GRACER: Graph Regulation and Adaptive Compression for Efficient LLM Reasoning

Chain-of-thought reasoning improves large language models by making intermediate reasoning steps explicit. Recent methods organize intermediate reasoning into structured forms such as trees, graphs, and forests. However, these methods often produce verbose or inconsistent structures. To address this issue, we introduce GRACER, Graph-Regularized Adaptive Compression for Efficient Reasoning. GRACER first regularizes noisy dependency structures through role-constrained repair. A critic then estimates structural credit and downstream uncertainty over the dependency graph. These estimates guide greedy compression of redundant nodes under the information-bottleneck principle. Across six benchmarks and two backbone models, GRACER produces more compact reasoning graphs and improves accuracy. GRACER consistently outperforms AoT, the strongest baseline in our comparison. Average performance rises from 82.42% to 86.05% on DeepSeek-V3 and from 79.13% to 81.33% on GPT-4o-mini. Compared with AoT, GRACER reduces average token usage per instance from 26.48K to 23.21K and average LLM calls by 21.5%. Role-constrained repair also reduces structural error rates from 32% to 12%, supporting the effectiveness of graph regularization.