AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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CounterCount goes beyond showing that VLMs fall back on knowledge priors, revealing that once a familiar context is recognized, models favor prior knowledge over available visual evidence rather than failing to encode that evidence.
Test-time training (TTT) lets a model store information in its weights during inference. When the model learns from its own output, however, each update also changes the model that generates the next training example. Across 128K-token streams, retaining generated-text updates worsens prediction on independent human-wr...
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network protocols and classical shadows, while both methods have limitations: the former suffers from incompatible dataset construction rules, resul...
Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchmarks present the complete image and question at once. We ask what models lose when the same information is fragmented. We introduce Layered-VQA, with 93 scenes and 300...
This work proposes a large-scale multimodal large language model-based supervision generation framework that improves supervision diversity, fidelity, and semantic coverage, and introduces a granularity-aware text representation with separate CLS tokens for summary and detailed views.
Dialectal Arabic machine translation (MT) remains challenging despite recent progress in Arabic language technologies, particularly because effective translation requires modeling not only semantic content but also dialectal variation, conversational context, speaker and addressee characteristics, and sociolinguistic a...
An event-aware evaluation protocol that separately measures post-trigger editing quality, pre-trigger preservation, and false-trigger robustness is introduced.
ContextFlow is introduced, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning and adapts perceiver-style multimodal context compressors that distill visual, proprioceptive, and action sequences into compact, task-relevant latent representations.
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
B-MIM is introduced, a modification of the iBOT objective that stochastically reduces global semantic alignment to prioritize local patch reconstruction and suggests that reducing global semantic pressure during pretraining enhances generalization to intricate anatomical structures.
This work studies an inference-time substitution of the row-wise softmax in the final visual self-attention layers with the $\alpha$-entmax transform, applied across both the standard query-key attention and self-correlation variants.
GenView is presented, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics, and an adaptive view generation method that dynamically adjusts the noise level in sampling to ensure the preservation of essential semantic meani...
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