17 citations · 63 across the 35 of their papers we have counts for
35 papers
VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
CPO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models
Swapnanil Mukherjee, Agyeya Negi, Tanuja Ganu +1
Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cro…
CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement
Mercy Prasanna Ranjit, Anirban Porya, Sathvik Joel +8
A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive th…
Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models
Raj Jaiswal, Dhruv Jain, Rishabh Dhawan +4
Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited domain knowledge, hallucina…
Obey, Diverge, Collapse: Blind Obedience to Incorrect Instructions Drives Code LLMs to Irrecoverable Code Semantic Collapse
Raj Jaiswal, Anany Singh Divy, Savar Bhasin +3
Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that evaluates them assumes the ins…
SAE++: Cascaded Sparse Autoencoders Learn Multi-Level Visual Concepts in Multimodal LLMs
Yusong Zhao, Hengyi Wang, Tanuja Ganu +2
Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representations remain difficult to interpret. Spa…
A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs
Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian +1
Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradig…
Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs
Rohit Sinha, Aditya Kanade, Sai Srinivas Kancheti +2
Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains…
Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization
Sai Srinivas Kancheti, Aditya Kanade, Rohit Sinha +2
Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe tha…
Exposing Weak Links in Multi-Agent Systems under Adversarial Prompting
Nirmit Arora, Sathvik Joel, Ishan Kavathekar +6
LLM-based agents are increasingly deployed in multi-agent systems (MAS). As these systems move toward real-world applications, their security becomes paramount. Existing research l…
TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems
Ishan Kavathekar, Hemang Jain, Ameya Rathod +2
Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities, leading to their widespread adopt…
GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models
Zhangyu Wang, Nemin Wu, Qian Cao +8
The widespread adoption of AI models, especially foundation models (FMs), has made a profound impact on numerous domains. However, it also raises significant ethical concerns, incl…
Shiksha Copilot: Teacher-AI Collaboration for Curating and Customizing Lesson Plans in Low-Resource Schools
Deepak Varuvel Dennison, Bakhtawar Ahtisham, Kavyansh Chourasia +6
This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise th…
Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMs
Aditya Kanade, Tanuja Ganu
Multimodal Large Language Models (MLLMs) show reasoning promise, yet their visual perception is a critical bottleneck. Strikingly, MLLMs can produce correct answers even while misi…
EnCortex: A General, Extensible and Scalable Framework for Decision Management in New-age Energy Systems
Millend Roy, Vaibhav Balloli, Anupam Sobti +4
With increased global warming, there has been a significant emphasis to replace fossil fuel-dependent energy sources with clean, renewable sources. These new-age energy systems are…
RadPhi-3: Small Language Models for Radiology
Mercy Ranjit, Shaury Srivastav, Tanuja Ganu
LLM based copilot assistants are useful in everyday tasks. There is a proliferation in the exploration of AI assistant use cases to support radiology workflows in a reliable manner…
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
Nemin Wu, Qian Cao, Zhangyu Wang +12
Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, network…
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
Hengyi Wang, Haizhou Shi, Shiwei Tan +6
Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comp…
MMCTAgent: Multi-modal Critical Thinking Agent Framework for Complex Visual Reasoning
Somnath Kumar, Yash Gadhia, Tanuja Ganu +1
Recent advancements in Multi-modal Large Language Models (MLLMs) have significantly improved their performance in tasks combining vision and language. However, challenges persist i…
PromptWizard: Task-Aware Prompt Optimization Framework
Eshaan Agarwal, Joykirat Singh, Vivek Dani +3
Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering…
BEnQA: A Question Answering and Reasoning Benchmark for Bengali and English
Sheikh Shafayat, H M Quamran Hasan, Minhajur Rahman Chowdhury Mahim +3
In this study, we introduce BEnQA, a dataset comprising parallel Bengali and English exam questions for middle and high school levels in Bangladesh. Our dataset consists of approxi…
RAD-PHI2: Instruction Tuning PHI-2 for Radiology
Mercy Ranjit, Gopinath Ganapathy, Shaury Srivastav +2
Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, p…
Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering
Pragya Srivastava, Manuj Malik, Vivek Gupta +2
Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstruc…