AgentFold

AgentFold introduces proactive context management for long-horizon web agents, addressing context saturation through dynamic "folding" operations that balance detail preservation with efficient compression. The 30B parameter model outperforms dramatically larger competitors while achieving state-of-the-art results on web browsing benchmarks. - **Core problem solved**: LLM-based web agents face a fundamental trade-off: ReAct-based approaches accumulate noisy histories causing context saturation, while fixed summarization methods risk losing critical details irreversibly. AgentFold's "folding" paradigm works across multiple scales—performing granular condensations for vital details or deep consolidations for multi-step sub-tasks, inspired by human retrospective consolidation. - **Proactive context management**: Rather than passively logging action histories, AgentFold actively sculpts its context workspace through multi-scale folding operations. The system adapts dynamically to task complexity and information density, determining when to preserve fine-grained details versus when to deeply consolidate completed sub-tasks into compact summaries. - **Impressive efficiency gains**: AgentFold-30B-A3B achieves 36.2% on BrowseComp and 47.3% on BrowseComp-ZH, outperforming DeepSeek-V3.1-671B (22x larger) and surpassing proprietary agents like OpenAI's o4-mini. This demonstrates that intelligent context management can substitute for raw parameter count in long-horizon agent tasks. - **Training simplicity**: Achieved through supervised fine-tuning on folding trajectories without requiring continual pre-training or reinforcement learning. This makes the approach more accessible for practitioners and demonstrates that the folding capability can be learned from demonstration alone. - **Benchmark leadership**: Sets new state-of-the-art results among open-source models on Chinese and English web navigation tasks. The model's ability to maintain coherent multi-step reasoning across extended browsing sessions addresses a key bottleneck in deploying agents for real-world information-seeking workflows. - **Deployment advantage**: The 30B parameter size with proactive context management offers a practical trade-off for production deployment—achieving competitive performance with 671B+ parameter competitors while requiring significantly less compute infrastructure for inference and fine-tuning.