Noticias de IA - ítems

arXiv:2605.04971v2 Announce Type: replace-cross Abstract: Weight matrices in deep networks exhibit geometric continuity---principal singular vectors of adjacent layers point in similar directions. While this property has been widely observed, its origin remains unexplained. Through experiments on toy MLPs and small transformers, we identify two mechanisms: residual connections create cross-layer gradient coherence that aligns weight updates across layers, and symmetry-breaking nonlinearities constrain all layers to a shared coordinate frame, preventing the rotation drift that would otherwise destabilize weight structure. Crucially, a nonlinear but rotation-equivariant activation reaches less than half of ReLU's continuity, isolating symmetry breaking---not nonlinearity alone---as the active ingredient. Activation and normalization play distinct roles: activation concentrates continuity in the leading singular direction, while normalization distributes it across multiple directions. In transformers, continuity is projection-specific: Q, K, Gate, and Up (which read from the residual stream) develop input-space ($\mathbf{v}_1$) continuity; O and Down (which write to it) develop output-space ($\mathbf{u}_1$) continuity; V alone, lacking an adjacent nonlinearity, develops only low continuity.

arXiv:2605.02525v2 Announce Type: replace-cross Abstract: Semantic indoor navigation requires robots to connect human instructions with spatial destinations, execute missions and retain useful operational experience. This paper presents the Semantic Autonomy Stack (SAS), a six-layer integration framework, and develops its semantic-reasoning, mission-interface and operational-memory layers over a previously validated ROS 2/Nav2 foundation. A deterministic-first cascade combines the route graph, semantic objects, robot state and stored instruction-destination associations, escalating unresolved or guarded cases to a pretrained Vision-Language Model (VLM). Recurrent model-mediated associations can pass a frequency-and-consistency gate and be compiled for deterministic M3 reuse; this gate measures sampled-output stability rather than semantic truth. The frozen v4.8-review implementation was evaluated on two physical robot configurations in one indoor environment, with onboard navigation and perception and shared-workstation reasoning and memory. Of 216 decision cycles outside the start gate, 112 resolved deterministically at sub-millisecond resolver-compute latency. A sealed 28-instruction challenge yielded 17 acceptable outcomes; five fast-path decisions included one incorrect selection. One promoted association was reused on the second platform through deliberate shared-digest installation. A controlled 594-observation offline comparison showed greater agreement with the stored associations and lower decision latency from M3 reuse in five familiar cases. The M3-disabled cascade reduced VLM calls but matched direct-VLM decision quality in standardized context. Large latency savings were concentrated in paths avoiding VLM inference. Together, the results establish a working integration of selective hybrid reasoning and reusable operational memory, while identifying implementation and evaluation boundaries for further development within SAS.

arXiv:2605.01720v4 Announce Type: replace-cross Abstract: Large-scale sign language resources usually pair RGB videos with text and support recognition, translation, and generation. However, RGB frames contain backgrounds, clothing, and other appearance information unrelated to signing motion. Representing the videos as whole-body keypoints such as DWPose removes much of this variation, produces a compact description of body, hand, and facial movement, and matches the input used by recent pose-guided video models. Existing datasets do not provide this representation at scale for in-the-wild sign language videos. We present SignVerse-2M, a multilingual dataset containing approximately two million pose clips from more than 55 sign languages. We process publicly available sign language videos with a shared DWPose pipeline, representing body, hand, and facial motion in the same keypoint format across all clips. The source videos include diverse signers, viewpoints, and recording environments. We also release the processing pipeline, task definitions, and SignDW Transformer baselines. Experiments show that the data supports multilingual text-to-pose modeling and that the generated poses can directly drive several recent pose-conditioned rendering systems.

arXiv:2605.00913v2 Announce Type: replace-cross Abstract: Body-worn sensor networks capture human activities through complementary views, where nodes, modalities, and axes partially describe the underlying motion. In real deployments, hardware faults, wireless dropouts, and loose attachments often cause entire channels or nodes to go missing, while environmental interference degrades the remaining signals. The common impute-then-recognize pipeline introduces reconstruction errors that cannot be observed or corrected at inference. We propose the Missingness-Conditioned Spatio-Temporal Network (MCSTN), an end-to-end framework for robust activity recognition under imperfect sensing. MCSTN has three components: a structured imperfection generator that simulates point, temporal-block, axis, modality, and node failures, plus noise at varying signal-to-noise levels; a mask-aware representation that conditions node embeddings, attention, and temporal aggregation on the availability mask; and a complete-to-imperfect multi-view learning scheme where an exponential-moving-average teacher guides multiple degraded student views through semantic consistency. The encoder models temporal and cross-feature dependencies in parallel: a bi-directional selective state-space branch captures long-range temporal dependencies, a transposed-attention branch models cross-channel correlations, and adaptive weights fuse their outputs. At inference, the generator, teacher, and consistency loss are removed, so MCSTN predicts directly from the observed window and its mask without reconstruction or noise-level estimation. On PAMAP2, Opportunity, and WISDM, MCSTN achieves average macro-F1 scores of 94.24\%, 91.67\%, and 96.22\% under imperfect observations, the best among all compared methods on each dataset, and the best in every failure mode on Opportunity. It requires fewer than one million parameters, with 3.78~ms inference latency per sample.

arXiv:2604.28075v3 Announce Type: replace-cross Abstract: Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English languages like German, French, or Japanese, aggressive filtering creates a strategic dilemma: should practitioners prioritize diversity by training once on large amounts of lightly filtered web data, or prioritize quality by strictly filtering for a high-quality core and repeating it over multiple epochs? We investigate this trade-off for German by applying hierarchical quality filters to 500M web documents, comparing multi-epoch training on filtered subsets against single-pass training on a diverse corpus. Our experiments across multiple model scales and token budgets show that repeating high-quality data consistently outperforms single-pass training on larger, less filtered sets. Notably, the performance gap persists even after 7 epochs. Our findings suggest that for non-English LLMs, semantic concentration through quality filtering offers a more viable path to efficient language modeling than simply maximizing unique data volume. We release our German language models (called Boldt), as well as our cleaned evaluation benchmarks to the research community. Our experiments indicate that they achieve state-of-the-art results despite training on 10-360x fewer tokens than comparable models.

arXiv:2604.25370v3 Announce Type: replace-cross Abstract: The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter Dataset, the first published dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21, 2026 release. Leveraging the Twitter API v2 and a multi-stage curation pipeline spanning multilingual text heuristics (English, Japanese, and Chinese), browser-automated Twitter "Made with AI" badge verification, and model name variant matching, we curate 10,217 confirmed GPT-image-2 images from 27,662 collected records over a six-day window. We characterize the dataset across four analyses: CLIP-based zero-shot subject taxonomy, OCR text legibility (82.0% of images contain detectable text), face detection (59.2% of images, 22,583 total faces), and semantic clustering (137 CLIP ViT-L/14 clusters). A key negative result is that C2PA content credentials are systematically stripped by Twitter's CDN on upload, rendering cryptographic provenance verification infeasible for social-media-sourced AI images. The dataset and all curation code are released publicly.

arXiv:2604.22868v2 Announce Type: replace-cross Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal-centric lens. While recent research demonstrates the promise of fully visual approaches, they suffer from significant computational inefficiency due to the step-by-step planning-by-generation paradigm. In this work, we present EAR, an editing-as-reasoning paradigm that reformulates visual planning as a single-step image transformation. To isolate intrinsic reasoning from visual recognition, we employ abstract puzzles as probing tasks and introduce AMAZE, a procedurally generated dataset that features the classical Maze and Queen problems, covering distinct, complementary forms of visual planning. The abstract nature of AMAZE also facilitates automatic evaluation of autoregressive and diffusion-based models in terms of both pixel-wise fidelity and logical validity. We assess leading proprietary and open-source editing models. The results show that they all struggle in the zero-shot setting, finetuning on basic scales enables remarkable generalization to larger in-domain scales and out-of-domain scales and geometries. However, our best model that runs on high-end hardware fails to match the zero-shot efficiency of human solvers, highlighting a persistent gap in neural visual reasoning.

arXiv:2604.18835v2 Announce Type: replace-cross Abstract: We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwise document comparison. We analogize this as a needle-in-a-haystack problem: a single semantically altered sentence (the needle) is embedded within surrounding context (the hay), and we vary the perturbation type (negation, conjunction swap, named entity replacement), context type (original vs. topically unrelated), needle position, and document length across all combinations, testing seven LLMs on tens of thousands of document pairs. Our analysis reveals several striking findings. First, LLMs can exhibit a within-document positional bias distinct from previously studied candidate-order effects: some models penalize semantic differences more harshly when they occur earlier in a document. Second, when the altered sentence is surrounded by topically unrelated context, it raises similarity scores in almost all settings. This is consistent with a contradiction account, in which topically related context allows the alteration to conflict with the surrounding sentences. Third, all models exhibit the same hierarchy in how harshly they score different perturbation types, with negation penalized the most, but express this hierarchy on significantly different scales. Finally, varying the prompt's conception of semantic similarity across several alternatives changes the magnitude but not the direction of these effects. Together, these results demonstrate how LLM semantic similarity scores are sensitive to the aforementioned parameters in ways that go beyond the semantic change itself; our framework offers a practical toolkit for auditing and comparing scoring behavior across current and future models.

arXiv:2604.18161v2 Announce Type: replace-cross Abstract: In policy gradient reinforcement learning, access to a differentiable model enables 1st-order gradient estimation that accelerates learning compared to relying solely on derivative-free 0th-order estimators. However, discontinuous dynamics cause bias and undermine the effectiveness of 1st-order estimators. Prior work addressed this bias by constructing a confidence interval around the REINFORCE 0th-order gradient estimator and using these bounds to detect discontinuities. However, the REINFORCE estimator is notoriously noisy, and we find that this method requires task-specific hyperparameter tuning and has low sample efficiency. This paper asks whether such bias is the primary obstacle and what minimal fixes suffice. First, we re-examine standard discontinuous settings from prior work and introduce DDCG, a lightweight test that switches estimators in nonsmooth regions; with a single hyperparameter, DDCG achieves robust performance and remains reliable with small samples. Second, on differentiable robotics control tasks, we present IVW-H, a per-step inverse-variance implementation that stabilizes variance without explicit discontinuity detection and yields strong results. Together, these findings indicate that while estimator switching improves robustness in controlled studies, careful variance control often dominates in practical deployments.

arXiv:2604.16677v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers that map visual observations and language instructions to continuous actions. However, pretrained VLAs typically provide limited information about the reliability of their generated actions, making deployment challenging when execution failures must be anticipated. We address this problem by introducing SentryVLA, a lightweight runtime wrapper for frozen VLA policies. At the action level, SentryVLA uses conformalized quantile regression to estimate a calibrated upper bound on the deviation between sampled VLA actions and expert-like behavior under the calibration distribution. The predicted deviation is then used to rank multiple stochastic candidates for action selection. At the execution-monitoring level, SentryVLA applies a Mahalanobis-distance-based anomaly score to the contextualized latent execution state extracted before final action prediction, providing a complementary signal for detecting abnormal execution states during closed-loop control. The framework does not require retraining or architectural modification of the underlying VLA policy. Experiments in simulation and on a physical robot show that SentryVLA improves action selection and provides useful runtime warning signals for abnormal execution states. These findings demonstrate the practical value of post-hoc reliability scoring for improving the runtime behavior of pretrained VLA policies. Project webpage: https://robotic-vision-lab.github.io/sentryvla/.