Large Language Model Agents Are Not Always Faithful Self-Evolvers
Study finds self-evolving LLMs rely on raw experience but struggle to utilize condensed experience effectively.
Key Findings
Methodology
The study employs causal intervention techniques to evaluate experience faithfulness in four self-evolving frameworks (ExpeL, Dynamic Cheatsheet, ReasoningBank, G-Memory) across 13 LLMs and 9 environments, analyzing raw and condensed experience utilization.
Key Results
- Result 1: Models exhibit strong reliance on raw experience, with performance dropping significantly when removed (e.g., ExpeL on HotpotQA: success rate drops from 47% to 24%).
- Result 2: Even when only condensed experience is provided, models show limited semantic reliance, with negligible performance impact from semantic perturbations.
- Result 3: The raw-condensed faithfulness gap persists across multi-agent setups and model scales, indicating a fundamental limitation.
Significance
This study challenges the assumption that self-evolving LLMs faithfully leverage accumulated experience. It highlights the limitations of current methods in utilizing condensed experience, emphasizing the need for more reliable approaches to experience integration, with implications for both research and industry.
Technical Contribution
The study introduces a causal intervention framework to systematically evaluate experience faithfulness. It identifies key issues in condensed experience utilization, including semantic limitations, internal biases, and task-specific factors, providing a comprehensive analysis across diverse models and tasks.
Novelty
This is the first systematic investigation into experience faithfulness in self-evolving LLMs, introducing novel intervention techniques and uncovering fundamental issues in the use of condensed experience.
Limitations
- Limitation 1: The study relies on existing frameworks and does not explore new methods for generating condensed experiences.
- Limitation 2: Limited task diversity; not all potential scenarios were tested.
- Limitation 3: Long-term behavior in multi-agent collaboration remains underexplored.
Future Work
Future directions include developing semantically richer condensed experiences, improving internal mechanisms for better utilization, and exploring efficient experience sharing in multi-agent systems.
AI Executive Summary
Self-evolving large language models (LLMs) aim to improve by accumulating and reusing past experiences, but whether they faithfully rely on these experiences remains unclear. This study systematically investigates experience faithfulness, defined as the causal dependence of model behavior on provided experiences. Using causal intervention techniques, the authors evaluate four frameworks across 13 LLMs and 9 environments, revealing a significant gap: while models rely heavily on raw experience, they often fail to utilize condensed experience effectively.
The experiments demonstrate that removing or perturbing raw experience leads to substantial performance degradation, whereas interventions on condensed experience have minimal impact. This pattern persists across single-agent and multi-agent setups and across models of varying scales. The findings highlight a fundamental limitation in current self-evolving designs: despite theoretical efficiency, condensed experience is often ignored or misinterpreted by models.
The study identifies three root causes: semantic limitations in condensed content, internal biases favoring local context over retrieved knowledge, and task structures where pretrained priors suffice. The authors call for more robust and interpretable mechanisms for experience-driven adaptation, paving the way for more reliable self-evolving AI systems.
Deep Analysis
Background
Self-evolving agents represent a significant advancement in AI, enabling systems to learn and adapt continuously by leveraging past experiences. While prior research has focused on storing and representing these experiences, little attention has been paid to whether models faithfully utilize them. With the rise of LLMs, this question becomes increasingly critical.
Core Problem
The core issue is whether self-evolving LLMs faithfully rely on accumulated experiences to guide their behavior. Specifically, do models adjust their decisions based on provided experiences, and can they effectively utilize the semantic content of condensed experiences? Addressing this is crucial for improving the reliability of self-evolving systems.
Innovation
Key innovations include: 1) introducing a causal intervention framework to assess experience faithfulness, 2) systematically analyzing the disparity in reliance on raw versus condensed experiences, and 3) identifying three root causes for the limited utilization of condensed experiences: semantic limitations, processing biases, and task-specific factors.
Methodology
- �� Developed a causal intervention framework to test experience faithfulness by perturbing raw and condensed experiences.
- �� Evaluated four self-evolving frameworks (ExpeL, Dynamic Cheatsheet, ReasoningBank, G-Memory) across 13 LLMs and 9 diverse environments.
- �� Conducted experiments in both single-agent and multi-agent settings to analyze experience utilization.
- �� Investigated the impact of model scaling on experience faithfulness across six Qwen3 variants.
Experiments
The study evaluates performance on diverse tasks, including knowledge-intensive QA (e.g., HotpotQA), mathematical reasoning (e.g., AIME 2024), interactive environments (e.g., ALFWorld), and web interaction (e.g., WebShop). Controlled interventions such as removing, shuffling, or corrupting experience content were used to assess causal dependence on experience.
Results
Results reveal a strong reliance on raw experience, with significant performance drops when it is removed or perturbed (e.g., ExpeL on HotpotQA: success rate drops from 47% to 24%). In contrast, condensed experience shows limited semantic influence, with interventions like corruption or irrelevance having negligible effects.
Applications
The findings are critical for developing more reliable self-evolving systems in applications requiring dynamic learning and adaptation, such as robotic navigation, automated customer support, and personalized recommendations.
Limitations & Outlook
The study is limited to existing frameworks and does not explore novel methods for generating condensed experiences. Additionally, it does not fully address long-term behavior in multi-agent scenarios or cover all possible task types.
Plain Language Accessible to non-experts
Think of a self-evolving LLM as a chef learning to cook. The chef can either watch full cooking videos (raw experience) or read summarized recipes (condensed experience). This study shows that the chef learns much better from the videos because they provide more details. When relying on recipes, the chef often ignores or misinterprets the instructions, leading to mistakes. This highlights that while summaries seem efficient, they may not always be helpful if the chef doesn't understand or use them properly.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to move bananas from the kitchen to the fridge. The game gives you two types of hints: a video showing someone doing it step by step (raw experience) and a short list of tips (condensed experience). This study found that watching the video helps you play better, but the tips? Not so much! Sometimes, you might even ignore them or get confused. So, even though tips are shorter, they might not be as useful as you'd think!
Glossary
Self-evolving
The ability of a system to improve by learning from past experiences.
Describes the adaptive learning capability of LLMs.
Raw Experience
Detailed interaction trajectories including observations, actions, and feedback.
Used for direct reference or replay by the model.
Condensed Experience
High-level summaries distilled from raw experiences, such as heuristics or lessons.
Intended to provide generalizable insights.
Causal Intervention
A method to test causal dependence by modifying input variables.
Used to evaluate experience faithfulness.
Experience Faithfulness
The extent to which a model's behavior is causally influenced by provided experiences.
Measures whether the model truly utilizes experience.
Open Questions Unanswered questions from this research
- 1 How can we generate semantically richer and more useful condensed experiences?
- 2 What mechanisms can improve models' ability to utilize condensed experiences?
- 3 How can multi-agent systems effectively share and integrate experiences?
Applications
Immediate Applications
Robotic Navigation
Enhance path planning efficiency by dynamically learning from environmental experiences.
Smart Customer Support
Leverage user interaction history to provide personalized and efficient customer service.
Long-term Vision
General AI
Advance general AI by improving experience integration methods for self-learning systems.
Abstract
Self-evolving large language model (LLM) agents continually improve by accumulating and reusing past experience, yet it remains unclear whether they faithfully rely on that experience to guide their behavior. We present the first systematic investigation of experience faithfulness, the causal dependence of an agent's decisions on the experience it is given, in self-evolving LLM agents. Using controlled causal interventions on both raw and condensed forms of experience, we comprehensively evaluate four representative frameworks across 13 LLM backbones and 9 environments. Our analysis uncovers a striking asymmetry: while agents consistently depend on raw experience, they often disregard or misinterpret condensed experience, even when it is the only experience provided. This gap persists across single- and multi-agent configurations and across backbone scales. We trace its underlying causes to three factors: the semantic limitations of condensed content, internal processing biases that suppress experience, and task regimes where pretrained priors already suffice. These findings challenge prevailing assumptions about self-evolving methods and underscore the need for more faithful and reliable approaches to experience integration.