#compositional-learning News & Analysis

6 articles tagged with #compositional-learning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · May 287/10

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Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement

Researchers introduce Prompt Codebooks (PCO), a new framework for automatic prompt optimization that breaks down instructions into reusable, atomic components rather than treating prompts as fixed strings. The method achieves up to 30% performance gains over baseline approaches while reducing prompt lengths by 14x, enabling more efficient and adaptive language model instruction refinement.

AINeutralarXiv – CS AI · Jun 56/10

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Learning to Theorize the World from Observation

Researchers introduce Learning-to-Theorize, a new AI paradigm that builds explicit explanatory theories of the world from observations rather than simply predicting future states. The Neural Theorizer (NEO) model represents understanding as executable, compositional programs whose learned primitives can be recombined to explain novel phenomena, enabling explanation-driven generalization.

AINeutralarXiv – CS AI · Jun 26/10

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Inverse Depth Scaling From Most Layers Being Similar

Researchers analyzing large language models find that loss scales inversely with network depth, suggesting most layers function similarly and reduce error through ensemble averaging rather than compositional learning. This inefficient scaling pattern may stem from architectural constraints in residual networks, indicating that improving LLM efficiency requires fundamental architectural innovations rather than simply adding more layers.

AIBullisharXiv – CS AI · May 126/10

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Gate-and-Merge: Zero-shot Compositional Personalization of Vision Language Models

Researchers present Gate-and-Merge, a zero-shot framework enabling vision-language models to recognize and compose multiple user-defined concepts without requiring co-occurrence training data. The approach uses lightweight LoRA adapters for individual concepts and employs a gating mechanism to merge them intelligently at inference time, maintaining concept integrity while enabling compositional personalization.

AINeutralarXiv – CS AI · May 116/10

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AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites

Researchers propose AGWM (Affordance-Grounded World Models), a machine learning framework that improves how AI agents understand which actions are executable in dynamic environments by explicitly tracking prerequisite dependencies. The approach addresses a fundamental limitation in conventional world models that fail to account for how actions reshape the availability of future actions, reducing multi-step prediction errors and improving generalization.

AIBullisharXiv – CS AI · Mar 116/10

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PRECEPT: Planning Resilience via Experience, Context Engineering & Probing Trajectories A Unified Framework for Test-Time Adaptation with Compositional Rule Learning and Pareto-Guided Prompt Evolution

Researchers introduce PRECEPT, a new framework for AI language model agents that improves knowledge retrieval and adaptation through structured rule learning and conflict-aware memory systems. The framework shows significant performance improvements over existing methods, with 41% better first-try accuracy and enhanced compositional reasoning capabilities.