Last week, we went over Peter Parker’s best suits in Marvel’s Spider-Man 2. But the game featured another protagonist: Miles Morales. And so today, we’ll go over his best suits. Just like Peter, Miles had some stinkers, but also some gems. Here are his 5 best suits in Marvel’s Spider-Man 2. P.S. I got all these screenshots from this YouTube video. Make sure to check it out! Miles Morales 5 Best Suits in Marvel’s Spider-Man 2 Smoke and Mirrors Suit […]

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In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hybrid approach that integrates 64-dimensional time series embeddings from Granite TinyTimeMixer with 28-dimensional statistical features based on domain knowledge for HVAC equipment anomaly prediction tasks. Specifically, we combine time series embeddings extracted from a Granite TinyTimeMixer encoder fine-tuned with LoRA (Low-Rank Adaptation) and 28 types […]

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​ I am trying to convert XQuery statements into SQL queries within an enterprise context, with the constraint that the solution must rely on locally run LLMs. A key challenge is the limited availability of training data (pairs of XQueries and their corresponding SQL queries), especially with enough diversity to cover different patterns. I initially experimented with a parsing-based approach. The idea was to extract elements such as table names, columns, and conditions from the XQuery (using a […]

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Moved an AI feature into production a few months ago and the cost profile has been a constant surprise since so the demos and the early prototypes ran cheap because the volume was tiny + the prompts were short but when it hit traffic the token usage scaled a lot. I think it was partly because customers ask longer and unclear questions than our test set because we ended up adding context retrieval that doubled the input length […]

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arXiv:2602.22276v1 Announce Type: new Abstract: Software engineering (SE) and requirements engineering (RE) face a significant increase in secondary studies, particularly literature reviews (LRs), due to the ever-growing number of scientific publications. Generative artificial intelligence (GenAI) exacerbates this trend by producing LRs rapidly but often at the expense of quality, rigor, and transparency. At the same time, secondary studies often fail to share underlying data and artifacts, limiting replication and reuse. This paper introduces EmpiRE-Compass, a neuro-symbolic dashboard designed […]

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arXiv:2601.10896v1 Announce Type: new Abstract: LLMs are increasingly used as third-party judges, yet their reliability when evaluating speakers in dialogue remains poorly understood. We show that LLMs judge identical claims differently depending on framing: the same content elicits different verdicts when presented as a statement to verify (“Is this statement correct?”) versus attributed to a speaker (“Is this speaker correct?”). We call this dialogic deference and introduce DialDefer, a framework for detecting and mitigating these framing-induced judgment shifts. […]

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Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means the model was trained from the ground up to process and reason across text and visual inputs simultaneously — not a vision module […]

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Hi everyone, I recently published a paper on arXiv introducing a new ensemble learning framework called EARCP: https://arxiv.org/abs/2603.14651 EARCP is designed for sequential decision-making problems and dynamically combines multiple models based on both their performance and their agreement (coherence). Key ideas: Online adaptation of model weights using a multiplicative weights framework Coherence-aware regularization to stabilize ensemble behavior Sublinear regret guarantees: O(√(T log M)) Tested on time series forecasting, activity recognition, and financial prediction tasks The goal is to […]

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