AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,685 papers
This work proposes ER-Audit, a two-stage black-box auditing framework that compares frozen verifier checkpoints before and after adaptation, and introduces two evaluation benchmarks pairing monitored and hidden task prompts grounded in shared contexts.
This work introduces compositional controllability to address compositional controllability in pipeline performance, and bounds the gap between observed and controlled score differences using only nuisance outside the window.
TRAP, a one-sided penalty on tokenwise TRA that acts only where the target model pulls ahead of its reference, brings memorization near the level of an untrained model at little utility cost, where generic regularizers barely move and differential privacy gives up most of what fine-tuning bought.
This framework is adopted to systematically analyse assumptions in recent research on culture in NLP along the full pipeline of task selection, data collection, system design and evaluation.
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This work reviews 28 papers posted between 19 and 24 September and relates their findings to earlier work on label-probability classification, constrained decoding, reranking, calibration, and model cascades and uses recurring weaknesses in these studies to derive a 14-item evaluation checklist for future TDM work.
A framework for evaluating and improving a large-scale, multi-agent shopping assistant in production in production is presented, and lessons from its use are reported.
This survey formalizes persistent evidence-grounded document state as a unifying framework, specifying the operations and invariants required to convert multimodal evidence into durable, provenance-linked state.
Communication between two frozen large language models from different providers, with different tokenizers, accessed through their API endpoints is studied, finding that successful place value communication in some runs is rare.
The results confirm the hypothesis that LLMs, when instructed through Prompt Engineering, can achieve performance comparable to or superior to manual extraction by health professionals, and demonstrate its potential as a complementary verification tool alongside specialists.
While modern text-to-speech (TTS) systems generate highly natural speech and support inline non-verbal vocalization (NVV) tags, accurate control over these events remains challenging. A key gap is the lack of established post-training methods for non-verbal control in continuous autoregressive flow-matching TTS. To thi...
A training-free method that reformulates question--frame matching as comparing frames against a set of dynamically generated visual cues, showing that agentic cue refinement drives active re-exploration of the video, yielding relative similarity gains of up to 92% over the initial context.
This work aims to promote broader adoption of OT in speech processing by reviewing OT foundations through intuitive physical interpretations and highlighting connections to modern generative models, and demonstrating OT applications in cross-domain and cross-modal speech tasks.
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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