Domain Authority (DA) is one of the most discussed metrics in SEO. While Google has confirmed that it does not use Domain Authority as a direct ranking factor, many websites with higher authority scores tend to perform better in search results. This creates confusion for many SEO professionals. If Google does not use Domain Authority, why do high-authority websites often rank faster and show more stability? The explanation lies in what Domain Authority represents rather than the metric […]

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arXiv:2601.14260v1 Announce Type: new Abstract: Transformers have become central to natural language processing and large language models, but their deployment at scale faces three major challenges. First, the attention mechanism requires massive matrix multiplications and frequent movement of intermediate results between memory and compute units, leading to high latency and energy costs. Second, in long-context inference, the key-value cache (KV cache) can grow unpredictably and even surpass the model’s weight size, creating severe memory and bandwidth bottlenecks. Third, […]

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arXiv:2601.05219v2 Announce Type: replace Abstract: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor. We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme to […]

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arXiv:2602.12321v1 Announce Type: new Abstract: Internet services and applications depend critically on the availability and acc uracy of network time. The Network Time Protocol (NTP) is one of the oldest core network protocols and remains the de facto mechanism for clock synchronization across the Internet today. While multiple NTP infrastructures exist, one, the “NTP Pool,” presents an attractive attack target for two basic reasons, it is: 1) administratively distributed and based on volunteer servers; and 2) heavily utilized, […]

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Deep Research (DR) agents extend Large Language Models (LLMs) beyond parametric knowledge by autonomously retrieving and synthesizing evidence from large web corpora into long-form reports, enabling a long-horizon agentic paradigm. However, unlike real-time conversational assistants, DR is computationally expensive and time-consuming, creating an autonomy-interaction dilemma: high autonomy on ambiguous user queries often leads to prolonged execution with unsatisfactory outcomes. To address this, we propose IntentRL, a framework that trains proactive agents to clarify latent user intents before starting […]

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arXiv:2601.04237v1 Announce Type: new Abstract: We demonstrate SAGE-32B, a 32 billion parameter language model that focuses on agentic reasoning and long range planning tasks. Unlike chat models that aim for general conversation fluency, SAGE-32B is designed to operate in an agentic loop, emphasizing task decomposition, tool usage, and error recovery. The model is initialized from the Qwen2.5-32B pretrained model and fine tuned using Iterative Distillation, a two stage training process that improves reasoning performance through rigorously tested feedback […]

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arXiv:2603.24652v1 Announce Type: new Abstract: Network pruning, which removes less important parameters or architectures, is often expected to improve efficiency while preserving performance. However, this expectation does not consistently hold across language tasks: pruned models can perform well on non-generative tasks but frequently fail in generative settings. To understand this discrepancy, we analyze network pruning from a representation-hierarchy perspective, decomposing the internal computation of language models into three sequential spaces: embedding (hidden representations), logit (pre-softmax outputs), and probability […]

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arXiv:2602.17700v1 Announce Type: new Abstract: Differentiable Neural Architecture Search (NAS) provides efficient, gradient-based methods for automatically designing neural networks, yet its adoption remains limited in practice. We present MIDAS, a novel approach that modernizes DARTS by replacing static architecture parameters with dynamic, input-specific parameters computed via self-attention. To improve robustness, MIDAS (i) localizes the architecture selection by computing it separately for each spatial patch of the activation map, and (ii) introduces a parameter-free, topology-aware search space that models […]

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arXiv:2604.02391v1 Announce Type: new Abstract: Audio-Visual Navigation (AVN) requires an embodied agent to navigate toward a sound source by utilizing both vision and binaural audio. A core challenge arises in complex acoustic environments, where binaural cues become intermittently unreliable, particularly when generalizing to previously unheard sound categories. To address this, we propose RAVN (Reliability-Aware Audio-Visual Navigation), a framework that conditions cross-modal fusion on audio-derived reliability cues, dynamically calibrating the integration of audio and visual inputs. RAVN introduces an […]

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