Papers by Zhastay Yeltay
More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are powerful but weak when inputs are perturbed. |
| Approach: | They evaluate LLMs that are more powerful than single LLM in math question answering . they use a unified sampling-and-voting framework to evaluate their models . |
| Outcome: | The proposed models show that collaboration between agents improves accuracy and clean accuracy even with a large number of agents. |