AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
arXiv:2602.22769v1 Announce Type: new Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical for achieving strong performance. However, a significant gap exists between practical applications and current evaluation standards for agent memory: existing benchmarks primarily focus on dialogue-centric, human-agent interactions. In reality, agent memory consists of a continuous stream of agent-environment interactions that are primarily composed of machine-generated representations. To bridge this gap, we introduce AMA-Bench (Agent Memory with Any length), which evaluates long-horizon memory for LLMs in real agentic applications. It features two key components: (1) a set of real-world agentic trajectories across representative agentic applications, paired with expert-curated QA, and (2) a set of synthetic agentic trajectories that scale to arbitrary horizons, paired with rule-based QA. Ou
arXiv:2602.22769v1 Announce Type: new Abstract: Large Language Models (LLMs) are deployed as autonomous agents in increasingly complex applications, where enabling long-horizon memory is critical for achieving strong performance. However, a significant gap exists between practical applications and current evaluation standards for agent memory: existing benchmarks primarily focus on dialogue-centric, human-agent interactions. In reality, agent memory consists of a continuous stream of agent-environment interactions that are primarily composed of machine-generated representations. To bridge this gap, we introduce AMA-Bench (Agent Memory with Any length), which evaluates long-horizon memory for LLMs in real agentic applications. It features two key components: (1) a set of real-world agentic trajectories across representative agentic applications, paired with expert-curated QA, and (2) a set of synthetic agentic trajectories that scale to arbitrary horizons, paired with rule-based QA. Our comprehensive study shows that existing memory systems underperform on AMA-Bench primarily because they lack causality and objective information and are constrained by the lossy nature of similarity-based retrieval employed by many memory systems. To address these limitations, we propose AMA-Agent, an effective memory system featuring a causality graph and tool-augmented retrieval. Our results demonstrate that AMA-Agent achieves 57.22% average accuracy on AMA-Bench, surpassing the strongest memory system baselines by 11.16%.
Executive Summary
This study bridges the gap between current evaluation standards and practical applications of Large Language Models (LLMs) as autonomous agents. AMA-Bench, a new evaluation benchmark, assesses long-horizon memory for LLMs in real-world agentic applications, highlighting limitations of existing memory systems. The study proposes AMA-Agent, a novel memory system that addresses these limitations, achieving superior performance on AMA-Bench. This work has significant implications for the development and deployment of LLMs in complex applications.
Key Points
- ▸ AMA-Bench evaluates long-horizon memory for LLMs in real-world agentic applications
- ▸ Existing memory systems underperform due to causality and objective information limitations
- ▸ AMA-Agent proposes a novel memory system with causality graph and tool-augmented retrieval
Merits
Strength
AMA-Bench provides a comprehensive evaluation framework for long-horizon memory in LLMs, highlighting the limitations of existing systems.
Innovative Approach
AMA-Agent's causality graph and tool-augmented retrieval provide a novel solution to addressing the limitations of existing memory systems.
Improved Performance
AMA-Agent achieves superior performance on AMA-Bench, surpassing state-of-the-art memory systems by a significant margin.
Demerits
Limitation
AMA-Bench is narrowly focused on LLMs, potentially limiting its applicability to other types of agents.
Scalability
AMA-Agent's performance may degrade as the complexity of the agentic application increases.
Interpretability
The causal graph and tool-augmented retrieval components of AMA-Agent may be challenging to interpret and understand.
Expert Commentary
The study presents a significant advancement in the evaluation and development of long-horizon memory for LLMs. AMA-Bench and AMA-Agent demonstrate a deep understanding of the limitations of existing memory systems and propose innovative solutions to address these limitations. The study has far-reaching implications for the development and deployment of LLMs in complex applications. However, further research is needed to fully explore the scalability and interpretability of AMA-Agent. Overall, this study is a significant contribution to the field of AI and LLMs.
Recommendations
- ✓ Future research should explore the scalability of AMA-Agent in more complex agentic applications.
- ✓ Developers should consider incorporating AMA-Bench and AMA-Agent into their evaluation and development pipelines for LLMs.
