Papers by Ashutosh Bajpai
SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving (2026.findings-eacl)
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| Challenge: | Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. |
| Approach: | They propose a Spatial Comprehension-Infused Symbolic Reasoning Framework to integrate spatial representations into structured symbolic reasoning chains. |
| Outcome: | The proposed framework outperforms existing models in vision-intensive mathematical problems. |
Temporally Consistent Factuality Probing for Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are used as an alternative knowledge base for many tasks. |
| Approach: | They propose a temporally consistent factuality probe task that extends the consistency probe in the temporal dimension. |
| Outcome: | The proposed task extends the definitions of existing metrics to represent consistent factuality across temporal dimension. |
Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References? (2025.emnlp-main)
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| Challenge: | Existing efforts to ensure temporal consistency in large language models are lacking in time-sensitive fields . temporal reasoning is essential for time- sensitive fields such as finance and healthcare . a new benchmark aims to improve temporal referent consistency of LLMs . |
| Approach: | They propose a temporal referential consistency benchmark with a resource TEMP-ReCon to assess LLMs across temporal references. |
| Outcome: | The proposed model improves LLMs' temporal consistency by comparing them to baseline models. |
Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception (2026.findings-acl)
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| Challenge: | Small Vision-Language Models (SVLMs) suffer from visual brittleness and poor tool orchestration. |
| Approach: | They propose a supervision-free framework that bootstraps agentic capabilities via Coldstart Reinforcement Learning for SVLMs. |
| Outcome: | The proposed framework improves task accuracy and tool efficiency by 5% and 9%. |