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Reasoning on a Budget: Smarter AI with Self-Generated Data

20.01.2026 by qfx

Across evaluations on AIME, GPQA, and NaturalPlan, a prompting technique utilizing preference data-designated by a red line in comparative analyses-consistently enhances the reasoning capabilities of models including Grok-3, Grok-3-mini, and GPT-4.1, demonstrating improved performance regardless of token budget limitations and suggesting an inherent ability to foster more robust anytime reasoning.

New research demonstrates a method for large language models to improve their problem-solving abilities during the reasoning process, even with limited computational resources.

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Seeing Trouble Before It Starts: AI-Powered Health Checks for Helicopters

20.01.2026 by qfx

A new study explores how artificial intelligence can predict helicopter engine failures without relying on costly and often unavailable labeled failure data.

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Decoding Harmful Intent: Probing Large Language Models for Cyber Threats

19.01.2026 by qfx

Through architectural refinements and training optimizations, a probing system-specifically a Max of Rolling Means Attention Probe-achieves performance exceeding that of large language models at a cost reduction exceeding 10,000×, and further diminishes error rates when strategically deferring to an LLM like Gemini 2.5 Flash for nuanced cases, demonstrating that even highly complex systems benefit from targeted delegation and a focus on minimizing false positive errors during decision-making-a principle validated through weighted error rate analysis on test data and reflected in the system’s positioning relative to the Pareto frontier.

New research explores how analyzing internal model states can effectively detect malicious prompts targeting large language models, offering a practical defense against emerging cybersecurity risks.

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Beyond Benchmarks: The Rise of Recursive Reasoning in AI

19.01.2026 by qfx

The task demonstrates an example of an ARC-AGI challenge.

The ARC Prize 2025 technical report details significant progress toward artificial general intelligence, revealing how systems are learning to improve themselves through iterative refinement.

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Learning What’s Normal to Find What Isn’t

19.01.2026 by qfx

The analysis of PageBlocks and Thyroid datasets reveals that initial phases of model training-warm-up and polarization-are characterized by fluctuating risks of both accepting incorrect data (inliers) and rejecting correct data (outliers), suggesting an inherent instability before convergence.

A new active learning framework boosts outlier detection by first mastering the characteristics of normal data.

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Reading Minds for Mood: AI Spots Depression in Brain Signals

19.01.2026 by qfx

A new study demonstrates the potential of artificial intelligence to detect depression by analyzing electroencephalography (EEG) data, offering a promising avenue for objective diagnosis.

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Spot the Fake: A New Dataset to Combat AI-Generated Video

19.01.2026 by qfx

DeepSeek-VL-2 demonstrates a discernible preference among evaluation metrics when assessing the accuracy of AI-generated video detection, suggesting the model's performance is not uniformly consistent across all assessment standards.

Researchers have released a comprehensive benchmark to help detect increasingly realistic videos created by artificial intelligence.

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Beyond Surface-Level Explanations: Making Graph AI Truly Understandable

19.01.2026 by qfx

The self-reflection framework reveals a performance decline correlated with increasing levels of spurious correlation-specifically, as the correlation coefficient [latex]b[/latex] rises from 0.5 to 0.9, the system’s efficacy diminishes, demonstrating the framework’s sensitivity to deceptive patterns within the data.

A new self-reflection framework helps graph neural networks identify and eliminate misleading correlations, leading to more reliable and consistent explanations.

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Stories in Data: Unlocking Narrative Insights

19.01.2026 by qfx

An interactive interface facilitates narrative analytics through a semantic map visualization, enabling knowledge integration and direct manipulation of the underlying narrative structure.

A new approach combines automated text analysis with human expertise to make sense of complex stories hidden within large collections of text.

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The Illusion of Progress: How AI’s Promise Can Distort Markets

19.01.2026 by qfx

The expansion of technological options within competitive environments introduces a complex dynamic where gains for one agent can coincide with losses for another, a phenomenon amplified by instances where the new technology isn’t even <i>used</i> but still alters strategic incentives-creating a “Poisoned Apple” effect-and regulatory outcomes frequently demonstrate that improvements in fairness or efficiency are linked to widespread adoption, while stagnation or even harm to these metrics often arise when the technology exists as an unrealized potential, highlighting the critical need for adaptive market designs to mitigate risks associated with technological inertia.

The mere presence of advanced artificial intelligence, even if unused, can create incentives for strategic manipulation of regulatory systems and ultimately shift market dynamics.

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