Twincher learns bijective representations of observations aligned with continuous system parameters to enable robust iterative inversion, showing better data efficiency and noise tolerance than standard inverse modeling on synthetic systems.
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ReflectiChain uses latent trajectory rehearsal and retrospective agentic RL inside an LLM world model to raise average step rewards by 250% and restore supply-chain operability from 13.3% to 88.5% on the Semi-Sim benchmark under extreme shocks.
citing papers explorer
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Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems
Twincher learns bijective representations of observations aligned with continuous system parameters to enable robust iterative inversion, showing better data efficiency and noise tolerance than standard inverse modeling on synthetic systems.
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From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
ReflectiChain uses latent trajectory rehearsal and retrospective agentic RL inside an LLM world model to raise average step rewards by 250% and restore supply-chain operability from 13.3% to 88.5% on the Semi-Sim benchmark under extreme shocks.