AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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8,786 papers
Self-Play Pretraining with Zero Data is introduced, an initial proof-of-concept towards realizing a more general approach to pretraining, and whether zero-shot performance on natural data improves predictably with self-play compute is tested.
If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude, and instances of one model monitoring each other could collude.
This work assembles Gold-50, fifty real product and policy episodes whose real-world outcome is known, adjudicated against the public record, and scores the five-way release verdict against it, finding most of the measured gap between frontier cloud models and open-weight models fine-tune and serve offline is attributa...
PUBG Ally is introduced, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate that combines agentic tool use with real-time game control.
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This work constructs a large-scale dataset from over 100K computer science papers, and trains a goal-conditioned reward model to predict citation-impact labels from research goal and idea pairs, and uses this reward to align an idea generator through supervised fine-tuning followed by reinforcement learning.
C3M is presented, a cross-session multimodal memory organization that maintains a bounded active index over persistent source text-image evidence in a bounded, query-blind memory budget for cross-session long-horizon tasks.
The results chart an engineering path toward recursive self-improvement, in which humans distill the principles of model building, agents operationalize them through evidence-driven experimentation, and each generation of AI becomes a more capable architect of the next.
PartHackBench provides a certified control for testing whether evaluator credit changes while all benchmark-defined task-relevant progress remains fixed, and provides a certified control for testing whether evaluator credit changes while all benchmark-defined task-relevant progress remains fixed.
Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended interactions. However, existing research lacks auditable, multi-turn, multi-factorial experiments that quantify LLM behavior under explicit constraints, with time-resol...
Together, the survival and rationale analyses reveal distinct temporal reliability regimes and model-specific"failure fingerprints", offering an evaluation lens for diagnosing inconsistency in multi-turn agent behavior.
RLCDAlignBench is presented, which benchmarks Jev on ten alignment failures: sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty, and power seeking.
CAR-bench evaluates whether tool-using agents stay reliable under real-world uncertainty, executing every tool inside the evaluator so that each tool-result exchange is a separate agent round-trip. A conventional next-action agent can batch parallel tool calls, but a chain of dependent calls costs it one model call per...
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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