Papers with role-specialized agents
Debate, Deliberate, Decide (D3): A Cost-Aware Adversarial Framework for Reliable and Interpretable LLM Evaluation (2026.eacl-long)
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| Challenge: | Existing evaluation tools for Large Language Models (LLMs) are inconsistency, bias, and lack of transparent decision criteria. |
| Approach: | They propose a cost-aware, adversarial multi-agent framework that orchestrates structured debate among role-specialized agents to produce reliable and interpretable evaluations. |
| Outcome: | The proposed framework orchestrates structured debate among role-specialized agents to produce reliable and interpretable evaluations. |
MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) when retrieved contexts are noisy, incomplete, or heterogeneous, a single generation process often struggles to reconcile evidence effectively. |
| Approach: | They propose a multi-agent synthesis approach to retrieval-augmented generation that structures evidence processing into multiple role-specialized agents. |
| Outcome: | Experiments on four benchmarks show that MASS-RAG consistently improves performance over strong RAG baselines. |
Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Existing Chain-of-Thought (CoT) approaches lack intrinsic correction mechanisms, rendering them vulnerable to error propagation. |
| Approach: | They propose a multi-agent framework that enforces diagnostic rigor through adversarial dialectics. |
| Outcome: | Empirical evaluations show that the proposed framework improves explanation faithfulness and mitigates hallucinations. |