让大模型自己看清上下文状态，实现自动管理。
LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception
- 在模型层添加可感知上下文的透明界面，无需训练即可识别内容块状态。
- 在多个任务上提升性能，最高达58.0%，且在不同规模轨迹中可迁移。
- 适合需要长程推理与上下文管理的智能体系统开发者使用。
长周期工具代理受限于上下文增长逼近上下文窗口极限。现有系统采用代理或系统控制的上下文管理，但要么学习压缩策略丢弃证据，要么在代理无法感知的层进行管理。我们指出两者均忽略一个基本问题：前沿语言模型对其自身上下文缺乏本体感知能力——仅凭提示无法可靠推断块大小、时间顺序或剩余预算，而这些是决定保留或归档的关键。我们提出VISTA（Tool Agents可见内部状态），一种无需训练、模型无关的层，将工作记忆表示为可类型化、可寻址的块，实时展示令牌使用情况、时间顺序、归档状态和剩余预算，并以完整保真度归档块。在LOCA-Bench、BrowseComp-Plus和GAIA上，同一未训练接口跨100万、10万、1万级轨迹迁移成功。在LOCA-Bench上，使Gemini-3-Flash性能从22.7%提升至50.7%，在BrowseComp-Plus达到58.0%，在GAIA保持竞争力。收益随上下文压力增加，且在不同模型骨架间可迁移，消融实验证明仪表盘本身比归档与恢复工具更关键。
原文摘要 · Abstract (English)
Long-horizon tool agents are bottlenecked by how their context grows toward the limits of the context window. Recent systems make context management agent- or system-controlled, but they either learn compression policies that discard evidence or manage context in a layer the agent never sees. We argue that both miss a more basic gap: frontier language models are proprioceptively blind to their own context. From the prompt alone they cannot reliably infer block size, recency, or the remaining budget, all of which are needed for keep-or-archive decisions. We introduce VISTA (Visible Internal State for Tool Agents), a training-free, model-agnostic layer that represents working memory as typed addressable blocks, surfaces a runtime dashboard of token usage, recency, archive status, and remaining budget, and archives blocks as recoverable full-fidelity payloads. On LOCA-Bench, BrowseComp-Plus, and GAIA, the same untrained interface transfers across 1M-, 100K-, and 10K-scale trajectories. On LOCA-Bench it lifts Gemini-3-Flash from 22.7 to 50.7%, reaches 58.0% on BrowseComp-Plus, and remains competitive on GAIA. Gains grow with context pressure and transfer across backbones, while ablations confirm that the dashboard matters beyond archive and recovery tools.
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