Bangla Sign Language Translation (BdSLT) has been severely constrained so far as the language itself is very low resource. Standard sentence level dataset creation for BdSLT is of immense importance for developing AI based assistive tools for deaf and hard of hearing people of Bangla speaking community. In this paper, we present a dataset, IsharaKhobor , and two subset of it for enabling research. We also present the challenges towards developing the dataset and present some way forward by benchmarking with landmark based raw and RQE embedding. We do some ablation on vocabulary restriction and canonicalization of the same within the dataset, which resulted in two more datasets, IsharaKhobor_small and IsharaKhobor_canonical_small. The dataset is publicly available at: www.kaggle.com/datasets/hasanssl/isharakhobor [1].

Sign language recognition (SLR) for low-resource languages like Bangla suffers from signer variability, viewpoint variations, and limited annotated datasets. In this paper, we present BdSLW401, a large-scale, multi-view, word-level Bangla Sign Language (BdSL) dataset with 401 signs and 102,176 video samples from 18 signers in front and lateral views. To improve transformer-based SLR, we introduce Relative Quantization Encoding (RQE), a structured embedding approach anchoring landmarks to physiological reference points and quantize motion trajectories. RQE improves attention allocation by decreasing spatial variability, resulting in 44.3% WER reduction in WLASL100, 21.0% in SignBD-200, and significant gains in BdSLW60 and SignBD-90. However, fixed quantization becomes insufficient on large-scale datasets (e.g., WLASL2000), indicating the need for adaptive encoding strategies. Further, RQE-SF, an extended variant that stabilizes shoulder landmarks, achieves improvements in pose consistency at the cost of small trade-offs in lateral view recognition. The attention graphs prove that RQE improves model interpretability by focusing on the major articulatory features (fingers, wrists) and the more distinctive frames instead of global pose changes. Introducing BdSLW401 and demonstrating the effectiveness of RQE-enhanced structured embeddings, this work advances transformer-based SLR for low-resource languages and sets a benchmark for future research in this area.

A significant roadblock to the development of principled multi-agent reinforcement learning is the fact that desired solution concepts like Nash equilibria may be intractable to compute. To overcome this obstacle, we take inspiration from behavioral economics and show that -- by imbuing agents with important features of human decision-making like risk aversion and bounded rationality -- a class of risk-averse quantal response equilibria (RQE) become tractable to compute in all $n$-player matrix and finite-horizon Markov games. In particular, we show that they emerge as the endpoint of no-regret learning in suitably adjusted versions of the games. Crucially, the class of computationally tractable RQE is independent of the underlying game structure and only depends on agents' degree of risk-aversion and bounded rationality. To validate the richness of this class of solution concepts we show that it captures peoples' patterns of play in a number of 2-player matrix games previously studied in experimental economics. Furthermore, we give a first analysis of the sample complexity of computing these equilibria in finite-horizon Markov games when one has access to a generative model and validate our findings on a simple multi-agent reinforcement learning benchmark.