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...
arXiv:2606.22953v2 Announce Type: replace Abstract: Context managers summarize or evict old messages so agents can operate beyond a finite window. This creates a reliability problem: removing history...
StateComp introduces a method for long‑horizon agents to decide when to compress historical interactions based on the current agent state, rather than relying on fixed windows or periodic schedules. The framework uses a two‑stage annotation process to create KEEP and READY labels, trains an imbalance‑aware router on frozen language model representations, and groups adjacent READY interactions into compact summaries. Experiments on WorkBuddyBench show that StateComp cuts agent and summarization tokens by 52.27% and speeds up representation extraction 12.67‑fold while preserving task performance.
arXiv:2609.37012v1 Announce Type: new Abstract: For a long-horizon agent, context is the bottleneck: the history is resent with every request, the window caps task length, and reasoning degrades as t...
FlowState introduces a method for long-horizon LLM agents to treat execution state as a reusable memory, preserving semantically typed state nodes and their relations while separating persistent retention from on-demand access. The system uses Incremental State Update to maintain current state and Progressive State Access to reveal historical states during reasoning, allowing agents to reassess past decisions with new information. Experiments show FlowState improves success rates on MemoryArena and pass rates on τ³‑Bench by 4.55 and 13.95 percentage points, respectively, while cutting token consumption by over 40%.
arXiv:2607. 00692v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix.
arXiv:2607. 09493v1 Announce Type: new Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.