ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
This paper addresses the 'behavioral inertia' problem in tool-augmented LLM agents when expanding their toolset. By injecting counterfactual anchor contexts at critical decision points, the proposed ToolAnchor framework breaks this inertia, recovering failed trajectories. It uses teacher models to hypothesize counterfactuals, verifies them via student rollouts, and internalizes successful interventions through post-training. Evaluated on GAIA, BrowseComp, and VDR-Bench, it shows competitive performance, bridging static post-training and dynamic adaptation.
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[Submitted on 14 Jul 2026]
Title:ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
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Abstract:Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets. When tasks demand new tools, these agents struggle to incorporate them effectively, and retraining from scratch is often impractical. We identify the core obstacle in such toolset expansion problem as behavioral inertia: the tendency of agents to fall back on familiar tools and established reasoning patterns despite having access to new ones. We demonstrate that injecting counterfactual anchor contexts at critical decision points can break this inertia, recovering failed trajectories by eliciting suppressed agent capabilities. To scale this insight, we propose ToolAnchor, a framework that uses teacher models to hypothesize these counterfactual contexts, verifies them via student rollouts, and internalizes the successful interventions through agentic post-training. Extensive evaluations across general AI assistant (GAIA), textual search (BrowseComp), and visual search (VDR-Bench) tasks demonstrate that ToolAnchor consistently exhibits competitive performance under expanded toolsets. Our work bridges the gap between static post-training and dynamic adaptation, charting a new path for scalable agentic reinforcement learning.
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.14145 [cs.AI]
(or arXiv:2607.14145v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.14145
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Weiting Liu [view email] [v1] Tue, 14 Jul 2026 06:03:39 UTC (730 KB)
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