TASKGRAPH: DYNAMIC HIERARCHICAL MEMORY FOR LONG-HORIZON LANGUAGE MODEL AGENTS

Long-horizon tasks require agents to sustain extended interactions and multi-step reasoning. As context grows, fragmented memories can draw attention toward local details, causing agents to lose track of task progress and forget earlier goals and constraints. Existing methods organize historical information through hierarchies and relational links. These structures improve retrieval but do not explicitly organize memory around task progress and the requirements of each execution step. We propose TaskGraph, a unified model of task and memory that represents memory as the evolving structure of task execution. Task goals, stages, steps, and atomic records of constraints, evidence, and variables form a hierarchical graph linked by inclusion, mainline, and dependency relations. The graph therefore turns memory into an execution structure: it binds goals to completed work and makes the constraints, variables, and evidence required by the next step immediately accessible. For each current step, TaskGraph selects relevant historical dependencies and constructs its execution context from the graph. Observed results are then written back to update the memory available to subsequent steps. This process allows task execution and memory to evolve together within a shared representation. On StableToolBench, TaskGraph improves macro-average final answer correctness by 14.97 percentage points over the same-backbone DFS baseline using Qwen3-14B-AWQ, validating the effectiveness of jointly modeling task progression and memory