Noticias de IA - ítems

arXiv:2610.36077v1 Announce Type: new Abstract: Multimodal intelligence can be viewed as human-guided co-evolution between worlds and agents through interaction. However, scaling this process faces two bottlenecks: constructing heterogeneous, verifiable environments is costly, and manual Harness iteration cannot keep pace with growing task diversity and complexity. A key insight is that tasks with different modalities and surface forms can share the same underlying capability requirements. We therefore introduce The Trinity, a recursive self-improvement framework coupling world generation with Harness evolution through three roles: Creator, Explorer, and Evolver. Drawing on verified execution trajectories, Evolver shifts the basis of task organization from surface categories to shared requirements, reorganizing the original task tree into a hierarchical capability tree. We introduce Capability-Driven Real2Sim2Real: for heterogeneous tasks at the same node, Creator abstracts their common requirements into simplified, verifiable simulated worlds for capability learning, while evaluation on original tasks measures transfer. Along this capability tree, Explorer progressively evolves reusable capability modules through selective inheritance, forming a Harness tree whose validated updates accumulate across iterations within a fixed topology. The resulting Harnesses specialize in different capabilities, motivating a Capability-Aware Harness Router that uses public task information to select compatible module configurations from the tree. Our evolved Harnesses improve mean performance by 34 points across 28 training benchmarks and 26 points across 18 unseen benchmarks, including two real-world embodied tasks.

arXiv:2610.35984v1 Announce Type: new Abstract: Deep learning models are highly effective in a wide range of domains, particu larly in plant disease diagnosis. However, their performance is often limited by challenges such as insufficient data and class imbalance. In our work, we propose a comprehensive data generation strategy to overcome these limitations. First, we apply various data generation techniques, including traditional augmentation methods, CutMix, MixUp, and synthetic image generation using Deep Convolu tional Generative Adversarial Networks (DCGANs), to create new data samples. Second, we exploit this generated data in three scenarios: (S1) balancing class distribution in the original dataset, (S2) increasing the number of samples per class, and (S3) expanding the dataset while maintaining balanced classes. Third, we evaluate the impact of these data distributions (S1, S2, S3) on model perfor mance using the EfficientNetV2B0 architecture with pre-trained and fine-tuned weights. The experimental results show a substantial improvement in classifica tion accuracy, emphasizing the critical role of data diversity and class balance. These findings highlight the effectiveness and adaptability of the proposed strat egy of data generation and distribution for enhancing dataset quality, offering promising applications in plant disease detection and precision agriculture.

arXiv:2610.35942v1 Announce Type: new Abstract: I consider what it means for AI assistance to save time. I contrast a stopwatch view, on which time saved is whatever the clock records, with a ledger view, on which it is a counterfactual that has to be read next to the errors the work lets through, and I argue for the latter. The argument has four parts. I define a model-free pair of quantities, time saved and excess escaped defects, and one decision number built from them: the break-even defect cost. I give a baseline model of checking in which, until the model is good enough not to check, a calibrated worker's escaped-defect rate does not depend on how good the model is; checking time grows with the logarithm of the model's error rate; and over-trust buys minutes on the clock at a larger cost in total minutes. I show what randomized studies can and cannot identify, and I replicate METR's developer study from its public data: the same 246 issues support a slowdown of 19% or 24% depending on the estimand (34% without adjusting for a chance imbalance), and AI helped on somewhere between 3% and 82% of them. Finally, I restate the published record, with stated bounds in place of imported point assumptions. In GDPval's own review-every-output workflow, review time alone caps GPT-5's speedup at 3.6x; the consultants outside the frontier were faster, and more often wrong. Last, I test two of the model's predictions in a small pre-specified experiment on AI reviewers checking AI drafts of the same kind of brief, built from SEC filings made after the models' training data were collected. Told the drafter's true error rate, the reviewer checked more as it rose, but all-or-nothing, switching near where its prompt's costs break even. Told that the drafter erred ten times less often than it did, it checked 7 fewer of 9 figures and let a wrong figure through in 22.5% of briefs instead of none: faster and worse, entirely through reduced effort.

En el margen de una semana se celebran dos importantes elecciones en continentes diferentes. Los resultados marcarán la geopolítica y la economía global.

Apple parece decidida a mantener la fórmula de su gama de entrada sin muchos cambios en 2027. Una nueva filtración reveló las principales características del iPhone 18e, el próximo móvil "barato" de la compañía que debutará en la primavera del próximo año. Los primeros datos anticipan que será más potente que su predecesor, pero el […]

Seguir leyendo: Se filtra el iPhone 18e y esta sería la única razón real para comprarlo en 2027

Excel es una de las aplicaciones que más se ha beneficiado de la integración de la IA. La más reciente tiene que ver con una función que permitirá obtener y limpiar datos automáticamente a través de Copilot. Los usuarios solo necesitarán dictar las instrucciones en lenguaje natural y el asistente se encargará de eliminar una […]

Seguir leyendo: Excel quiere ahorrarte horas frente a la pantalla con una nueva IA que hace el trabajo aburrido por ti

UC Berkeley’s LEANN vector index stores a pruned graph and recomputes embeddings at query time. It matches HNSW accuracy at 90% recall using 4 GB instead of 188 GB, at the cost of seconds-level search.

The post Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB appeared first on MarkTechPost.

Liquid AI released LFM3, 6 open-weight 2B-class models for text, vision, audio, omni and decisions. LFM3-1.7B uses a tri-hybrid backbone, a 256K context window and fits in about 1 GB at 4-bit.

The post Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models appeared first on MarkTechPost.

Este año, el MacBook Neo tomó el mercado de portátiles ligeros por asalto. El dispositivo de Apple demostró que un chip de iPhone (y ni siquiera el de última generación) era lo suficientemente potente para ofrecer una experiencia de primera calidad con macOS, sumando a esto un diseño y un precio muy atractivo. El éxito […]

Seguir leyendo: Apple pisa el acelerador con el MacBook Neo 2

Hubo un tiempo en el que Android Auto se utilizaba desde la pantalla del móvil. Años antes de que Google centrara la experiencia en la pantalla del coche, solo bastaba con abrir la aplicación para acceder a una interfaz adaptada a la conducción, con navegación, controles multimedia y más. Si bien Google puso fin a […]

Seguir leyendo: Android Auto vuelve a la pantalla del móvil gracias a esta app gratuita