Artificial intelligence is changing how we build software. Just a few years ago, writing code that could talk, decide, or use external data felt hard. Today, thanks to new tools, developers can build smart agents that read messages, reason about them, and call functions on their own. One such platform that makes this easy is LangChain. With LangChain, you can link language models, tools, and apps together. You can also wrap your agent inside a FastAPI server, then […]

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Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning methods often rely on performance-degradation heuristics, such as loss maximization or random labeling. However, these signals can be poorly conditioned, leading to unstable optimization and harming the model’s generalization. We argue that unlearning should instead prioritize distributional indistinguishability, aligning the model’s behavior on forget data with its behavior on truly unseen data. Motivated by this, we propose Reference-Guided […]

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arXiv:2601.16273v1 Announce Type: new Abstract: This technical report describes our submission to the ICME 2025 audio encoder challenge. Our submitted system is built on BEATs, a masked speech token prediction based audio encoder. We extend the BEATs model using 74,000 hours of data derived from various speech, music, and sound corpora and scale its architecture upto 300 million parameters. We experiment with speech-heavy and balanced pre-training mixtures to study the impact of different domains on final performance. Our […]

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Sen. Bernie Sanders thinks he’s tricked Claude into revealing the AI industry’s secrets, but he really just exposed how agreeable chatbots can become.

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arXiv:2502.03366v4 Announce Type: replace-cross Abstract: In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian inference methods have been proposed. We develop a common formalism to describe such methods, which we view as outputting Gaussian distributions over the logit space. Predictives are then obtained as the expectations of the Gaussian distributions pushed forward through the softmax. However, such softmax Gaussian integrals […]

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This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feature extraction, clustering, and threshold estimation to identify abnormal appliance behavior. To avoid retraining models on every device, we introduce the Distributed Internet of Learning (DIoL), which enables a model trained on one MCU to be exported as a portable, text-based representation and reused directly on other […]

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arXiv:2601.17109v1 Announce Type: new Abstract: Health information seeking has fundamentally changed since the onset of Large Language Models (LLM), with nearly one third of ChatGPT’s 800 million users asking health questions weekly. Understanding the sources of those AI generated responses is vital, as health organizations and providers are also investing in digital strategies to organically improve their ranking, reach and visibility in LLM systems like ChatGPT. As AI search optimization strategies are gaining maturity, this study introduces an […]

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arXiv:2603.12298v1 Announce Type: new Abstract: Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from static activation differences are susceptible to high-dimensional noise and layer-wise semantic drift, often capturing spurious correlations rather than the target intent. To address this, we propose Global Evolutionary Refined Steering (GER-steer), a training-free framework that grounded in the geometric stability of the network’s representation evolution. GER-steer exploits this global signal to […]

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arXiv:2602.17687v1 Announce Type: new Abstract: AI systems have achieved remarkable success in processing text and relational data, yet visual document processing remains relatively underexplored. Whereas traditional systems require OCR transcriptions to convert these visual documents into text and metadata, recent advances in multimodal foundation models offer retrieval and generation directly from document images. This raises a key question: How do image-based systems compare to established text-based methods? We introduce IRPAPERS, a benchmark of 3,230 pages from 166 scientific […]

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arXiv:2310.09488v2 Announce Type: replace Abstract: Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships. As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate LTSF Transformers to model series-wise relationships: the characteristic differences between series are often captured incorrectly. To address this, we introduce ARM: a multivariate temporal-contextual adaptive learning method, which is an enhanced […]

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