agent memory
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deductive state episodic memory
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facts / rules / time fuzzy context
provenance semantic retrieval
retractions source text
I accidentally turned LLM memory into program analysis
101–105 of 105 posts
Re: I accidentally turned LLM memory into program analysis
#102I reached a similar conclusion: LLMs should only really sit at the terminals of request fulfilment. 1. User request understanding: natural language -> a more rigorous representation, in my case Datalog. 2. Result interpretation: facts and derived facts -> natural language. Between those terminals, the work should be mechanical reasoning over some ontology or formal knowledge structure. That connects to another princi…
When i hear “weathering” i think of something slowly eroding away
Weather is fun choice since it’s a contronym. It has opposing definitions: to endure and to erode.
“He weathered the storm” vs “the stone weathered into an arch”.
Re: I accidentally turned LLM memory into program analysis
#103I’ve been playing with this too, the OP is rediscovering the whole field of AI before LLMs. Prolog, temporal logic …
The next one is ontology and semantic web, for which the “trying to stem verbs and nouns” is a crude v0.1 attempt.
Re: I accidentally turned LLM memory into program analysis
#104[flagged]
Re: I accidentally turned LLM memory into program analysis
#105Earlier quoted context omitted.
When i hear “weathering” i think of something slowly eroding away
Weather is fun choice since it’s a contronym. It has opposing definitions: to endure and to erode. “He weathered the storm” vs “the stone weathered into an arch”.
Really (in your example) the timeframe is the only difference.
You can endure as long as you want to, but we're all going to erode eventually.