Author name cluster

Avinash Anand

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5 papers

2 author rows

AAAI Conference 2026 Short Paper

BRI-MH: Behavioral Risk Index for Mental Health — An Interpretable Multimodal LLM-Augmented Framework (Student Abstract)

Mahi Mann
Avinash Anand
Rajiv Ratn Shah

Mental health monitoring faces challenges from fragmented data and opaque risk scores. We present BRI-MH, an in- terpretable multimodal framework combining behavioral sig- nals with cognitive features from large language models to produce a weekly Behavioral Risk Index. Unlike prior work with isolated or black-box scores, BRI-MH offers transpar- ent, actionable insights and links continuous monitoring to adaptive feedback and therapeutic support, bridging digital phenotyping and clinical care.

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AAAI Conference 2026 Short Paper

IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)

Vashu Chauhan
Avinash Anand
Manisha Luthra
Uelison Jean Lopes dos Santos
Carsten Binnig
Rajiv Ratn Shah

Traffic accidents pose a significant societal challenge, with many fatalities being avoidable through timely emergency response. We introduce IMPACT (Integrated Multimodal Pipeline for Rapid Accident Causality Tracking), a scalable AI framework designed for autonomous, rapid traffic incident analysis using existing urban CCTV infrastructure. IMPACT combines a low-latency CPU-based vision module for real-time key-frame filtering (24 FPS) with the causal reasoning capabilities of MLLMs, reducing costly MLLM calls by over 92% compared to naive sparse sampling. We further present TRACE10K, a dataset featuring three-tier textual annotations that describe accident dynamics at the frame-sequence level.

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AAAI Conference 2026 Short Paper

When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student Abstract)

Ravada Satyadev
Aditya Ganesh Kumar
Avinash Anand
Rajiv Ratn Shah
Zhengkui Wang
Mukesh Prasad

Bias in Large Language Models (LLMs) is increasingly addressed through fairness-oriented techniques. However, in some cases, these approaches may inadvertently remove genuine cultural differences between groups, leading to “over-normalization” or models losing important socio-cultural distinctions. In this work, we introduce OverNormEval, a benchmark designed to detect when an LLM exhibits such over-normalization. We further explore the use of Direct Preference Optimization (DPO) to mitigate over-normalization.

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AAAI Conference 2025 Conference Paper

Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi and English

Avinash Anand
Kritarth Prasad
Chhavi Kirtani
Ashwin R Nair
Manvendra Kumar Nema
Raj Jaiswal
Rajiv Ratn Shah

Large Language Models (LLMs) excel in linguistic tasks but struggle with mathematical reasoning, particularly in non- English languages like Hindi. This research aims to en- hance the mathematical reasoning skills of smaller, resource- efficient open-source LLMs in both Hindi and English. We evaluate models like OpenHathi 7B, LLaMA-2 7B, Wizard- Math 7B, Mistral 7B, LLeMMa 7B, MAmmoTH 7B, Gemini Pro, and GPT-4 using zero-shot, few-shot chain-of-thought (CoT) methods, and supervised fine-tuning. Our approach in- corporates curriculum learning, progressively training mod- els on increasingly difficult problems, a novel Decompo- sition Strategy to simplify complex arithmetic operations, and a Structured Solution Design that divides solutions into phases. Our experiments result in notable performance en- hancements. WizardMath 7B exceeds Gemini’s accuracy on English datasets by +6% and matches Gemini’s performance on Hindi datasets. Adopting a bilingual approach that com- bines English and Hindi samples achieves results comparable to individual language models, demonstrating the capability to learn mathematical reasoning in both languages. This re- search highlights the potential for improving mathematical reasoning in open-source LLMs.

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ECAI Conference 2024 Conference Paper

Unveiling Learner Dynamics: The ECLIPSE Dataset and NeuralGaze Framework for Prolonged Engagement Assessment in Online Learning

Avinash Anand
Avni Mittal
Laavanaya Dhawan
Mahisha Ramesh
Juhi Krishnamurthy
Naman Lal
Raj Jaiswal
Pijush Bhuyan

Understanding student engagement in online education is crucial for optimizing learning outcomes. This paper introduces ECLIPSE dataset (Extended Classroom Learning Insights via Prolonged Student Engagement), comprising 10, 110 annotated images from a 55-minutes, 30-minutes and 20-minutes online lecture. Annotations include four affective states: engagement, boredom, confusion, and frustration. ECLIPSE enables the investigation of learner attention dynamics over extended periods, overcoming the limitations of short-duration datasets. We establish benchmarks for ECLIPSE using models such as EfficientNet, Vision Transformer, Residual Attention Network, and GLAMOR-Net. We propose NeuralGaze, a novel framework integrating Neural Cellular Automata (NCA) with self-attention mechanisms, demonstrating superior accuracy in engagement level assessment compared to basic single-frame models. Furthermore, we introduce CG-SwT, a content-guided Swin Transformer model, which significantly outperforms the baseline ViT model on the ECLIPSE dataset (with F1-score improvements of 21. 12%, 12. 5%, 16. 77%, and 15. 41% for engagement, boredom, frustration, and confusion respectively). Our methods surpass existing single-frame engagement prediction baselines for both EngageNet and DAiSEE datasets by significant margins (7. 4% and 6. 2%, respectively). The code and dataset will be made publicly available.

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