Papers by Chaithanya Bandi
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing (2026.acl-long)
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Michael Lan, Narmeen Fatimah Oozeer, Chaithanya Bandi, Philip Quirke, Austin Meek, Fazl Barez, Amir Abdullah
| Challenge: | a recent paper found conflicting conclusions for the same behavior in a neural network . authors propose auditing MI itself is essential for its application in AI safety, industry, and governance . |
| Approach: | They propose to develop a system that can audit experiments to ensure validity . authors propose to generalize good practices found on platform into expert-verified guidelines . |
| Outcome: | a new review system could be developed that can be standardized and audited . authors argue that auditing MI is essential for its application in AI safety, industry, and governance . |
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. |