SideQuest: Model-Driven KV Cache for Agents

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📄Read original on ArXiv AI

#kv-cache#agentic-reasoning#memory-compressionsidequestsidequestlrmarxiv

💡65% KV cache cut for long agentic tasks—model-driven beats heuristics!

⚡ 30-Second TL;DR

What Changed

LRM reasons about token usefulness for KV cache compression

Why It Matters

Enables efficient long-horizon agentic reasoning like deep research by slashing memory use. Boosts decode performance for real-world LLM agents without accuracy trade-offs.

What To Do Next

Download arXiv:2602.22603v1 and prototype SideQuest in your LRM agent for memory savings.

Who should care:Researchers & Academics

Key Points

•LRM reasons about token usefulness for KV cache compression
•Parallel auxiliary task prevents context pollution
•Trained on just 215 samples
•65% peak token reduction on agentic tasks
•Outperforms heuristic compression techniques

🧠 Deep Insight

Background and context from public sources — not the original article. 7 sources cited.

🔑 Enhanced Key Takeaways

•SideQuest was submitted to arXiv on February 26, 2026, by authors Sanjay Kariyappa and G. Edward Suh from Cornell University.[1]
•It employs a shared-context parallel reasoning architecture to execute memory management concurrently with the main task, reducing KV cache memory reads by 53-71%.[2]
•Evaluated on FRAMES (in-distribution) and BrowseComp (out-of-distribution) benchmarks, it shows up to 2% accuracy drop on FRAMES and 5% on BrowseComp.[2]

📊 Competitor Analysis▸ Show

Feature	SideQuest	Quest	TARDIS
Approach	Model-driven LRM reasoning for token eviction	Query-aware sparsity using min/max Key values for Top-K pages	GPU-driven KV store with NVMe SSD access via GeminiFS
Speedup	56-65% peak token reduction, 53-71% memory read reduction	Up to 7.03× self-attention, 2.23× end-to-end	Not specified in results
Accuracy Loss	≤2% in-dist, 5% out-of-dist	Negligible	Not specified
Benchmarks	FRAMES, BrowseComp agentic tasks	Long-context text generation	LLM inference
Training Data	215 samples	Not specified	Not specified

🛠️ Technical Deep Dive

•Leverages LRM to analyze ReAct loop state and problem definitions for explicit tool response eviction, avoiding proxy metrics like attention scores.[2]
•Uses shared-context parallel reasoning to run auxiliary compression task without adding management tokens to primary context.[2]
•Demonstrated on long-horizon agentic tasks like deep research involving multi-hop reasoning over distributed webpages.[1]

🔮 Future ImplicationsAI analysis grounded in cited sources

SideQuest shifts Pareto frontier for agentic memory management

It achieves 56-65% peak token reduction and 53-71% KV cache memory read savings with minimal accuracy loss, outperforming heuristics on agentic benchmarks.[2]

Low-data training enables rapid deployment

Training on only 215 samples yields strong results, lowering barriers for adapting to new LRMs or tasks.[1]

⏳ Timeline

2026-02

SideQuest paper submitted to arXiv by Sanjay Kariyappa and G. Edward Suh

📎 Sources (7)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

📄Read original article on ArXiv AI

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