53 citations · 187 across the 29 of their papers we have counts for
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cs.AI2024
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
Talor Abramovich, Meet Udeshi, Minghao Shao +13
Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited.…
cs.AI2024★ 7 cited
-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
Shunyu Yao, Noah Shinn, Pedram Razavi +1
Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real…