Reasoning models often talk too much, and those extra words can actually make them more wrong.
CharacterFlywheel is a step‑by‑step loop that steadily improves chatty AI characters by learning from real conversations on Instagram, WhatsApp, and Messenger.
MobilityBench is a big, carefully built test that checks how well AI helpers can plan real-world routes using natural language and map tools.
ROCKET is a fast, training-free way to shrink big AI models while keeping most of their smarts.
The paper finds a hidden symmetry inside GRPO’s advantage calculation that accidentally stops models from exploring new good answers and from paying the right attention to easy versus hard problems at the right times.
When you tune the learning rate carefully, plain old LoRA fine-tuning works about as well as fancy new versions.
Giving large language models a few good examples and step-by-step instructions can make them much better at spotting feelings in text.
This paper builds MFMD-Scen, a big test to see how AI changes its truth/false judgments about the same money-related claim when the situation around it changes.
A digital twin is a living computer copy of a real thing (like a bridge, a heart, or a factory) that stays in sync with sensors and helps us predict, fix, and improve the real thing.
Large language models often sound confident even when they are wrong, and existing ways to catch mistakes are slow or not very accurate.
Reinforcement learning (RL) can make big language models smarter, but off-policy training often pushes updates too far from the “safe zone,” causing unstable learning.
BEAVER is a new way to check, with guaranteed certainty, how likely a language model is to give answers that obey important rules.