Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however...
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Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however...
General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning...
Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for...
Large language model (LLM) inference serving is increasingly constrained by memory rather than compute. As long-conte...
Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explain...
Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems prim...
With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploy...
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to prot...
The development of embodied Intelligent Virtual Agents (IVAs) that have cognitive capabilities in real-time interacti...
Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a u...
We present Ego-OSCAR, an open-hardware, low-cost, head-mounted stereo-inertial capture device for egocentric data col...
Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment...