Papers by Divij Handa
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization (2025.acl-long)
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Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |
ThinkTuning: Instilling Cognitive Reflections without Distillation (2025.emnlp-main)
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| Challenge: | Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. |
| Approach: | They propose a GRPO-based interactive training approach that augments the rollouts of a student model with the guidance of . a teacher poses a problem, lets the student try an answer, then gives corrective feedback–enough to point the mind in the right direction and then show the correct solution. |
| Outcome: | The proposed method shows 3.69% improvement over zero-shot baselines and 2.08% and 3.99% improvement over the vanilla-GRPO baselines. |
Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents (2025.findings-naacl)
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| Challenge: | Recent research has leveraged Large Language Models to accelerate materials discovery and design. |
| Approach: | They propose a dataset that features goals, constraints, and methods for designing real-world applications and a method that emulates the process a materials scientist would use to evaluate a hypothesis critically. |
| Outcome: | The proposed method emulates the process a materials scientist would use to evaluate a hypothesis critically. |