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.
SPEAR augments automatic prompt engineering with an agent that uses evaluate, python, set_prompt and finish tools plus auto-rollback guardrails, outperforming baselines on industrial LLM-judge tasks and BBH by leveraging Python-authored structural error analysis.
Shepherd provides a reversible execution trace substrate for LLM agents that enables meta-agents to inspect and transform runs, yielding reported gains on coding and terminal benchmarks via supervision, counterfactual repair, and RL credit assignment.
MAA formalizes alignability and comparability conditions and uses differential signals, EMA accumulation, and semantic identity merging to enable cross-batch operation-level evidence accumulation, outperforming batch-level baselines in 14 of 16 settings while matching online methods.
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.
citing papers explorer
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Diagnosis Is Not Prescription: Linguistic Co-Adaptation Explains Patching Hazards in LLM Pipelines
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.
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Learning, Fast and Slow: Towards LLMs That Adapt Continually
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.
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SPEAR: Code-Augmented Agentic Prompt Optimization
SPEAR augments automatic prompt engineering with an agent that uses evaluate, python, set_prompt and finish tools plus auto-rollback guardrails, outperforming baselines on industrial LLM-judge tasks and BBH by leveraging Python-authored structural error analysis.
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Shepherd: Enabling Programmable Meta-Agents via Reversible Agentic Execution Traces
Shepherd provides a reversible execution trace substrate for LLM agents that enables meta-agents to inspect and transform runs, yielding reported gains on coding and terminal benchmarks via supervision, counterfactual repair, and RL credit assignment.
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Marginal Advantage Accumulation for Memory-Driven Agent Self-Evolution
MAA formalizes alignability and comparability conditions and uses differential signals, EMA accumulation, and semantic identity merging to enable cross-batch operation-level evidence accumulation, outperforming batch-level baselines in 14 of 16 settings while matching online methods.
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MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization
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.