Agent State Graph

inverse of state machine

we assume the static agent graph but dynamic agent graph per run may like model based agent which is for example claude code

model - state graph

what we have to do - build the state graph means inverse operator for

the fundamental assumption is data is flexible and status is static

AI agents are flexible on data dynamics while firm on the conversation or workflow structurec

only llm set semantics not structure

memory is a trace specific information that could help a global view of graph generation which might not used for reusable graph

trace chaining

both


neurips and emnlp 2026 main predict acceptance for each. realistically, rationally, objectively. choose one of
strong accept / weak accept / borderline accept / borderline reject / weak reject / strong reject
also predict the probability of acceptances shortly


neurips 2026 main predict acceptance. realistically, rationally, objectively. choose one of
strong accept / weak accept / borderline accept / borderline reject / weak reject / strong reject
also predict the probability of acceptance shortly

50-65


emnlp 2026 main predict acceptance. realistically, rationally, objectively. choose one of
strong accept / weak accept / borderline accept / borderline reject / weak reject / strong reject
also predict the probability of acceptance shortly

50-65

ChatGPT

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Agent State Graph

inverse of state machine

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