Multi-agent LLM simulations with trait-conditioned agents and a reinforcement-learning orchestrator show heterogeneous teams and dynamic trait selection outperform static configurations in simulated legal argumentation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
PeReGrINE is a graph-based benchmark that restructures Amazon Reviews 2023 with temporal cutoffs and introduces dissonance analysis to measure how well retrieval-conditioned models match user style and product consensus.
Emotional perturbations induced via activation steering systematically alter strategic choices made by small language model agents in cooperative and competitive game templates, yet the resulting behaviors remain unstable and only partially aligned with human patterns.
citing papers explorer
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Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation
Multi-agent LLM simulations with trait-conditioned agents and a reinforcement-learning orchestrator show heterogeneous teams and dynamic trait selection outperform static configurations in simulated legal argumentation.
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Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
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PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context
PeReGrINE is a graph-based benchmark that restructures Amazon Reviews 2023 with temporal cutoffs and introduces dissonance analysis to measure how well retrieval-conditioned models match user style and product consensus.
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On Emotion-Sensitive Decision Making of Small Language Model Agents
Emotional perturbations induced via activation steering systematically alter strategic choices made by small language model agents in cooperative and competitive game templates, yet the resulting behaviors remain unstable and only partially aligned with human patterns.