AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,926 papers
This work adds to the Kathleen trunk a second memory layer -- a"notebook": a fixed-key holographic (HRR) associative store with a learned local write gate, a self-gating raw read, and write-triggered forgetting -- 25K parameters that attach to the logits of any trunk.
CREST is proposed, an inference-time alignment method that steers base model hidden representations using safety directions extracted from a guidance model of any family, avoiding token-level structural limitations entirely and outperforming baselines by up to 22.2\% on safety benchmarks.
This work quantifies the interaction between prosodic features and syntactic representations as their mutual information, and provides a general-purpose framework for estimating this quantity over large speech-text corpora using multimodal language models.
VIBE is introduced, a novel text-and-video-to-music (T+V2M) generation model that leverages a depth-wise cross-layer conditioning mechanism that dynamically bridges the planning and diffusion refinement heads and a comprehensive reward modeling taxonomy, optimizing for both hard, verifiable constraints and soft, subjective qualities with a structured 5-stage training curriculum.
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This work study how a fitted classifier and an LLM can be combined for credit-default prediction, and recommends a simple classifier-guided prompt for LLM-based credit prediction.
Balance of Benchmarks (BoB) is introduced, which embeds benchmark descriptions and assigns each benchmark an inverse-density semantic weight, providing a principled foundation for task-aware and multiplicity-robust model evaluation.
Evolutionary Soups, a mixture-of-experts framework for fine-grained generation control, with gating networks trained via an evolutionary algorithm, achieves the best hypervolume, linear utility, and Tchebyshev utility among controllable methods on all tasks.
This work presents a systematic mechanistic analysis of multilingual safety using sparse autoencoder features, sparse interpretable directions in the residual stream associated with harmful and harmless model behavior across three instruction-tuned LLMs, eight languages, and all model layers to qualify the language-universality of safety alignment as architecture-dependent and offer a mechanistic account of multilingual safety interventions.
This is the first work to formulate compression-aware abstention as a learning problem, in which a model learns to answer when supporting evidence survives compression and abstain when it does not, and controlled-deletion experiments show that the learned behavior is driven by evidence content rather than input length alone.
Experiments show Influence-Directed Adaptive On-Policy Distillation (IDA-OPD), rather than relying on costly full-vocabulary Forward-KL objectives, preserves entropy-expanding updates while replacing entropy-contracting ones with divergence-adaptive advantage shrinkage, using only the teacher's sampled-token log-probability.
This work examines whether FVs extracted from a source language can steer task behavior in another language under both standard clean and perturbed zero-shot settings without providing demonstrations during inference, and observes that each LLM exhibits a relatively stable optimal range of attention heads for constructing effective FVs, and the pattern remains consistent across languages.
TACS is proposed, a trajectory-aware candidate selection framework for jailbreak suffix optimization that augments per-step evaluation with a trajectory-aware proxy and stabilizes selection with reference-policy regularization and a discriminator-estimated chi-squared correction, encouraging choices that remain effective beyond the current step.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Algorithms & Theory
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
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