Selected work

Representative Papers

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

Aug 06, 2026

Existing 3D scene generation methods struggle to reliably satisfy task-critical functional constraints such as navigability and reachability, limiting the practical utility of synthetic data. This work proposes an iterative agent-based reinforcement learning framework that first enhances physical plausibility and layout quality through pretraining with generic rewards, then leverages a large language model (LLM) to generate executable, task-specific reward programs. These LLM-generated rewards are integrated into a feedback-driven reinforcement learning loop for iterative refinement. By uniquely combining LLM-synthesized reward functions with iterative reinforcement learning, the approach significantly improves adherence to functional constraints while preserving scene diversity, thereby enhancing downstream task performance.

0 citationsRead paper

Neural Multi-Speaker Voice Cloning for Nepali in Low-Resource Settings

Jan 26, 2026

This work proposes a few-shot voice cloning system to address the scarcity of multi-speaker speech synthesis for low-resource Nepali. Leveraging a small amount of untranscribed Nepali speech, the system trains a speaker encoder and integrates it with a Tacotron2 acoustic model and a WaveRNN vocoder to generate target-speaker utterances directly from Devanagari script. A novel generative end-to-end loss is introduced to optimize speaker embeddings, and their representational quality is validated through UMAP visualization. Experimental results demonstrate that the system not only effectively clones voices of seen speakers but also generalizes to unseen speakers, achieving the first successful implementation of multi-speaker voice cloning for Nepali under low-resource conditions and offering a viable pathway toward personalized text-to-speech synthesis for other resource-constrained languages.

0 citationsRead paper

Recent publications

Latest Papers

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

Aug 06, 2026

Existing 3D scene generation methods struggle to reliably satisfy task-critical functional constraints such as navigability and reachability, limiting the practical utility of synthetic data. This work proposes an iterative agent-based reinforcement learning framework that first enhances physical plausibility and layout quality through pretraining with generic rewards, then leverages a large language model (LLM) to generate executable, task-specific reward programs. These LLM-generated rewards are integrated into a feedback-driven reinforcement learning loop for iterative refinement. By uniquely combining LLM-synthesized reward functions with iterative reinforcement learning, the approach significantly improves adherence to functional constraints while preserving scene diversity, thereby enhancing downstream task performance.

0 citationsRead paper

Neural Multi-Speaker Voice Cloning for Nepali in Low-Resource Settings

Jan 26, 2026

This work proposes a few-shot voice cloning system to address the scarcity of multi-speaker speech synthesis for low-resource Nepali. Leveraging a small amount of untranscribed Nepali speech, the system trains a speaker encoder and integrates it with a Tacotron2 acoustic model and a WaveRNN vocoder to generate target-speaker utterances directly from Devanagari script. A novel generative end-to-end loss is introduced to optimize speaker embeddings, and their representational quality is validated through UMAP visualization. Experimental results demonstrate that the system not only effectively clones voices of seen speakers but also generalizes to unseen speakers, achieving the first successful implementation of multi-speaker voice cloning for Nepali under low-resource conditions and offering a viable pathway toward personalized text-to-speech synthesis for other resource-constrained languages.

0 citationsRead paper