arXiv subjects

Aaryan Shakya

Publications and source records attributed to Aaryan Shakya.

2 recordsLinked to original sources

arXiv2026

Nepali Legal Expertise through Generative and Extractive Pre-trained Transformers (NepLEGiT)

The complexity of legal language and limited accessibility to legal information pose significant challenges to justice delivery in Nepal. Traditional legal services remain inaccessible to many citizens due to language barriers, information fragmentation, and a critical shortage of legal expertise, particularly in rural areas. We present NepLEGiT (Nepali Legal Expertise through Generative and Extractive Pre-trained Transformers), a specialized small language model (SLM) designed to democratize legal knowledge and enhance legal-service delivery in Nepal. We pre-train a decoder-based GPT-2 SLM from scratch on a curated corpus of ~4 million tokens of Nepali legal text, covering constitutional law, civil and criminal codes, and administrative regulations. The model comprises ~30 million parameters in a 6-layer, 6-head, 384-dimensional transformer trained with warmup cosine-decay scheduling, gradient accumulation, and mixed-precision arithmetic. On a held-out validation split, NepLEGiT attains a cross-entropy loss of 0.5684, a perplexity of 1.8, and a next-token prediction accuracy of 82.9%. We further evaluate continual masked-language-model pre-training of mBERT and MuRIL on the same corpus; mBERT achieves a perplexity of 2.35 (eval loss 0.8565), outperforming MuRIL (perplexity 6.07, eval loss 1.8026), providing a strong encoder baseline complementary to NepLEGiT's generative orientation.

cs.CL↗

arXiv2026

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep tigers in immobility. Although Baghchal has a complex structure which is strategic, has perfect information structure, and has cultural meaning, it has not been adequately covered in deep reinforcement learning (RL) literature. This paper gives a systematic exploration of four deep RL solutions Deep Q-Network (DQN), REINFORCE, Proximal Policy Optimization (PPO) and MuZero that are trained on one side of the asymmetric gameplay of Baghchal and then evaluated on the other side. The algorithms are rated based on win rate, draw rate, average captures, training convergence and computational cost. It is experimentally found that MuZero generates the best performance in both tasks, achieving 86 percent win over these Tiger and 62 percent win over these Goat and the ability to do so is due to the model-based planning machine through the Monte Carlo Tree Search. PPO is the most realistic algorithm and is provided to be competitive over both asymmetric tasks with significantly reduced computational costs compared to MuZero. Emergent strategic behavior analysis shows that model-based strategies are optimal over long-horizon planning, whereas value-based counterparts like DQN are more biased up towards the Tiger role owing to the more substantial reward signal.

cs.AI↗