Customer-obsessed science

Multi-model inference systems—whether based on routing, cascading, or unified strategies—often rely on confidence signals to decide when a small language model (SLM) output should be accepted or deferred. While such signals are commonly used in classification and short-form generation, their reliability in structured generation settings remains poorly understood. In this work, we study log-probability confidence

Large Language Models (LLMs) perform well on short-horizon tasks but struggle with long-horizon, multimodal scenarios that require multi-step reasoning, perception, and adaptive planning. We identify two key challenges in these settings: the difficulty of long-term coordination between planning and execution within single-agent architectures and the inefficiency of indiscriminate visual grounding. To address

We present a protocol for fault-tolerantly implementing the logical quantum random access memory (QRAM) operation, given access to a specialized, noisy QRAM device. For coherently accessing classical memories of size 2^n, our protocol consumes only poly(n) fault-tolerant quantum resources (logical gates, logical qubits, quantum error correction cycles, etc.), avoiding the need to perform active error correction

This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges inherent in manually annotating in-domain queries. We study the data strategies for a two-stage training framework: in the first stage, which focuses on learning to decode document identifiers from queries, we investigate LLM-generated

Length control in Large Language Models (LLMs) is a crucial but under-addressed challenge, with applications ranging from voice interfaces requiring concise responses to research summaries needing comprehensive outputs. Current approaches to length control, including Regularized DPO, Length-Instruction Fine-Tuning, and tool-augmented methods, typically require expensive model retrain-ing or complex inference-time