Customer-obsessed science


Research areas
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April 11, 2025Novel three-pronged approach combines claim-level evaluations, chain-of-thought reasoning, and classification of hallucination error types.
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Featured news
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PLDI 20252025We present the first technique to synthesize programs that compose side-effecting functions, pure functions, and control flow, from partial traces containing records of only the side-effecting functions. This technique can be applied to synthesize API composing scripts from logs of calls made to those APIs, or a script from traces of system calls made by a workload, for example. All of the provided traces
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2025Recent advancements in 3D Large Multi-modal Models (3D-LMMs) have driven significant progress in 3D question answering. However, recent multi-frame VisionLanguage Models (VLMs) demonstrate superior performance compared to 3D-LMMs on 3D question answering tasks, largely due to the greater scale and diversity of available 2D image data in contrast to the more limited 3D data. Multi-frame VLMs, although achieving
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ICMLT 20252025Applications of reinforcement learning (RL) in real-world scenarios are often limited by its generalizability across multiple different environments. Contextual RL offers a principled solution to this issue by capturing environmental heterogeneity through observable contextual variables. However, directly applying Contextual RL may not achieve optimal results when contexts exhibit high randomness and variance
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International Journal of Research in Marketing2025In 2020, Amazon launched the Climate Pledge Friendly (CPF) program to make it easy for customers to discover and shop for products with sustainability certifications. In this paper, we measure the causal impact of products qualifying for CPF on consumer purchase behavior. Using a dataset of about 45 thousand products spanning three categories, and a Difference-in-Differences identification strategy, we
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QECC-Synth: A layout synthesizer for quantum error correction codes on sparse hardware architecturesASPLOS 20252025Quantum Error Correction (QEC) codes are essential for achieving fault-tolerant quantum computing (FTQC). However, their implementation faces significant challenges due to disparity between required dense qubit connectivity and sparse hardware architectures. Current approaches often either underutilize QEC circuit features or focus on manual designs tailored to specific codes and architectures, limiting
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