AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,685 papers
The results show domain-specific changes associated with using recursive synthetic-data training in biomedical QA, but do not establish clinical hallucination rates or universal model collapse.
With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. T...
This work proposes RewardExplainer, a framework that obtains feedback from the target reward model through counterfactual rewriting and uses this feedback to further optimize the explainer, enabling the explainer to more faithfully capture the target RM's scoring preferences and sensitive behaviors than single-pass gen...
High-level text preprocessing is introduced: a systematic, rule-based intervention applied before the standard preprocessing pipeline to isolate each document's actual claim from its discursive structure and introduces the semantic diffusion index (SDI), a per-document metric for assessing the semantic reorientation be...
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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.
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.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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
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