Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2608.21690v1 Announce Type: new Abstract: LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier i...
LLM agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier interactions or extract selected information into...
arXiv:2608. 16381v1 Announce Type: new Abstract: Agentic systems often organize execution and state around a single conversation, model invocation, or agent instance, even when real work spans many calls and stages.
The paper introduces "Trace as State," a method that places collected reasoning traces before a long-context block in a transformer, allowing earlier derived information to guide rereading. Experiments across three models and datasets show that this approach consistently outperforms a control where traces are appended after the context, achieving significant accuracy gains on tasks such as GraphWalks Parents and GLM-5.2. The results demonstrate that positioning traces before the context can enhance long-context reasoning while preserving the causal transformer architecture.
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 a compression policy that discards evidence or manage context in a layer the agent never sees.
arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even...