LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via State Proprioception
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 type...
| Authors | Binyan Xu, Haitao Li, Kehuan Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 31 July 2026 |
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