ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability

• URL: http://arxiv.org/abs/2607.14145v1
• Date: Tue, 14 Jul 2026 06:03:39 GMT
• Title: ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
• Abstract summary: Tool-augmented large language model agents excel at long-horizon tasks, yet they are typically post-trained on fixed toolsets.<n>We identify the core obstacle in such toolset expansion problem as behavioral inertia.<n>We demonstrate that injecting counterfactual anchor contexts at critical decision points can break this inertia.
• Score: 16.62013823879194
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Spurious Tool Use: When RL Agents Learn the Wrong Reason to Act [38.40533345940731]
  Large language model (LLM) agents increasingly interleave natural language reasoning with external tools.<n>We study when and why RL-trained agents learn shortcut tool-selection policies.
  arXiv  Detail & Related papers  (2026-09-14T19:31:04Z)
• Forgotten in Weights, Recovered by Tools: Agentic Tool Unlearning for LLM Agents [54.050506620820784]
  Agentic Tool Unlearning (ATU) aims to reduce both parametric recall and tool-mediated recovery while preserving normal tool use for retained knowledge.<n>ATU achieves a better balance between target forgetting and retained utility, making unlearning more robust under tool-augmented agent deployment.
  arXiv  Detail & Related papers  (2026-08-21T18:34:48Z)
• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents [52.758563996979596]
  Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use.<n>We propose SENTINEL, a failure-driven reinforcement learning framework that turns rollout failures into targeted training tasks.<n>On Tau2-Bench Retail with Qwen3-4B-Thinking-2507, SENTINEL improves Pass1 from 66.4 to 74.9 and outperforms RL on general synthetic tasks across Passk metrics.
  arXiv  Detail & Related papers  (2026-06-11T05:06:50Z)
• Diversity Over Frequency: Rethinking Tool Use in Visual Chain-of-Thought Agents [8.575257729172202]
  Visual agents employ external visual tools within visual chains of thought to incorporate fine-grained evidence.<n>In this paper, we move beyond simple visual search tasks to investigate more challenging tasks, including 3D spatial reasoning and medical visual question answering.<n>We identify a tool-use collapse phenomenon: models progressively stop using tools while still achieving higher task accuracy.<n>We find that vanilla training and tool-use encouragement both reduce rollout diversity, explaining why higher tool use does not yield stronger reasoning performance.
  arXiv  Detail & Related papers  (2026-05-25T13:06:59Z)
• Tool-Genesis: A Task-Driven Tool Creation Benchmark for Self-Evolving Language Agent [45.450766613995135]
  Tool-Genesis is a diagnostic benchmark designed to quantify agent capabilities across multiple dimensions.<n>We find that even state-of-the-art models struggle to produce precise tool interfaces or executable logic in a one-shot setting.
  arXiv  Detail & Related papers  (2026-03-05T17:44:29Z)
• Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning [16.12114923351562]
  We propose a training-free framework that transforms agents from tool users to tool creators.<n>This approach harvests reasoning experiences and distills them into reusable assets.<n>We also introduce a memory consolidation mechanism to maintain the tool library.
  arXiv  Detail & Related papers  (2026-02-02T11:37:45Z)
• AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning [66.24374176797075]
  We introduce textbfAdaReasoner, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior.<n>AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that prioritizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage.
  arXiv  Detail & Related papers  (2026-01-26T16:04:43Z)
• Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates [56.73907811047611]
  Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities.<n>LLMs often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.<n>We introduce a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function callings.
  arXiv  Detail & Related papers  (2025-09-22T17:55:14Z)
• Self-Challenging Language Model Agents [98.62637336505242]
  We propose the Self-Challenging framework for training an agent on high-quality tasks that are generated by itself.<n>The framework achieves over a two-fold improvement in Llama-3.1-8B-Instruct, despite using only self-generated training data.
  arXiv  Detail & Related papers  (2025-06-02T14:23:33Z)
• Learning to Use Tools via Cooperative and Interactive Agents [58.77710337157665]
  Tool learning empowers large language models (LLMs) as agents to use external tools and extend their utility. We propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately. Our experiments on three datasets show that the LLMs, when equipped with ConAgents, outperform baselines with substantial improvement.
  arXiv  Detail & Related papers  (2024-03-05T15:08:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.