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Research

Independent benchmarks that test the limits of frontier models

At Deccan AI research, our mission is to deeply understand, evaluate, and advance the science of foundation models, enabling clearer insights for the AI community.

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// Collaborated and co-authored with ML engineers at

Introducing CaptionBench: A Stalemate on the Leaderboard Masks Distinct Failure Modes

In this video captioning benchmark, we evaluated six video captioning models by having trained reviewers rewrite and grade every caption against the footage, then checked what those corrections revealed against what the leaderboard scores showed.

Introducing VoxClash: A TTS leaderboard that reveals where exactly a model breaks down.

TTS outputs finally sound human. Yet, picking the right model for your language and use case is still a guessing game. VoxClash is our shot at fixing that.

Introducing the Bumblebee Dataset Report

This report of our egocentric video corpus focuses on the properties that help characterize a corpus for embodied learning.

From Compliance to Foresight: Benchmarking Deep Research Agents

The study focuses on examining the “Proactive” research capabilities of the SOTA models using novel rubrics

IF Benchmark: Constraint Choice Predicts Failure Rate Better Than Model Choice

This study evaluates instruction-following reliability in frontier models under controlled, multi-constraint conditions.

Image Generation Study: Deeper Assessment of Image SOTA Models

We introduce a new framework for end-to-end evaluation of next-gen image generation models

WebApp Study: Evaluating AI-Native Web Application Builders

A human evaluation benchmark measuring how well no-code platforms handle real application development tasks.

Anthar Study: Evaluating AI Coding Agents Beyond Benchmarks

A human evaluation benchmark measuring how well autonomous coding agents perform in real development workflows and security-critical scenarios.

This doesn’t have to end here

Accuracy is Intelligence