Portrait of Spandana Gella is unavailable

Spandana Gella

Collaborating researcher
Supervisor
Research Topics
Natural Language Processing

Publications

BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning
Ahmed Masry
Abhay Puri
Masoud Hashemi
Juan A. Rodriguez
Khyati Mahajan
Vikas Yadav
Sathwik Tejaswi Madhusudhan
Alexandre Piché
David Vazquez
Enamul Hoque
Perouz Taslakian
Sai Rajeswar
Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Juan A. Rodriguez
Haotian Zhang
Abhay Puri
Rishav Pramanik
Pascal Wichmann
Arnab Mondal
Mohammad Reza Samsami
Perouz Taslakian
Sai Rajeswar
David Vazquez
Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-lang… (see more)uage models (VLMs) have enabled high-quality SVG generation by framing the problem as a code generation task and leveraging large-scale pretraining. VLMs are particularly suitable for this task as they capture both global semantics and fine-grained visual patterns, while transferring knowledge across vision, natural language, and code domains. However, existing VLM approaches often struggle to produce faithful and efficient SVGs because they never observe the rendered images during training. Although differentiable rendering for autoregressive SVG code generation remains unavailable, rendered outputs can still be compared to original inputs, enabling evaluative feedback suitable for reinforcement learning (RL). We introduce RLRF(Reinforcement Learning from Rendering Feedback), an RL method that enhances SVG generation in autoregressive VLMs by leveraging feedback from rendered SVG outputs. Given an input image, the model generates SVG roll-outs that are rendered and compared to the original image to compute a reward. This visual fidelity feedback guides the model toward producing more accurate, efficient, and semantically coherent SVGs. RLRF significantly outperforms supervised fine-tuning, addressing common failure modes and enabling precise, high-quality SVG generation with strong structural understanding and generalization.
SafeArena: Evaluating the Safety of Autonomous Web Agents
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Xiangru Jian
Kevin Qinghong Lin
Juan A. Rodriguez
Montek Kalsi
M. Tamer Özsu
David Vazquez
Perouz Taslakian
Sai Rajeswar
Human Annotator
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
David Vazquez
Giuseppe Carenini
Issam Hadj Laradji
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Amirhossein Abaskohi
Amrutha Varshini Ramesh
Shailesh Nanisetty
David Vazquez
Giuseppe Carenini
Issam Hadj Laradji
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
David Vazquez
Sai Rajeswar
Perouz Taslakian
StarFlow: Generating Structured Workflow Outputs From Sketch Images
Patrice Bechard
Chao Wang
Amirhossein Abaskohi
Juan A. Rodriguez
David Vazquez
Sai Rajeswar
Perouz Taslakian
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction
Xiangru Jian
Kevin Qinghong Lin
Juan A. Rodriguez
Montek Kalsi
M. T. ¨Ozsu
David Vazquez
Perouz Taslakian
Sai Rajeswar
Human Annotator
SafeArena: Evaluating the Safety of Autonomous Web Agents
LLM-based agents are becoming increasingly proficient at solving web-based tasks. With this capability comes a greater risk of misuse for ma… (see more)licious purposes, such as posting misinformation in an online forum or selling illicit substances on a website. To evaluate these risks, we propose SafeArena, the first benchmark to focus on the deliberate misuse of web agents. SafeArena comprises 250 safe and 250 harmful tasks across four websites. We classify the harmful tasks into five harm categories -- misinformation, illegal activity, harassment, cybercrime, and social bias, designed to assess realistic misuses of web agents. We evaluate leading LLM-based web agents, including GPT-4o, Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To systematically assess their susceptibility to harmful tasks, we introduce the Agent Risk Assessment framework that categorizes agent behavior across four risk levels. We find agents are surprisingly compliant with malicious requests, with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests, respectively. Our findings highlight the urgent need for safety alignment procedures for web agents. Our benchmark is available here: https://safearena.github.io
SafeArena: Evaluating the Safety of Autonomous Web Agents
Ada Defne Tur
Esin DURMUS
Karolina Sta'nczak
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding
Ahmed Masry
Juan A. Rodriguez
Chao Wang
Akshay Kalkunte Suresh
Abhay Puri
Xiangru Jian
Pierre-Andre Noel
Sathwik Tejaswi Madhusudhan
Enamul Hoque
Issam Hadj Laradji
David Vazquez
Perouz Taslakian … (see 2 more)
Sai Rajeswar
Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges… (see more) on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), often produce out-of-distribution or noisy inputs, leading to misalignment between the modalities. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where scanned document images must be accurately mapped to their textual content. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods. We provide further analysis demonstrating improved vision-text feature alignment and robustness to noise.