Here you go:
Google Play: https://
play.google.com/store/apps/det
ails?id=ai.qwenlm.chat.android
…
AppStore: https://
apps.apple.com/app/id67437784
42
…
@jiqizhixin
-
Qwen LM Chat App Now Available on Google Play and App Store
By
–
-
Meta’s Token-Shuffle Advances Autoregressive Image Generation
By
–
Even if Yann LeCun isn’t a fan of autoregressive models, Meta’s still pushing out some seriously impressive research using them.
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models
Paper: https://
arxiv.org/pdf/2504.17789 -
Paper2Code: Automated ML Paper to Runnable Code Framework
By
–
Paper2Code, from KAIST, is a multi-agent LLM framework that turns ML papers into runnable code repos—automatically! Bridging the gap between research and implementation, one agent at a time.
Paper: https://
arxiv.org/pdf/2504.17192 -
LLM-as-Judge Won’t Save Product: Fixing Process Will
By
–
An LLM‑as‑Judge Won't Save The Product—Fixing Your Process Will by @eugeneyan
-
Tina: Open Source AI Project with Training Logs and Models
By
–
Notion Blog: https://
shangshangwang.notion.site/tina
Code: https://
github.com/shangshang-wan
g/Tina
…
Training Logs: https://
wandb.ai/upup-ashton-wa
ng-usc/Tina
…
Models: https://
huggingface.co/Tina-Yi -
Tiny Reasoning Models via LoRA Achieve Strong AIME Performance
By
–
Apparently, you can build tiny reasoning models via LoRA.
Tina is a family of 1.5B models trained using LoRA-based RL—cost-efficient and performant.
The best Tina hits >20% gain in reasoning performance and 43% Pass@1 on AIME24,
with just $9 in post-training + eval cost. -
Survey of AI Agent Protocols: Standards and Interoperability
By
–
A Survey of AI Agent Protocols https://
arxiv.org/pdf/2504.16736 -
TTRL: Test-Time Reinforcement Learning for LLM Self-Improvement
By
–
TTRL: Test-Time Reinforcement Learning
A new approach that lets LLMs evolve on reasoning tasks without explicit labels. TTRL taps into the priors of pre-trained models, enabling self-improvement through data alone. Paper: https://
arxiv.org/pdf/2504.16084
Code: https://
github.com/PRIME-RL/TTRL
