AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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20,463 papers
Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models mus...
This paper studies agent memory from a self-evolving perspective and introduces EvoMemBench, a unified benchmark organized along two axes: memory scope (in-episode vs. cross-episode) and memory content (knowledge-oriented vs. execution-oriented).
In recent years, open-source efforts like Senorita-2M have propelled video editing toward natural language instruction. However, current publicly available datasets predominantly focus on local editing or style transfer, which largely preserve the original scene structure and are easier to scale. In contrast, Backgroun...
All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneous platform. Even academic PIM/PNM proposals still treat the GPU as the central hub for cross-device communication. Yet the GPU's compute-rich architecture is fundamentall...
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The standard recipe for training reasoning models---SFT on rationale annotations followed by RLVR---relies on rationale annotations being available and clean. When no annotated rationales exist, SFT cannot start at all, and when the model is too unaligned, RLVR alone stalls at cold start; when annotations are weak, SFT...
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate objects, and few generate both. The generated scene layout is often simple even with LLM's help. Moreover, the generated scene is often inc...
Large language model agents rely on a memory harness to write, organize, retrieve, and use past experience. A harness that works for one task often fails on another, because conversations, embodied planning, and expert reasoning require different storage and retrieval behavior. To address this limitation, we introduce...
The results indicate that user-side detection is not a viable defense and motivate system-level countermeasures such as cryptographic content provenance.
Autonomous cyber operations increasingly depend on agents that can chain reconnaissance, exploitation, and analysis across many tools while maintaining state over long task horizons. Large Language Models (LLMs) are promising orchestrators for this workflow, but naive agents that pipe model output directly into a shell...
Safety alignment does not fully prevent large language models (LLMs) from producing harmful content under adversarial attacks. We observe that existing attack methods typically move harmful embeddings toward regions of harmless embeddings. This motivates us to explore whether increasing the distance between harmful and...
This work proposes a specification-aware action distribution optimization framework that enforces a broad class of Signal Temporal Logic constraints during execution of a pretrained robotics foundation model without modifying its parameters.
An open, reproducible biomedical knowledge graph ecosystem built on Samyama, a Rust-native graph database that describes a reproducible ETL and portable .sgsnap snapshot pattern where graphs load alone or compose into one store.
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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