arXiv:2605.29668v1 Announce Type: new Abstract: LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the environment. Prior self-improvement methods accumulate natural-language guidance without checking that each new item preserves previously correct behavior, so a note that fixes one trajectory can silently regress another. We introduce GRASP (Gated Regression-Aware Skill Proposer), which treats agent improvement as a sequence of edits to a bounded skill library, admitting each candidate only if it
The proliferation of LLM agents in structured environments highlights the current limitations of self-improvement, specifically regression in learned behaviors, necessitating novel solutions like GRASP to ensure reliability.
Reliable and self-improving LLM agents are critical for collapsing white-collar workflows and enabling autonomous systems, making methods to prevent regression directly impactful on their commercial viability and adoption.
Agent self-improvement methods will need to incorporate regression-aware mechanisms to ensure stability and cumulative progress, moving beyond simple accumulation of natural-language guidance.
- · LLM agent developers
- · Automation software providers
- · Enterprises adopting AI agents
- · Inefficient AI agent development processes
Increased reliability and robustness of LLM agents in operational settings.
Faster and safer deployment of autonomous AI systems across various industries.
Accelerated collapse of certain white-collar roles as agents become more capable and trusted.
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Read at arXiv cs.AI