AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,786 papers
This work isolates SLM graph reasoning from navigation and traceability capability by employing the THESEUS navigation and traceability framework and using frozen, off-the-shelf SLMs as local action policies, and results motivate evaluating SLM graph reasoning beyond endpoint accuracy alone.
This survey connects Articulated, Inner and Perceived tasks to the neural populations they engage, the representations available to decoders and the outputs those representations can support, and outlines a prospective five-level trajectory from commands and language to meaning, scenarios and bidirectional cognitive ex...
Findings and error-scope analyses show that benchmark agreement reflects report size and error definitions as well as medical error detection, and local RadMatch achieves stronger agreement on clinically significant errors in both expert datasets and on total errors in the shared RadEvalExpert subset.
J-ICL is proposed, a post-training framework that incorporates context validation into the training objective and improves ICLEval and reality accuracy by an average of 5.84 percentage points and reality accuracy by 9.20 points over the corresponding base models.
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This work introduces Multiword Expression Effective Context Length (MWE-ECL), a bilingual diagnostic whose matched anchor-retrieval, no-anchor prior, and interpretation prompts measure explicit recoverability, model-observed defaults, and anchor-conditioned decisions, respectively.
This work develops a reproducible pipeline for cleaning, processing, and parsing Thai text under the Universal Dependencies framework and releases a frequency lexicon and CoNLL-U parses in machine-readable formats suitable for both AI-assisted and conventional programmatic analysis.
This work uses compression rate to evaluate how well models predict new text, and results yield three main findings: compression performance follows a consistent scaling trend with model size, and lower compression rates are strongly associated with higher zero-shot MMLU accuracy.
Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two reviewers extract each study independently, an adjudicator resolves disagreements, and the team keeps an auditable record of how every value was produced. Large langua...
DPara is a PSD framework that reuses effective parallel drafters yet guarantees backbone--verification overlap in every round, thereby eliminating this probabilistic fallback altogether and surpassing the strongest serial and parallel speculative decoding baselines alike.
PRISM-VLM is introduced, a multi-axis discriminative benchmark that scores every item along seven axes covering the recurring failure modes (task quality, behavioral robustness, and capability bottlenecks) and combines them into a single PScore, with items recycled from fifteen public benchmarks.
AraGenre is a shared task on hierarchical, definition-guided Arabic genre classification, motivated by the limited availability of annotated data in Arabic and other low-resource languages. Systems assign each Arabic text segment both a broad communicative genre and a fine-grained specific genre. The released training...
MORSE is introduced, a compression-aware method for evidence-preserving context ordering that improves evidence retention over reverse ordering and generally outperforms matched random search, with downstream QA gains.
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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