, Hannah Kim +2
We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without parameter updates. While conventi…
Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling
Seiji Maekawa, Jackson Hassell, Pouya Pezeshkpour +2
Existing benchmarks for tool-augmented language models (TaLMs) lack fine-grained control over task difficulty and remain vulnerable to data contamination. We present FuncBenchGen,…