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What's the angle you're thinking?
Readit Newskk58 commented on Build real-time knowledge graph for documents with LLM cocoindex.io/blogs/knowle... · Posted by u/badmonster
kk58 commented on Does RL Incentivize Reasoning in LLMs Beyond the Base Model? limit-of-rlvr.github.io/... · Posted by u/leodriesch
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.
kk58 commented on I built a large language model "from scratch" brettgfitzgerald.com/post... · Posted by u/controversy187
kk58 commented on "Language Models Can Solve Engineering Optimization Problems" sciencedirect.com/science... · Posted by u/kk58
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
kk58 commented on An Introduction to Neural Ordinary Differential Equations [pdf] diposit.ub.edu/dspace/bit... · Posted by u/gballan
How would you describe what a neural ODE is in the simplest possible terms? Let's say I know what an NN and a DE are :).
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.
kk58 commented on Eighty Years of the Finite Element Method (2022) link.springer.com/article... · Posted by u/sandwichsphinx
Those are definitely up next in the flashy-new-thing pipeline and I'm not that up to speed on them yet.
Another group within my company is evaluating them right now and the early results seems to be "not very accurate, but directionally correct and very fast" so there may be some value in non-FEM experts using them to quickly tell if A or B is a better design; but will still need a more proper analysis in more accurate tools.
It's still early though and we're just starting to see the first non-research solvers hitting the market.
Very curious, we are getting good results with PiNN and operators, what's your domain?
kk58 commented on STORM: Get a Wikipedia-like report on your topic storm.genie.stanford.edu/... · Posted by u/fragmede
Doesn't even work. 500 error
kk58 commented on The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems arxiv.org/abs/2312.17601... · Posted by u/dhruvdh
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