Localist LLMs -- A Mathematical Framework for Dynamic Locality Control

• URL: http://arxiv.org/abs/2510.09338v2
• Date: Mon, 03 Nov 2025 09:05:41 GMT
• Title: Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
• Authors: Joachim Diederich,
• Abstract summary: Key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining.<n>We prove that when group sparsity penalties exceed certain threshold values, the model's attention mechanisms concentrate on semantically relevant blocks, achieving low entropy and high fidelity with negligible error.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining. This is achieved through group sparsity penalties on attention mechanisms, information-theoretic anchor design, and dynamic rule injection. We provide rigorous mathematical proofs establishing explicit threshold conditions under which attention provably concentrates on semantically relevant blocks, with exponential bounds on attention entropy and pointer fidelity. Specifically, we prove that when group sparsity penalties exceed certain threshold values, the model's attention mechanisms concentrate on semantically relevant blocks, achieving low entropy and high fidelity with negligible error. This framework enables practitioners to continuously interpolate between interpretable and high-performance modes, supporting applications in regulated domains requiring both transparency and capability.

Related papers

• Progressive Localisation in Localist LLMs [0.0]
  This paper demonstrates that progressive localization represents the optimal architecture for creating interpretable large language models (LLMs)<n>We investigate whether interpretability constraints can be aligned with natural semantic structure while being applied strategically across network depth.<n>We show that progressive semantic localization, combining semantic block with steep adaptive locality schedules, achieves near-baseline language modeling performance while providing interpretable attention patterns.
  arXiv  Detail & Related papers  (2025-11-23T09:49:13Z)
• Localist LLMs with Recruitment Learning [0.0]
  We present a novel framework for training large language models with continuously adjustable internal representations.<n>Key innovations are (1) a locality dial, that dynamically controls the degree of localization during both training and inference without requiring model retraining, and (2) an information-theoretic recruitment mechanism that adaptively allocates semantic blocks as needed.
  arXiv  Detail & Related papers  (2025-10-20T09:58:34Z)
• Explaining multimodal LLMs via intra-modal token interactions [55.27436637894534]
  Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse vision-language tasks, yet their internal decision-making mechanisms remain insufficiently understood.<n>We propose enhancing interpretability by leveraging intra-modal interaction.
  arXiv  Detail & Related papers  (2025-09-26T14:39:13Z)
• Rule Encoding and Compliance in Large Language Models: An Information-Theoretic Analysis [0.0]
  The design of safety-critical agents based on large language models (LLMs) requires more than simple prompt engineering.<n>This paper presents a comprehensive information-theoretic analysis of how rule encodings in system prompts influence attention mechanisms and compliance behaviour.
  arXiv  Detail & Related papers  (2025-09-23T14:42:32Z)
• Towards Efficient General Feature Prediction in Masked Skeleton Modeling [59.46799426434277]
  We propose a novel General Feature Prediction framework (GFP) for efficient mask skeleton modeling.<n>Our key innovation is replacing conventional low-level reconstruction with high-level feature prediction that spans from local motion patterns to global semantic representations.
  arXiv  Detail & Related papers  (2025-09-03T18:05:02Z)
• ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification [51.07970070817353]
  An ideal time series classification (TSC) should be able to capture invariant representations.<n>Current methods are largely unguided, lacking the semantic direction required to isolate truly universal features.<n>We propose an end-to-end Energy-Regularized Information for Shift-Robustness framework to enable guided and reliable feature disentanglement.
  arXiv  Detail & Related papers  (2025-08-19T12:13:41Z)
• Mitigating Attention Hacking in Preference-Based Reward Modeling via Interaction Distillation [62.14692332209628]
  "Interaction Distillation" is a novel training framework for more adequate preference modeling through attention-level optimization.<n>It provides more stable and generalizable reward signals compared to state-of-the-art RM optimization methods.
  arXiv  Detail & Related papers  (2025-08-04T17:06:23Z)
• Neuro-symbolic Weak Supervision: Theory and Semantics [5.455744338342196]
  We propose a semantics for neuro-symbolic framework that integrates Inductive Logic Programming (ILP)<n>ILP defines a logical hypothesis space for label transitions, clarifies semantics, and establishes interpretable performance standards.<n>This hybrid approach improves robustness, transparency, and accountability in weakly supervised settings.
  arXiv  Detail & Related papers  (2025-03-24T10:02:51Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation [51.14107156747967]
  Weakly supervised semantic segmentation (WSSS) has attracted considerable attention because it requires fewer annotations than fully supervised approaches.<n>We propose an Adaptive Re-Activation Mechanism (AReAM) to control deep-level attention to undisciplined over-smoothing.<n>AReAM substantially improves segmentation performance compared with existing WSSS methods, reducing noise while sharpening focus on relevant semantic regions.
  arXiv  Detail & Related papers  (2023-05-04T19:11:33Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.