AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,089 papers
This work reformulates the output projection followed by top-k token selection as a maximum inner product search over token embeddings and replaces the dense vocabulary projection with an HNSW-based vector index, suggesting approximate retrieval is a practical alternative to dense output projections in latency-sensitive small-batch decoding.
This framework separates temporal alignment, plasticity, forgetting, and bounded rehearsal in recurrent sequence models, together with numerically stable positive-decay renormalization, to remain competitive in language modeling and improve length extrapolation on variable-digit addition.
Overall, it is found that probing is an effective means to catch a range of different tool-calling errors, including errors arising from using an argument that has the wrong value but the correct type, which might not be recorded by standard logging frameworks.
This work compares Turkish document question answering across three chunking strategies, five embedding models, and two LLMs, over three documents with contrasting layouts, finding the faster LLM is not the more accurate one.
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Evaluating 13-21 models across six presentation operationalizations and four task-domain operationalizations suggests that, despite confounds, some models possess practical SGTR capabilities, and that SGTR should be monitored and considered in the design of safety-critical AI applications.
This study explores and evaluates the ability of LLMs to follow and enhance human mental trajectories during semantic memory search and demonstrates that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans.
A consolidated memory that states a decision constraint and whose source record has since been superseded by a record that withdraws it is studied: provenance is immutable, the current record has changed, and the memory is stale.
This work introduces MathAdv, a diagnostic benchmark spanning 13 domains across undergraduate- and graduate-level mathematics, and shows how component-wise evaluation can reveal model capabilities and failure modes that aggregate theorem-proving accuracy obscures.
JuryProbe is introduced, an empirical consensus-risk diagnostic for reference-free factuality judge panels, paired with a calibration-based routing policy, which estimates consensus risk from a labeled calibration probe using false-negative-only (FN-only) judge correlation and false-consensus lift.
This work introduces Sieve, a search-inspect-fetch strategy driven by a Boolean Query Language (BQL): it searches webpage fields to filter candidates, uses an interchangeable ranker to order them, presents structure-rich result cards for inspection, and fetches only selected sections.
Tail subtraction is introduced, which removes shared prompt and continuation semantics from boundary states and yields cleaner, more stable steering signals, and suggests that steering depends on representations of what the model is about to do, not merely on what has already appeared.
This work investigates the ability of Large Language Models to generate structurally valid and constraint-compliant network topologies through a constraint-driven pipeline combining hierarchical modeling and systematic validation, and provides a systematic benchmark for understanding how LLMs handle structural and resilience constraints in topology synthesis.
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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