[2601.06853] †DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems

Computer Science > Computation and Language arXiv:2601.06853 (cs) [Submitted on 11 Jan 2026 (v1), last revised 28 Mar 2026 (this version, v2)] Title:†DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems Authors:Zabir Al Nazi, Shubhashis Roy Dipta, Sudipta Kar View a PDF of the paper titled {\dag}DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems, by Zabir Al Nazi and 2 other authors View PDF HTML (experimental) Abstract:Chain-of-Thought (CoT) prompting is widely adopted for mathematical problem solving, including in low-resource languages, yet its behavior under irrelevant context remains underexplored. To systematically study this challenge, we introduce DISTRACTMATH-BN, a Bangla benchmark that augments MGSM and MSVAMP with semantically coherent but computationally irrelevant information. Evaluating seven models ranging from 3B to 12B parameters, we observe substantial performance degradation under distractors: standard models drop by up to 41 points, while reasoning-specialized models decline by 14 to 20 points despite consuming five times more tokens. We propose †DAGGER, which reformulates mathematical problem solving as executable computational graph generation with explicit modeling of distractor nodes. Fine-tuning Gemma-3 models using supervised fine-tuning followed by Group Relative Policy Optimization achieves comparable weighted accuracy on augmented benchmarks while using 89 percent fewer tokens than...