AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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7,776 papers
Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which diminishes their advantage in practice. This has led to massive interest in and rapid development of new methods for effective few-step generation. We show that much of the s...
Redline is a finite-sample procedure that selects operating points, hand-picked or learned, from the correctness of their answers on calibration prompts, and keeps the reference-relative risk, the joint probability that the reference answers correctly and a candidate configuration does not, within a user-chosen budget...
Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate. We show that speaking rate can be steered at inference time, without retraining, by clamping a single decoder block's activation along a discovered speed axis. A decoder-block analysis recov...
Redwing is proposed, a design principle for robust token-level watermarking that generalizes to TTS models at a speech-quality cost close to that of KGW and shows that retokenization is not merely a source of noise: its transition structure can be exploited as a design principle for robust token-level watermarking.
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This work identifies what must be preserved beyond the replaced value when using a KV cache as updatable memory and recovers distinctions missed by fixed cache similarities on synthetic record pairs.
This work identifies three node categories (common temporal, common to the year, and chrono-semantic) which interact to generate a temporal filter during factual recall and provides the first feature-level map of temporal recall by isolating individual MLP features via transcoder circuit tracing.
CAME (Company-Aware Evidence-Memory Experts), a residual-forecasting framework that refines a no-leakage statistical anchor when current semantic evidence and prior error patterns justify an adjustment, is introduced.
What enters an LLM's context can influence its decision even when it should not determine it, and exposure systematically shifts decisions, with effects reaching nearly 50 percentage points in closed-weight models.
Across twelve models, dense and hybrid, the authors move a hidden state from one story into another where the two routes lead to different places, and read off which place the model gives, which makes the answer a handoff.
The USAI-Quant benchmark is developed, the first benchmark designed to quantitatively evaluate VLM's reasoning capabilities on built environment metrics via remote sensing imagery, and reveals that current state-of-the-art models consistently fall short on numeric reasoning tasks.
OpenTumorBoard is introduced, a benchmark with 611 patient cases and 19,157 discussion turns across ten specialist roles, transcribed from 12,534 minutes of publicly available tumor board recordings on YouTube, and automated curation pipeline is released to support the development and evaluation of LLMs for multidiscip...
KV-Lingo can be used for seamless model switching, staying close to re-prefill across repeated switches in the authors' multi-turn evaluations, and is found particularly useful for dynamic model routing.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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