Steven Robinson

Giacomo Bernardi, Ratul Mahajan, C. Seshadhri et al.

We design and deploy at Amazon the first production datacenter fabrics based on random graphs. While the cost and fault-tolerance benefits of such topologies have been long known, their practical realization has been hampered by a lack of scalable routing and cabling approaches. Our design, called RNG, has a new distributed routing protocol that exploits the properties of random graphs to find a large number of edge disjoint paths between endpoint pairs. A novel passive optical device that internally shuffles cable endpoints makes Amazon's cabling complexity similar to that of fat trees. We show that RNG fabrics match or exceed the performance of fat trees for a range of traffic patterns, despite being up to 45% cheaper. At Amazon, we made RNG the default datacenter fabric for most workloads.

Steven Robinson, Antonio Carlos Rivera

The pervasive deployment of large language models (LLMs) in conversational AI systems has revolutionized information access, yet their propensity for generating factually unsupported or hallucinated responses remains a critical impediment to trustworthiness and widespread adoption. This paper introduces Reinforced Unanswerability Learning (RUL), a novel hybrid training paradigm designed to imbue LLMs with the intrinsic capability to accurately detect unanswerable questions and generate reliably appropriate responses. Unlike conventional approaches that rely on external classifiers or simple prompting, RUL integrates a discriminative unanswerability prediction head with the LLM's generative core, guided by a multi-stage learning strategy. This includes supervised fine-tuning on a novel, richly annotated dataset, Enhanced-CAsT-Answerability (ECA), which features hierarchical answerability labels and ground-truth refusal responses. Crucially, RUL incorporates a subsequent reinforcement learning with human feedback (RLHF) phase to refine the nuance, helpfulness, and informativeness of refusal responses. Extensive experiments demonstrate RUL's superior performance, achieving significantly higher accuracy in unanswerability detection across sentence, paragraph, and ranking levels, and substantially increasing the generation of appropriate refusals for unanswerable queries, alongside strong performance on answerable questions. Human evaluations further corroborate RUL's effectiveness, highlighting a marked improvement in perceived helpfulness and trustworthiness, ultimately paving the way for more reliable and user-centric conversational AI.

Antonio Carlos Rivera, Anthony Moore, Steven Robinson

Object-aware reasoning in vision-language tasks poses significant challenges for current models, particularly in handling unseen objects, reducing hallucinations, and capturing fine-grained relationships in complex visual scenes. To address these limitations, we propose the Vision-Aware Retrieval-Augmented Prompting (VRAP) framework, a generative approach that enhances Large Vision-Language Models (LVLMs) by integrating retrieval-augmented object tags into their prompts. VRAP introduces a novel pipeline where structured tags, including objects, attributes, and relationships, are extracted using pretrained visual encoders and scene graph parsers. These tags are enriched with external knowledge and incorporated into the LLM's input, enabling detailed and accurate reasoning. We evaluate VRAP across multiple vision-language benchmarks, including VQAv2, GQA, VizWiz, and COCO, achieving state-of-the-art performance in fine-grained reasoning and multimodal understanding. Additionally, our ablation studies highlight the importance of retrieval-augmented tags and contrastive learning, while human evaluations confirm VRAP's ability to generate accurate, detailed, and contextually relevant responses. Notably, VRAP achieves a 40% reduction in inference latency by eliminating runtime retrieval. These results demonstrate that VRAP is a robust and efficient framework for advancing object-aware multimodal reasoning.