Causal diagnosis identifies the routing module as bottleneck in LLM agents but prompt patching there degrades results due to linguistic co-adaptation, while upstream patching improves them.
Trace is the next autodiff: Generative optimization with rich feedback, execution traces, and LLMs
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Fast-Slow Training uses context optimization as fast weights alongside parameter updates as slow weights to achieve up to 3x better sample efficiency, higher performance, and less catastrophic forgetting than standard RL in continual LLM learning.
MOCHA combines Chebyshev scalarization with exponential annealing to optimize LLM agent skills across performance and platform constraints, improving mean correctness by 7.5% over baselines on six tasks while finding more Pareto-optimal variants.