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This survey explains how large language models (LLMs) can clean, connect, and enrich messy data so it’s ready for real apps like dashboards, fraud detection, and training AI.

This paper builds SocialVeil, a testing world where AI chat agents must talk to each other even when communication is messy, not perfect.

This paper studies how AI agents get better while they are working, not just whether they finish the job.

World Craft lets anyone turn a short text description into a playable, visual game world without coding.

OpenRT is a big, open-source test bench that safely stress-tests AI models that handle both text and images.

The paper shows that judging vector search only by distance-based recall and speed can be very misleading for real tasks.

LiveMedBench is a new, always-updating test for medical AIs that keeps test questions safely separated from training data to avoid cheating by memorization.

This paper explains how AI agents remember things across long conversations and why many current tests don’t truly measure that memory.

The paper fixes a big problem in long video generation: models either forget what happened or slowly drift off-topic over time.

LongCLI-Bench is a new test that checks how well AI coding agents can handle long, realistic software projects in the command line, not just tiny coding puzzles.

This paper shows that letting an AI search many places at the same time (in parallel) can beat making it think in long, slow chains.