Papers by Avinash Anand
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size (2026.findings-acl)
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Dikshant Kukreja, Kshitij Sah, Gautam Gupta, Avinash Anand, Rajiv Ratn Shah, Zhengkui Wang, Aik Beng Ng, Erik Cambria
| Challenge: | Larger language models become better and worse at handling contextual information . et al. (2017) formalized contextual entrainment as a tendency to favor tokens in context . |
| Approach: | They formalize the first scaling laws for contextual entrainment . they find large models are four times more resistant to counterfactual misinformation . |
| Outcome: | The largest models are four times more resistant to counterfactual misinformation than the smallest, but twice as prone to copying arbitrary tokens. |
IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning (2026.acl-long)
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Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand, Chhavi Kirtani, Erik Cambria, Zhengchen Zhang, Zhengkui Wang, Timothy Liu, Aik Beng Ng, Simon See, Rajiv Ratn Shah
| Challenge: | Curriculum learning fails to generate consistent step-by-step reasoning in multilingual and low-resource settings. |
| Approach: | They propose a framework that combines supervised fine-tuning with reverse curriculum reinforcement learning to generate consistent step-by-step reasoning. |
| Outcome: | The proposed framework outperforms single-axis benchmarks and multilingual test sets on math reasoning tasks and in high-resource languages. |