BiCICLe frames bimanual robot control as a multi-agent leader-follower problem with Arms' Debate and an LLM judge, achieving up to 71.1% success on 13 TWIN benchmark tasks without fine-tuning.
NIPS ’22, Curran Associates Inc., Red Hook, NY, USA (2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Targeted prompting and system interventions enable local LLMs such as Llama 3.1 70B to exploit 83% of tested Linux privilege escalation vulnerabilities.
citing papers explorer
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Bimanual Robot Manipulation via Multi-Agent In-Context Learning
BiCICLe frames bimanual robot control as a multi-agent leader-follower problem with Arms' Debate and an LLM judge, achieving up to 71.1% success on 13 TWIN benchmark tasks without fine-tuning.
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Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents
Targeted prompting and system interventions enable local LLMs such as Llama 3.1 70B to exploit 83% of tested Linux privilege escalation vulnerabilities.