Full recorded talk: https://
youtube.com/watch?v=pYrV9P
U0-R0
… Paper: https://
alphaxiv.org/abs/2103.02559
Example notebooks and documentation: https://
pymde.org/examples/
marimo: https://
github.com/marimo-team/
marimomolab: https://
molab.marimo.io/notebooks
Careers: https://
marimo.io/careers
Discord: http://
marimo.io/discord
@askalphaxiv
-
marimo: Interactive Notebooks and Data Visualization Tool
By
–
-
Repeating Prompts Boosts Non-Reasoning Model Performance
By
–
now trending on alphaXiv when using non-reasoning model, simply repeating your prompt improves performance. basically 1-10% absolute improvement, depending on prompt ordering or the model. And this applies to every model, it wouldn't create extra latency or generated
-
GLM-5 Open Weights Model Beats Gemini-3-Pro
By
–
GLM-5: A new SoTA open weights, that BEATS Gemini-3-Pro!? A big push from vibe coding -> agentic engineering, with it being able to plan, act, and iterate over long workflows, not just spit out code. GLM-5 scales to 744B params (40B active MoE) and targets 200k contexts, with
-
Claude Sonnet 4.6 Enables Advanced Research Paper Analysis
By
–
Introducing Claude Sonnet 4.6 for understanding research papers 🚀
— alphaXiv (@askalphaxiv) 18 février 2026
Highlight any section of a paper to ask questions and “@” other papers for quick context, comparisons, and benchmark references pic.twitter.com/60eq7dLtEzIntroducing Claude Sonnet 4.6 for understanding research papers Highlight any section of a paper to ask questions and “@” other papers for quick context, comparisons, and benchmark references
-
Gemini 3 and GPT 5 Now Available on alphaXiv
By
–
Now available in addition to Gemini 3 and GPT 5. Check out http://
alphaXiv.org! Alternatively check out our Chrome extension -
Experiential Reinforcement Learning: Teaching LLMs Self-Reflection
By
–
Making LLMs truly learn from its experience "Experiential Reinforcement Learning (ERL)" ERL makes an agent attempt -> get sparse feedback -> write a self-reflection -> retry All by distilling the improved retry back into the base policy so the correction sticks without needing
-
Ant Group Releases LingBot-World Open Source Video World Model
By
–
Following Google's Genie 3, Ant Group released an open source alternative: LingBot-World! This video trained world model can also be steered like a game (WASD + camera), with sub-1s latency at 16fps in its real-time setup, and demonstrates coherent rollouts up to 10 minutes,
-
Reinforcement Learning via Self-Distillation with Rich Feedback
By
–
"Reinforcement Learning via Self-Distillation" Current RLVR has a major flaw, where credit assignments and signals are sparse due to its binary feedback So this paper introduced a new paradigm called Reinforcement Learning with Rich Feedback (RLRF), using a new
-
alphaXiv Evolution: GitHub for AI Research Platform
By
–
Really enjoyed chatting with @swyx on the @latentspacepod at NeurIPS! Walked through our origin story, evolution from comments towards deep research and ML sandbox environments, and vision for the future of alphaXiv as the GitHub for AI research Check out the podcast below!
-
Self-Distillation Fine-Tuning: On-Policy Continual Learning from Expert Demonstrations
By
–
2026 is the year of continual learning And we are getting some amazing papers towards that This paper introduces Self-Distillation Fine-Tuning (SDFT): on-policy continual learning from expert demonstrations, with no explicit reward inference or engineering The trick here is: