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  • arXiv cs.AIRAGOct 15

    Multi-sourced AI Tutor for Non-Native English Speaker Students

    Yiyin Shen, Xinyi Ai, Weibing Wang, Rogers Jeffrey Leo John, Meenakshi Syamkumar

    Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student questions. With the rise in enrollment of international students in computing programs, researchers have begun to explore the difficulties that non-native English speaker (NNES) students face in computing courses due to language barriers. To aid such students, we developed a general-purpose AI tutor system that can process student questions in foreign languages (currently focused on Mandarin) and provide bilingual responses. Our system also incorporates the functionality of generating a glossary of technical terms and their explanations to enhance comprehension. Our approach utilizes a specialized Retrieval-Augmented Generation (RAG) pipeline with a novel question extraction mechanism, enabling us to effectively utilize a diverse array of course materials for answering student queries. We evaluated our AI tutor using 91 Piazza posts, demonstrating that it could answer 83.52% of the posts correctly and in a helpful manner. The tutor also provided high-quality translations for 76.92% of the questions and generated…

    AIDeveloper tooling60PDF
  • arXiv cs.SEDeveloper toolingOct 15

    Stateful Testing for Deep Learning Compilers

    Jiatao Ge, Ruixiang Qian, Yibo Hu, Yanzhou Mu, Yinglong Zou +3

    Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is correct in isolation. An analysis of 26 publicly reported issues in PyTorch, JAX, and TensorFlow shows that detecting such bugs requires sequences that first establish compiler state and then expose a failure. We present Rondo, a stateful testing method that represents each test case as a model paired with an invocation sequence. Rondo mutates invocation sequences and uses a Markov chain Monte Carlo sampler guided by compilation behavior to favor rarely observed behaviors. Its three-way differential oracle compares execution without compilation, compiled execution with compiler state reset, and compiled execution with state retained. It confirms stateful bugs by replaying the same invocation with state retained and reset while preserving inputs and program state, then shortens sequences while preserving the original bug. We evaluate Rondo on four deep learning frameworks using 24 common model architectures. Rondo detects 117 bugs, including 35 stateful bugs. We submit 39 bug reports, including 18 for stateful…

    AIInference & serving70PDF
  • arXiv cs.NIDeveloper toolingOct 15

    Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker

    Douglas Leith

    Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for the protobuf messages that an iPhone sends to Apple's location services. Working from Apple's binaries, we use two frontier models (Claude Opus 4.8, Codex GPT Sol 5.6) to reconstruct the schemas of protobufs observed in network traffic. The great majority of the schemas contain substantial errors, and these are hard to detect because the schemas are internally consistent. Direct AI use is therefore unsuitable for forensic and privacy analysis. AI can, however, write reliable analysis tools when their output can be validated. We use Claude to write a Python tool, Extractor, that reliably recovers protobuf schemas from Apple binaries. We validate it against binaries compiled from schemas with known answers, previously published schemas, and network measurements. We demonstrate the use of Extractor to decode and analyse the location services network traffic of a jailbroken iPhone 8 (iOS 16.7.11).

    AIAI safety60PDF
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Developer tooling

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  • Sensing communication16+23.1%
  • Federated learning40+8.1%
  • Agents500+0.4%
  • Frontier LLMs431−7.5%
  • Learning verifiable rewards165−17.5%
  • Forecasting time75−17.6%
  • Inference & serving248−18.2%
  • Character recognition OCR15−21.1%