2 citations · 3 across the 6 of their papers we have counts for
6 papers
Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
Aradhye Agarwal, Gurdit Siyan, Yash Pandya +3
Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, su…
The Art of Scaling Test-Time Compute for Large Language Models
Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty
Test-time scaling (TTS) -- the dynamic allocation of compute during inference -- is a promising direction for improving reasoning in large language models (LLMs). However, a system…
First Finish Search: Efficient Test-Time Scaling in Large Language Models
Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty
Test-time scaling (TTS), which involves dynamic allocation of compute during inference, offers a promising way to improve reasoning in large language models. While existing TTS met…
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language Models
Aradhye Agarwal, Suhas K Ramesh, Ayan Sengupta +1
Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. Selective PEFT, a class of parameter-efficient fine-tuning (PEFT) methodo…
The Third Monocular Depth Estimation Challenge
Jaime Spencer, Fabio Tosi, Matteo Poggi +38
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation
Suraj Patni, Aradhye Agarwal, Chetan Arora
In the absence of parallax cues, a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is at…