arXiv:2604.22520v1 Announce Type: new
Abstract: Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy is the hybrid system pa…
arXiv:2511.07003v2 Announce Type: replace
Abstract: Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging. In this pap…
arXiv:2602.06019v2 Announce Type: replace
Abstract: Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inference pipelines. We co…
arXiv:2604.22750v1 Announce Type: cross
Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three question…
arXiv:2604.10079v4 Announce Type: replace
Abstract: Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent failure mode: even after convergence, models ofte…
arXiv:2604.22050v1 Announce Type: cross
Abstract: Transformers are mostly relying on softmax attention, which introduces quadratic complexity with respect to sequence length and remains a major bottleneck for efficient inference. Prior work on linear …
arXiv:2502.17011v2 Announce Type: replace-cross
Abstract: Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framework that leve…
arXiv:2604.20834v2 Announce Type: replace
Abstract: Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency and a lack of high-level knowledge and spatial a…
arXiv:2604.22014v1 Announce Type: cross
Abstract: We present DM$^3$-Nav, a fully decentralized multi-agent semantic navigation system supporting multimodal open-vocabulary goal specification and multi-object missions. In our setting, decentralization …
arXiv:2506.14831v3 Announce Type: replace-cross
Abstract: With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand’s reach, with important impli…