Customer-obsessed science

Optimizing the consolidation process in container-based fulfillment centers requires trading off com-peting objectives such as processing speed, re-source usage, and space utilization while adhering to a range of real-world operational constraints. This process involves moving items between con-tainers via a combination of human and robotic workstations to free up space for inbound inven-tory and increase

An AI agent that hallucinates a tool call typically shows no overt signs of failure: the call is syntactically correct, its arguments are well-formed, and the associated token probabilities appear routine. In these cases, the execution pipeline receives no indication that anything is amiss. The call executes, downstream components consume its output, and the erroneous transaction completes without human

Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to distribution shift and error accumulation.While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering. We propose VLLR, a dense reward

The problem of relevant and diverse subset selection has a wide range of applications, from recommender systems to retrieval-augmented generation(RAG). For example, in recommender systems, one is interested in selecting relevant items, while providing a diversified recommendation. Constrained subset selection problem is NP-hard, and popular approaches such as Maximum Marginal Relevance (MMR) are based on

Knowledge graphs provide a source of up-to-date structured knowledge, which makes them an ideal counterpart to LLMs. LLMs, by themselves, are not trained to run structured queries internally and can become stale without a source of up-to-date information. We hypothesize that knowledge graphs can be effectively connected to large language models via controlled natural languages. Unlike standard formal query