Why do LLM agents ignore condensed experience summaries?

The first systematic investigation of experience faithfulness in self-evolving LLM agents reveals a striking asymmetry. Using controlled causal interventions on both raw experience (concrete historical trajectories) and condensed experience (distilled rules, heuristics, abstract plans), the study evaluates four representative self-evolving frameworks across 10 LLM backbones and 9 environments.

The core finding: "while agents consistently depend on raw experience, they often disregard or misinterpret condensed experience, even when it is the only experience provided." Perturbing raw experience produces substantial behavioral changes. Perturbing condensed experience — even replacing it with irrelevant content — produces minimal changes. The asymmetry persists across single-agent and multi-agent configurations and across model scales.

Three causes form a cascading triad:

1. Semantic limitations of condensed content. Condensed experiences often "encode only vague heuristics or generic summaries, lacking the specificity required to guide behavior." The distillation process strips the actionable detail that made the raw experience useful.

2. Internal processing biases. Even when relevant content is present in condensed form, agents favor "local contextual signals over retrieved information." The model's attention to immediate context overrides the condensed experience — a processing-level failure, not a content-level one.

3. Pretrained priors suffice. For knowledge-intensive tasks, "agents often succeed by relying solely on their pretrained semantic priors, reducing the marginal utility of retrieved experience." When the model can already answer from parametric knowledge, it has no incentive to consult external experience.

This has direct implications for the vault's knowledge architecture. Since Does abstract preference knowledge outperform specific interaction recall?, the personalization finding (abstract > episodic) appears to conflict with the self-evolution finding (raw > condensed). The resolution: personalization tasks benefit from abstraction because user preferences are stable patterns well-captured by summary. Self-evolution tasks require adaptation to novel situations where the specific details of past trajectories contain the learning signal that abstraction destroys.

Since Can agents learn from failure without updating their weights?, the faithfulness asymmetry explains why verbal reflection works: it operates on raw episode-level experience (what happened, step by step) rather than condensed heuristics (what we learned). The environment-as-teacher mechanism requires the specific texture of experience, not its distillation.

The broader implication challenges the assumption that accumulated wisdom transfers effectively in AI systems. Models use concrete examples but ignore abstract principles derived from those examples — the opposite of what we might hope for systems that learn from experience.

Source: Evolution Paper: Large Language Model Agents Are Not Always Faithful Self-Evolvers