{"slug": "scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under", "title": "Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift", "summary": "Researchers propose Graph-Regularized Agentic Context Evolution (GRACE), a method that maintains LLM agent instructions as a typed semantic graph to enable local verification of updates. In a telecom agent benchmark under distribution shift, GRACE improved reliability from 0.091 to 0.673, outperforming flat-text baselines and a stronger zero-shot model. The work identifies structural substrate and consolidation as key requirements for reliable long-horizon context evolution.", "body_md": "arXiv:2607.09175v1 Announce Type: new\nAbstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the mutable component of that context is a persistent system-level instruction that is updated from operational experience while the model, tools, and harness remain fixed. Over long evolution horizons, flat-text maintenance makes verification increasingly difficult as accumulated instructions grow and interact. We propose Graph-Regularized Agentic Context Evolution (GRACE), which maintains the persistent instruction component as a typed semantic graph and validates proposed updates within the local typed neighborhoods of modified nodes. Accepted graph updates are reconstructed as incremental edits to the textual instruction checkpoint used at deployment. We evaluate GRACE within a fixed telecom agent harness derived from $\\tau^2$-bench under a controlled distribution-shift protocol. Across five independent replications, GRACE improves strict reliability, measured by pass^3, from the Gemini 2.5 Flash zero-shot value of 0.091 to 0.673$\\pm$0.136 at the final checkpoint. This exceeds a Gemini 3.1 Pro zero-shot reference of 0.242 on the same held-out set, while the flat-text HCE baseline finishes at 0.191$\\pm$0.051. These results identify two requirements for reliable long-horizon context evolution, a structural substrate that makes verification local and a consolidation mechanism that keeps accumulated instruction content usable.", "url": "https://wpnews.pro/news/scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under", "canonical_source": "https://arxiv.org/abs/2607.09175", "published_at": "2026-07-13 04:00:00+00:00", "updated_at": "2026-07-13 04:08:33.611062+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["GRACE", "Gemini 2.5 Flash", "Gemini 3.1 Pro", "τ²-bench"], "alternates": {"html": "https://wpnews.pro/news/scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under", "markdown": "https://wpnews.pro/news/scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under.md", "text": "https://wpnews.pro/news/scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under.txt", "jsonld": "https://wpnews.pro/news/scoped-verification-for-reliable-long-horizon-agentic-context-evolution-under.jsonld"}}