Scaling LLM-based agents to long-horizon deep research is constrained by the context-noise trade-off, where linear history accumulation degrades reasoning and dilutes fine-grained evidence. To address this, we introduce the Cognitive Scaffold, a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. Unlike unstructured summarization, our framework employs a Rejection Sampling Fine-Tuning (RFT) pipeline to crystallize saturated context into structured event snapshots, strictly enforcing atomic constraints to preserve numerical values and entities. During reasoning, a thought-driven dual-path retrieval mechanism enables the agent to proactively recover precise evidence. Empirical evaluations on Xbench-DeepSearch, BrowseComp-ZH, and GAIA demonstrate that Cognitive Scaffold consistently outperforms baselines, achieving 74.7% Avg@3 and 87.0% Pass@3 on Xbench-DeepSearch, 48.5% Avg@3 and 65.9% Pass@3 on BrowseComp-ZH, and 72.8% Avg@3 and 88.3% Pass@3 on GAIA, while reducing compression hallucinations to 5.3%. We open-source our codebase to facilitate future research.

Task-oriented dialogue (TOD) systems are vital for facilitating complex, goal-directed interactions across sectors like customer support and online retail. However, they face persistent limitations: labor-intensive manual metadata tuning and sparse reinforcement learning (RL) rewards that fail to diagnose invocation errors. To address this, we propose ToolDNA, a dynamic adaptation framework enabling autonomous co-evolution of policy networks and tool metadata via RL, anchored by two synergistic loops. An RL loop optimizes policies by generating rollout trajectories (reasoning, actions, descriptive updates) from user inputs, with multi-dimensional rewards refining invocations. A tool metadata loop—coordinated by a dedicated Tool Manager—evolves metadata through policy-generated candidates during rollouts and Feedback LLM-derived refinements from historical data. These mutually reinforcing loops close traditional reward gaps, forming a closed-loop trial-error-reflection cycle for self-improvement. Extensive experiments on a real-world dataset of 3,100 customer service dialogues confirm ToolDNA’s superiority, with notable gains over baselines: it achieves +11% problem resolution and +54% accuracy over commercial LLMs with prompt engineering; +25%/+35% over supervised fine-tuning; and +15%/+15% over traditional RL baseline. Linguistic analysis corroborates evolved metadata retain semantic intent while enhancing parseability. Case studies in two typical contexts, i.e., car inventory search and loan calculation, further validates its ability to resolve critical ambiguities. ToolDNA pioneers scalable self-improvement for robust, deployable tool-augmented agents with minimal human oversight. We release our code to facilitate future research.