SideQuest: Model-Driven KV Cache Management for Long-Horizon Agentic Reasoning
arXiv:2602.22603v1 Announce Type: new Abstract: Long-running agentic tasks, such as deep research, require multi-hop reasoning over information distributed across multiple webpages and documents. In such tasks, the LLM context is dominated by tokens from external retrieval, causing memory usage to grow rapidly and limiting decode performance. While several KV cache compression techniques exist for long-context inputs, we find that existing heuristics fail to support multi-step reasoning models effectively. We address this challenge with SideQuest -- a novel approach that leverages the Large Reasoning Model (LRM) itself to perform KV cache compression by reasoning about the usefulness of tokens in its context. To prevent the tokens associated with this management process from polluting the model's memory, we frame KV cache compression as an auxiliary task executed in parallel to the main reasoning task. Our evaluations, using a model trained with just 215 samples, show that SideQuest r
arXiv:2602.22603v1 Announce Type: new Abstract: Long-running agentic tasks, such as deep research, require multi-hop reasoning over information distributed across multiple webpages and documents. In such tasks, the LLM context is dominated by tokens from external retrieval, causing memory usage to grow rapidly and limiting decode performance. While several KV cache compression techniques exist for long-context inputs, we find that existing heuristics fail to support multi-step reasoning models effectively. We address this challenge with SideQuest -- a novel approach that leverages the Large Reasoning Model (LRM) itself to perform KV cache compression by reasoning about the usefulness of tokens in its context. To prevent the tokens associated with this management process from polluting the model's memory, we frame KV cache compression as an auxiliary task executed in parallel to the main reasoning task. Our evaluations, using a model trained with just 215 samples, show that SideQuest reduces peak token usage by up to 65% on agentic tasks with minimal degradation in accuracy, outperforming heuristic-based KV cache compression techniques.
Executive Summary
The article 'SideQuest: Model-Driven KV Cache Management for Long-Horizon Agentic Reasoning' proposes a novel approach to KV cache compression for long-context inputs. The authors address the challenge of existing heuristics failing to support multi-step reasoning models by leveraging the Large Reasoning Model (LRM) to reason about the usefulness of tokens in its context. This approach reduces peak token usage by up to 65% on agentic tasks with minimal degradation in accuracy, outperforming heuristic-based KV cache compression techniques. The research demonstrates promising results using a model trained with just 215 samples, suggesting potential for real-world applications. However, further evaluation with larger datasets and more complex tasks is necessary to fully assess the approach's efficacy and scalability.
Key Points
- ▸ SideQuest leverages the Large Reasoning Model (LRM) to perform KV cache compression by reasoning about token usefulness
- ▸ The approach reduces peak token usage by up to 65% on agentic tasks with minimal accuracy degradation
- ▸ SideQuest outperforms heuristic-based KV cache compression techniques in evaluations with a model trained on 215 samples
Merits
Innovative Approach
SideQuest's model-driven approach to KV cache compression offers a novel solution to the challenges of multi-step reasoning models, leveraging the strengths of the Large Reasoning Model (LRM)
Promising Results
The research demonstrates significant reductions in peak token usage and minimal accuracy degradation, suggesting potential for real-world applications
Demerits
Limited Evaluation
The research is limited by the use of a small dataset (215 samples) and the need for further evaluation with larger datasets and more complex tasks
Scalability Concerns
The approach's efficacy and scalability in real-world applications, particularly with large-scale datasets and complex tasks, require further investigation
Expert Commentary
The article presents a significant contribution to the field of deep learning, particularly in the context of long-context inputs and multi-step reasoning tasks. The innovative approach of leveraging the Large Reasoning Model (LRM) to perform KV cache compression demonstrates promising results and has the potential to improve the efficiency and scalability of deep learning models in real-world applications. However, further evaluation and investigation are necessary to fully assess the approach's efficacy and scalability. The research also highlights the importance of memory efficiency in deep learning models and the need for continued development of novel approaches to address these challenges.
Recommendations
- ✓ Future research should focus on evaluating the approach with larger datasets and more complex tasks to assess its efficacy and scalability in real-world applications
- ✓ The authors should investigate the potential applications of SideQuest in various domains, such as natural language processing, question-answering systems, and decision-making tasks
