kk58

Yesterday's harness becomes tomorrow's model capabilities. Not sure about harnesses sustainable moat

Im curious I've seen very different results for automation of business processes. Could you share an example of what you're dealing with?

Does this work on mac

What's the angle you're thinking?

Reasoning models aren't really reasoners, its basically neural style transfer protocol where you force a model "decoder" to emit tokens in a style that appears to be Reasoning like a deductive thinking.

Curious what's your questions that's really unanswered?

Using LLMs as Optimizers: A new population-based method called LEO that leverages large language models for numerical optimization tasks like nozzle shape and windfarm layout design. Shows comparable results to traditional methods while highlighting unique challenges of LLM-based optimization

classic NN takes a vector of data through layers to make a prediction. Backprop adjusts network weights till predictions are right. These network weights form a vector, and training changes this vector till it hits values that mean "trained network".

Neural ODE reframes this: instead of focusing on the weights, focus on how they change. It sees training as finding a path from untrained to trained state. At each step, it uses ODE solvers to compute the next state, continuing for N steps till it reaches values matching training data. This gives you the solution for the trained network.

Very curious, we are getting good results with PiNN and operators, what's your domain?

Doesn't even work. 500 error

LLM are fundamentally stateless. The exoskeleton of agents is essentially a work around to enable a state that resides externally to a LLM. This is required in some manner for creating an agent. Task oriented agents require reasoning and planning but the nature of its wildly different compared to the kind of behaviour that is observed in the interactive simulacra paper. Your ideas have merits but I feel it needs to have some further qualifications

The hallucination may not be a bug but a form regularisation that will helps sample behaviour and that itself might be of great use as it might act as a proxy for focus groups