Appen Research

@AppenResearch

Human data for frontier AI. Research and insights from Appen.

Joined May 2026

211

Appen Research
@AppenResearch
5h
Pass/Fail isn’t enough for Physical AI. A robot can complete a household task while taking an inefficient trajectory, making unstable grasps, or nearly dropping the object. Those behaviors matter and binary benchmarks don’t capture them. See how Appen partnered with a frontier
GIF
Appen Research
@AppenResearch
Aug 27
VLM judges can be right about the details and still get the overall judgment wrong. If the important moment never gets retrieved, better reasoning won’t fix it. And even when a judge catches a real omission, the bigger question is whether it actually mattered. New from
Appen Research
@AppenResearch
Aug 26
Scientific reasoning isn’t a data volume problem. It demands expert knowledge density. That’s why the Appen Research team built 35+ Academic STEM Off-the-Shelf Datasets across adjacent expert domains, expert-authored corpora sourced from peer-reviewed journals and professional
Appen Research
@AppenResearch
Aug 26
This is exactly what we’re seeing in practice. Expert analysis of where and why agents fail can become the feedback that improves how they reason the next time around. Really interesting paper from Google showing this loop in action.
marfin
@marfinxx
Aug 25
This Google AI research paper is f*cking brilliant A new research paper proves that distilling high-level strategies from failed and successful trajectories transforms static LLMs into self-improving agents ReasoningBank memory extraction, memory-aware test-time scaling
Appen Research
@AppenResearch
Aug 25
An LLM judge is only as reliable as the language it’s judging in. The MMLU-ProX benchmark, which evaluated 36 frontier models across 29 languages, found performance gaps of up to 24.3% between high and low-resource languages. Appen's Multilingual LLM-as-a-Judge Managed Service