Papers with agent architectures

2 papers
Towards Objectively Benchmarking Social Intelligence of Language Agents at the Action Level (2024.findings-acl)

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Challenge: evaluative findings highlight that the STSS benchmark is challenging for state-of-the-art language agents.
Approach: They propose a social task in sandbox simulation benchmark that assesses language agents objectively at the action level by scrutinizing goal achievements within the multi-agent simulation.
Outcome: The proposed social task-in-sandbox simulation is a language-level benchmark . the proposed benchmark effectively discriminates between distinct language agents .
Inefficiencies of Meta Agents for Agent Design (2025.findings-emnlp)

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Challenge: Recent work has automated the design of agentic systems using meta-agents . authors examine three key challenges in a common class of meta-gents.
Approach: They examine how meta-agents learn across iterations and show performance improves with evolutionary approach.
Outcome: The proposed meta-agents perform worse when iterating on multiple agents than human-designed agents.

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