AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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9,261 papers
Q-TIE is proposed, a re-ranking framework based on learned Temporal Intent Extraction that generalizes beyond predefined rules via model-based learning while explicitly modeling temporal constraints as a separate signal - jointly achieving what each paradigm typically trades off.
Disaster-risk-reduction archives describe hazard events in prose that databases such as EM-DAT (Delforge et al., 2025) cannot ingest directly. We present an LLM pipeline that generates candidate georeferenced event records using a controlled hazard vocabulary and fixed schema, retaining evidence for review. Applied to...
A comprehensive benchmark of Federated Learning for multilingual Automatic Speech Recognition (ASR) for multilingual Speech-LLM architectures, evaluating four Speech-LLM architectures on the Multilingual LibriSpeech dataset shows that optimized learning rates are critical for performance.
While test-time scaling enhances Large Language Model (LLM) agents in long-horizon software engineering (SWE), sparse binary rewards (Pass/Fail) create a severe credit assignment crisis and waste failed exploratory trajectories. Current trajectory optimization and scaling methods are costly and structurally limited, re...
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It is found that CD eliminates all structural failures across all models, but content accuracy reveals a persistent semantic gap that is scale-dependent: type coercion failures are fully CD-rescuable, while instruction-semantic failures remain CD-resistant.
Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defend a legal position, reason under incomplete information, or synthesize multiple statutory provisions. This gap is particularly pronounced for Canadian law, which remain...
It is found that while efficiency methods seemingly reduce the success rate of jailbreak attacks, this improvement is often superficial and largely arises from degraded reasoning capabilities leading to"attempted but failed"malicious responses, rather than an increase in genuine alignment.
A simple approach is presented that introduces error supervision into synthetic essay generation by fine-tune an LLM generator on error-annotated texts of the kind commonly used in Grammatical Error Detection (GED).
A hierarchical rotary positional encoding (hRoPE) that represents paragraph, sentence, and token indices as separate channels, hold the token sequence fixed, intervene on the paragraph coordinate $p_1$, and measure cross-paragraph attention with a token-distance-exact estimator is used.
BabelFlow is introduced, a benchmark-general agentic workflow that adapts existing agent benchmarks to new languages by analyzing runtime dependencies, coordinating structure-preserving translation, and combining multi-layer verification with human review to preserve task and evaluation semantics.
RPMem is introduced, a two-stage architecture that compiles each session into a model-independent latent memory through forward computation and selectively integrates it with retained memory via a task-trained recurrent gate, establishing RPMem as a lifecycle-independent parametric memory framework that maintains evolv...
This work introduces EGMEMORY, which formulates long-horizon multi-actor memory as a searchable state machine that separates persistent message-level evidence from an explicit active state.
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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.
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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.
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