Papers by Surgan Jandial
Do GUI Grounders Truly Understand UI Elements? (2026.findings-eacl)
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| Challenge: | Existing grounding models and benchmarks are skewed toward web and mobile environments, neglecting desktop interfaces (especially windows). |
| Approach: | They propose a GUI Grounding Sensitivity Benchmark to assess UI grounding sensitivity to multiple descriptions of the same UI element. |
| Outcome: | The proposed model generates multiple valid instructions per UI element and develops nuanced validation methods to validate them. |
“Thinking” Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models (2024.emnlp-main)
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Shaz Furniturewala, Surgan Jandial, Abhinav Java, Pragyan Banerjee, Simra Shahid, Sumit Bhatia, Kokil Jaidka
| Challenge: | Existing debiasing techniques are typically training-based or require access to the model’s internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs. |
| Approach: | They propose a system-based iterative framework that uses System 2 thinking processes to induce logical, reflective, and critical text generation with single, multi-step, instruction, and role-based variants. |
| Outcome: | The proposed framework significantly improves over other frameworks demonstrating lower mean bias in the outputs with competitive performance on the downstream tasks. |
On the Fine-Grained Planning Abilities of VLM Web Agents (2025.findings-emnlp)
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| Challenge: | Vision-Language Models (VLMs) have shown promise as web agents, yet their planning has been overlooked. |
| Approach: | They propose to examine VLMs’ ability to understand temporal relationships within web contexts and assess plans of actions across diverse scenarios. |
| Outcome: | The proposed models exhibit limited performance in the above skills and are not reliable to function as web agents. |
S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models (2026.findings-acl)
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| Challenge: | Existing methods focus on localized reasoning with pre-specified image indices, bypassing the skills of global visual search and autonomous cross-image comparison. |
| Approach: | They propose a learning framework that constructs multi-image preference data across three hierarchical reasoning levels requiring an increasing level of capabilities. |
| Outcome: | The proposed approach maintains strong single-image reasoning performance while strengthening multi-image understanding capabilities. |