Neurosymbolic Large Neighbourhood Search

Agentic Neural Architecture Search

This work proposes a mechanism that bridges these two paradigms: an LLM produces a high-quality seed architecture, then decomposes it into a "slotted architecture", a scaffold with named, interchangeable module slots that automatically defines a bounded, task-specific search space for conventional NAS to explore, without manual engineering.

An AI4AI Framework for Visual Token Pruning

This paper argues that the key lies in designing an appropriate search-state representation that connects the internal knowledge of LLMs with the structural requirements and constraints of visual-token pruning, and proposes AutoPrune, a training-free framework for LLM-driven visual-token pruning policy design.

Memory efficiency and resource-rational encoding in sentence processing

The results show how resource-rational models of WM allocation can be implemented in neural models simply and successfully, and point to a dissociation between WM retrieval mechanisms and the underlying memory representations in models of human sentence processing.

SIRD: Symbolic Integration Rules Dataset

SepPrune:A Separator-based Pruning Framework for Efficient Multimodal Large Language Models

It is observed that attention scores from both vision and text tokens peak at modality separator tokens, suggesting that these separators bridge the two modalities and proposes SepPrune, an efficient, training-free, plug-and-play pruning method that uses the separator token as a unified query to rank and select informative vision tokens.

Parallel Noising in Neural Markov Logic Networks

Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve strong performance as generative models for small relational structures, they underperform diffusion-based generative graph models on larger structures. In this paper, we strengthen NMLNs along two main dimensions: (i) we increase the expressive capacity of their potential functions using graph neural networks, and (ii) we develop a new training and inference algorithm inspired by parallel-tempering Markov chain Monte Carlo methods, which we name parallel noising. Together, these enhancements enable NMLNs to attain strong performance in graph generation relative to general diffusion-based generative graph models. Furthermore, they allow NMLNs to match the performance of specialized text-based recurrent models when generating small molecular structures.