R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
arXiv:2606.04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure reliable delivery. We trace this gap to three coupled structural failures: errors propagate without localization, worst-case perturbations go unevaluated, and accumulated knowledge is never invalidated. We argue these share a root cause: abductive, counterfactual, meta-inductive, corrective, and inductive reasoning pull a shared context in incompatible directions. We int
The increasing deployment of large language models in agentic systems, coupled with their inherent limitations in complex reasoning and error handling, necessitates new architectural approaches to ensure reliable performance.
Improving the reliability and robustness of AI agents through compositional reasoning and in-context meta-learning can unlock significant value in automated workflows and complex problem-solving.
This research suggests a pathway to developing more reliable and autonomous AI agents capable of handling complex, real-world tasks with greater robustness against errors and unforeseen perturbations.
- · AI agent developers
- · SaaS platforms embracing AI automation
- · Industries requiring complex autonomous decision-making
- · Companies relying on unreliable 'black box' AI solutions
- · Human workflows easily automated by advanced agents
More sophisticated and reliable AI agents will emerge, capable of tackling previously intractable problems.
The proliferation of these agents will accelerate the automation of knowledge work and complex operational tasks.
This could lead to a significant restructuring of white-collar employment and the emergence of new AI-driven economic sectors.
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Read at arXiv cs.AI