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Achin Kulshrestha

4 accepted papers

2025

Gatekeeper: Improving Model Cascades Through Confidence Tuning

NeurIPS 2025poster

Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to o…

Cited by 0SourceScholar
2025

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

ICML 2025poster

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM exp…

Cited by 6SourcePDFScholar
2025

Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos

CVPR 2025poster

Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring to light that current egocentric video question-answering datasets often include questions that can be answered using on…

2024

BRAVE: Broadening the visual encoding of vision-language models

ECCV 2024oral

"Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the limited capabilities of vision encoders, e.…