Ujjwal Tyagi

You should also generate a medical datasets of GLM 4.7 and non reasoning data of GPT 5.2 with 1k samples in structured detail output, or we can provide you the OpenAI tools for free....as if you can work for our team..so like there is no need to spend your own money on API Cost, my username is ujjwal_tyagi.shirova on discord, so you can contact me there

So like I want to train my LLMs on all of your medical datasets, does OpenMed/Medical-Reasoning-SFT-Mega covers all of those medical datasets that are been separated for the particular model like qwen, nemotron, baichuan, gpt oss? Would you like to join our team? We are looking for people just like you!

I love it, that's is really very helpful

Great!

Great to see that

It's amazing and Congrats!

I love guardrails and AI safety in agentic systems, I want to learn more from you regarding this!

Honestly, this way of looking at semantic memory just makes a lot of sense to me. It's way more useful than just keeping raw logs or simple embeddings.

I really like the SIM vs. SIS split. It feels exactly like how our own short-term and long-term memory works. It seems super handy for things like keeping track of agent memory, tool use, and just making sure we can actually check why a decision was made. Also, the focus on backing_refs and jurisdiction-aware semantics is a big deal compared to the usual RAG stuff you see everywhere.

From where I stand, this looks really good for:

• Agentic workflows
• Long-term planning
• Safety checks (I love this)
• Explaining why things happened

The only part that feels a bit light is how it handles fuzzy semantics (like updating beliefs or dealing with uncertainty) and how the system learns and changes its own mind over time.

It would be cool to see this expanded with:

• Native support for belief revision / contradiction handling
• Tighter integration with embedding-space retrieval (hybrid semantic + vector recall)
• Explicit patterns for LLM-generated semantic units vs. sensor-derived ones

This really feels like the missing piece connecting LLM thinking to real-world accountability. Super excited to see how it grows