As we move into 2026, the demand for fast, reliable, and real-time financial data continues to grow. Developers, analysts, and financial institutions increasingly rely on accurate market insights to build smarter tools and make informed decisions. Financial APIs are now transforming how market data is accessed, analyzed, and applied across global markets. Whether you are building a next-generation personal finance application or powering a high-frequency trading system, Stock Market Data APIs and Forex Data APIs play a critical […]

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What Healthy On-Call Looks Like Incident response gets most of the attention, but teams rarely measure the human cost of on-call with the same rigor they apply to incidents. Phone charged. Laptop by my bed. The distinctive PagerDuty alarm goes off in the middle of the night, and the process begins. I sit up, open my screen to a burst of blinding light, and try to figure out: is this transient, or is something actually broken? I pull […]

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arXiv:2510.08646v2 Announce Type: replace-cross Abstract: Safety alignment of large language models currently faces a central challenge: existing alignment techniques often prioritize mitigating responses to harmful prompts at the expense of overcautious behavior, leading models to incorrectly refuse benign requests. A key goal of safe alignment is therefore to improve safety while simultaneously minimizing false refusals. In this work, we introduce Energy Landscape Steering (ELS), a novel, fine-tuning free framework designed to resolve this challenge through dynamic, inference-time intervention. […]

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arXiv:2604.09803v1 Announce Type: new Abstract: Multimodal music creation requires models that can both generate audio from high-level cues and edit existing mixtures in a targeted manner. Yet most multimodal music systems are built for a single task and a fixed prompting interface, making their conditioning brittle when guidance is ambiguous, temporally misaligned, or partially missing. Common additive fusion or feature concatenation further weakens cross-modal grounding, often causing prompt drift and spurious musical content during generation and editing. We […]

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As skill gaps continue to create headwinds for US businesses fostering growth ambitions, the cause for blockchain adoption and the ability to create smart contracts for more hiring efficiency are growing. According to a report from the McCourt School of Public Policy’s Center on Education and the Workforce (CEW), the United States needs 5.25 million additional workers with training beyond high school through 2032, and 4.5 million will require at least a bachelor’s degree. The report, entitled ‘Falling […]

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arXiv:2601.00086v2 Announce Type: new Abstract: Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows. This highlights the need for effective adaptation to task-specific tools. We propose RIMRULE, a neuro-symbolic approach for LLM adaptation based on dynamic rule injection. Compact, interpretable rules are distilled from failure traces and injected into the prompt during inference to improve task performance. These rules are proposed by the […]

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arXiv:2603.25892v1 Announce Type: new Abstract: We present THFM, a unified video foundation model for human-centric perception that jointly addresses dense tasks (depth, normals, segmentation, dense pose) and sparse tasks (2d/3d keypoint estimation) within a single architecture. THFM is derived from a pretrained text-to-video diffusion model, repurposed as a single-forward-pass perception model and augmented with learnable tokens for sparse predictions. Modulated by the text prompt, our single unified model is capable of performing various perception tasks. Crucially, our model […]

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In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a substantial source of variability often goes undetected. Even when the data and model architecture are held fixed, randomness introduced by optimization and initialization can lead to materially different risk estimates for the same patient. This problem is largely obscured by standard evaluation practices, […]

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