Papers by Vibhav Vineet
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
| Approach: | They propose to evaluate LLMs’ abilities to detect and correct reasoning mistakes by using rule-based methods and smaller language models. |
| Outcome: | The proposed model outperforms existing models such as GPT-4o and GPT4 in both accuracy and accuracy, but lacks data contamination and memorization concerns. |
Navigating Hallucinations for Reasoning of Unintentional Activities (2024.findings-emnlp)
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| Challenge: | Existing models of intentionality recognition struggle to understand the reasoning behind unintentional actions. |
| Approach: | They propose a novel prompting technique which allows the model to navigate through hallucinated thoughts to achieve better reasoning. |
| Outcome: | The proposed prompting technique outperforms standard prompting while minimizing hallucinations. |
RiTTA: Modeling Event Relations in Text-to-Audio Generation (2025.emnlp-main)
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| Challenge: | Existing text-to-audio (TTA) generation methods have not explored audio event relation modeling, nor proposed any new framework to enhance this capability. |
| Approach: | They propose a comprehensive relation corpus covering all potential relations in real-world scenarios and a new audio event corpus encompassing commonly heard audios. |
| Outcome: | The proposed framework improves existing models’ relation modeling capability with negligible extra parameters. |
Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames (2025.emnlp-main)
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Sahithya Ravi, Gabriel Herbert Sarch, Vibhav Vineet, Andrew D Wilson, Balasaravanan Thoravi Kumaravel
| Challenge: | Disjoint-3DQA evaluates the spatial reasoning ability of embodied AI assistants based on egocentric video . it aims to catalyze future research at the intersection of vision, language, and embodie . |
| Approach: | They propose a generative QA benchmark that evaluates the ability of embodied AI assistants to integrate spatial cues across time by asking object pairs that are not co-visible in the same frame. |
| Outcome: | The proposed benchmark compares seven state-of-the-art VLMs and finds that they lag behind human performance by 28%, with steeper declines as the temporal gap widens. |
Grounding Task Assistance with Multimodal Cues from a Single Demonstration (2025.findings-acl)
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Gabriel Herbert Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi, Vibhav Vineet, Andrew D Wilson
| Challenge: | RGB video often fails to capture fine-grained contextual cues such as intent, safety-critical environmental factors, and subtle preferences embedded in human behavior. |
| Approach: | They propose a framework that integrates eye gaze and speech cues to improve conversational agents for task assistance by integrating eye gaze with speech cuests. |
| Outcome: | The proposed framework captures fine-grained intent and user-specific cues, enabling richer contextual grounding for visual question answering. |
Image Retrieval from Contextual Descriptions (2022.acl-long)
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| Challenge: | a new multimodal challenge challenges vision-and-language models to integrate context into their representations. |
| Approach: | They propose a multimodal challenge to integrate context into vision-and-language models . they benchmark several state-of-the-art models using cross-encoders and bi-encodings . |
| Outcome: | The proposed model lags behind human models on imageCoDe, compared with human models. |