AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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19,710 papers
The results demonstrate the effectiveness of complementary optimization strategies and the interpretability of optimal vocabulary-based discrimination in the MRBSSP.
The BABA-Bench, a benchmark of 47 interactive worlds, built from a de-identified, real patient advocacy case that expert advocates reviewed, is introduced, a benchmark of 47 interactive worlds for LLM agents.
The work demonstrates LanG's practical scalability, processing 3.33 million records in 6.4 minutes, over 30 million hourly, and its safeguards resisted prompt injection, label poisoning and ledger tampering, so small local models can carry most SOC detection affordably and safely.
It is found that LLM team with a dedicated external conflict resolution protocol results in fewest breaches, and is also found that LLM team with a dedicated external conflict resolution protocol results in fewest breaches.
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Capability-Grounded Agentic Planning (CGAP) is presented, which compiles the tool catalog deterministically into a PDDL model, uses an LLM to turn the request into goals, and solves the model with a classical planner.
When a language-model agent fails a multi-step task, can another model find the decisive mistake and fix it with a single action? We introduce ReplayRCA, a benchmark that scores such diagnoses by intervention rather than by agreement with annotations: the environment is restored to the state preceding the chosen step,...
A pilot expert workshop used to explore violet teaming as a governance methodology for biodefense-relevant AI capabilities and described observations from the exercise, summarizes the workshop design, outputs, and lessons learned.
Noura is presented, which pairs LLM planning with executable personal specifications instantiated from public clinical guidance and individual health and dietary profiles, and turns executed plans and contextually credited revisions into supervision for compact models, recovering targets even from trajectories that nev...
Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and wh...
The implemented prototype separates deterministic planning from large language model generation, represents content and delivery parameters explicitly, shapes each script through a four-phase attention taper, validates structural and lexical constraints, performs a bounded error-aware retry, and quarantines unresolved...
P-ContextBench provides measurable targets for improving the accuracy, reliability, and safety of LLM-based personal agents, a realistic, verifiable, and scalable benchmark for evaluating personal-context understanding.
It is observed that models can form statistical estimates of the monitor's effectiveness and use these estimates to guide attempts to evade detection, and highlight the need to evaluate CoT-based oversight under adaptive conditions, where the monitoring process itself may influence the behavior it seeks to observe.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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