nithin

nithin12342

nithin12342
nithin-gurusamy-68a76a324

AI & ML interests

Aspiring Software Developer | 🌐 Full-Stack Explorer | 🤖 AI Engineer-in-Progress Passionate about building scalable apps, automating workflows, and craft

Recent Activity

upvoted a paper about 9 hours ago

LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training

updated a collection about 20 hours ago

Ai papper for efficient ai model

upvoted a paper about 20 hours ago

Unified Neural Scaling Laws

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nithin

AI & ML interests

Recent Activity

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nithin12342 's collections 6

NeuROK: Generative 4D Neural Object Kinematics

The Physical AI Inference Gap in Batch-1 LLM Decode

NOVAglow646/Monet-SFT-125K

Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning

Qwen3.5-Omni Technical Report

REFRAG: Rethinking RAG based Decoding

OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation

Code as Agent Harness

REFRAG: Rethinking RAG based Decoding

OpenVision 3: A Family of Unified Visual Encoder for Both Understanding and Generation

Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model

Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems

NeuROK: Generative 4D Neural Object Kinematics

The Physical AI Inference Gap in Batch-1 LLM Decode

Code as Agent Harness

NOVAglow646/Monet-SFT-125K

REFRAG: Rethinking RAG based Decoding

Cut Your Losses! Learning to Prune Paths Early for Efficient Parallel Reasoning

Qwen3.5-Omni Technical Report

REFRAG: Rethinking RAG based Decoding

OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation

OpenVision 3: A Family of Unified Visual Encoder for Both Understanding and Generation

Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model

Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

NAACL: Noise-AwAre Verbal Confidence Calibration for LLMs in RAG Systems