End-to-end long-horizon reasoning with one Transformer model
subconscious.dev
End-to-end long-horizon reasoning with one Transformer model
1–4 of 4 posts
Re: End-to-end long-horizon reasoning with one Transformer model
#2- We build the Thread Inference Model (TIM) based on the transformer architecture, and its dedicated runtime TIMRUN.
- TIM + TIMRUN = Intelligent workflow generation, context engineering, and multi-hop tool use happens at the runtime level
- TIM + TIMRUN supports virtually unlimited reasoning enabled by context pruning, significantly improves the efficiency for long-horizon reasoning tasks
- Inference API is live at https://subconscious.dev/
- More details: https://github.com/subconscious-systems/TIMRUN
Re: End-to-end long-horizon reasoning with one Transformer model
#3- We build the Thread Inference Model (TIM) based on the transformer architecture, and its dedicated runtime TIMRUN. - TIM + TIMRUN = Intelligent workflow generation, context engineering, and multi-hop tool use happens at the runtime level - TIM + TIMRUN supports virtually unlimited reasoning enabled by context pruning, significantly improves the efficiency for long-horizon reasoning tasks - Inference API is live at…
Really cool work! I saw the “Selective Working Memory” section, are there hooks to swap in a custom retrieval store or memory layer, or is that all internal to TIM?
Thanks for sharing!
Re: End-to-end long-horizon reasoning with one Transformer model
#4- We build the Thread Inference Model (TIM) based on the transformer architecture, and its dedicated runtime TIMRUN. - TIM + TIMRUN = Intelligent workflow generation, context engineering, and multi-hop tool use happens at the runtime level - TIM + TIMRUN supports virtually unlimited reasoning enabled by context pruning, significantly improves the efficiency for long-horizon reasoning tasks - Inference API is live at…
I’m more than ready to stop my workflow being derailed by opening new chats—looking forward to LLM assistants that actually remember our conversation.