# BANGLAPLAN: A Benchmark for Constraint-Based Planning and Decision-Making in Bangla

arXiv: https://arxiv.org/abs/2610.19704 · HTML: https://mlsanity.com/paper/2610.19704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024)
- [INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge](https://mlsanity.com/paper/2411.19799/) (2024)
- [VoiceAgentBench: Are Voice Assistants ready for agentic tasks?](https://mlsanity.com/paper/2510.07978/) (2025)
- [ChinaTravel: An Open-Ended Travel Planning Benchmark with Compositional Constraint Validation for Language Agents](https://mlsanity.com/paper/2412.13682/) (2024)
- [TigerLLM - A Family of Bangla Large Language Models](https://mlsanity.com/paper/2503.10995/) (2025)
- [Script Fragmentation and Format: What Drives the English-Bengali Performance Gap in Open LLMs?](https://mlsanity.com/paper/2507.23248/) (2025)
- [BnMMLU: Measuring Massive Multitask Language Understanding in Bengali](https://mlsanity.com/paper/2505.18951/) (2025)
- [ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization](https://mlsanity.com/paper/2602.22465/) (2026)
- [Ticket-Bench: A Kickoff for Multilingual and Regionalized Agent Evaluation](https://mlsanity.com/paper/2509.14477/) (2025)
- [NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance](https://mlsanity.com/paper/2603.05462/) (2026)
- [GSM-Plus-BN: A Perturbation-Based Benchmark for Bangla Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2607.13248/) (2026)
- [TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning](https://mlsanity.com/paper/2607.26977/) (2026)
- [BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Bangladeshi Social Interaction](https://mlsanity.com/paper/2603.15949/) (2026)
- [An Actionable Diagnosis of Multilingual, Multi-Agent Planning Failures](https://mlsanity.com/paper/2608.03735/) (2026)
- [WorldBench: Culturally Grounded Benchmark for Multilingual Agents](https://mlsanity.com/paper/2609.01056/) (2026)

## Abstract (arXiv)

Large language models (LLMs) increasingly help users arrange appointments, plan journeys, and allocate limited resources. These tasks require models to satisfy constraints on time, cost, capacity, and order, or determine that no feasible solution exists. Bangla is the world's seventh most widely spoken language, and existing benchmarks cover many language and reasoning tasks. However, benchmarks for evaluating planning and decision-making in Bangla under explicit constraints remain lacking. We introduce BANGLAPLAN, a benchmark of 1,000 problems across ten task families grounded in everyday life and institutions in Bangladesh. It includes feasible and infeasible problems in four language registers: everyday, formal, colloquial, and Bangla--English mixed. Answers cover eight structured types, including schedules, routes, allocations, and ordered steps, in a common JSON format. We construct the benchmark through language-model-assisted drafting and independent human validation. We evaluate 11 open-weight and proprietary model configurations using exact match, separately reporting feasible-case accuracy, infeasibility detection, and format errors. We find a large performance gap: GPT-6.1 Sol achieves 98.7% accuracy and Qwen3.5-27B with thinking reaches 80.3%, while direct-answer configurations score at most 30.9%. Seven of eleven configurations perform below the always-``infeasible'' baseline. Our findings also suggest that harder problems primarily reduce plan-construction accuracy, while infeasibility detection remains more robust. BANGLAPLAN provides a reproducible basis for evaluating planning and decision-making in Bangla. BANGLAPLAN is available at: https://github.com/mrayhanulmasud/banglaplan.