dynamic-concept-models

name	dynamic-concept-models
title	Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.24617
keywords	["compression","concept-based reasoning","variable-length tokens","scaling laws","LLM efficiency","semantic spaces"]
description	Implement hierarchical language modeling that compresses variable-length token sequences into high-capacity semantic concepts, achieving +2.69% benchmark improvements while reducing inference FLOPs by reallocating compute to concept-level reasoning. Use for efficiency-critical deployments where reasoning quality can be improved while maintaining computational budget.

When to Use This Skill

• Budget-constrained inference systems with fixed FLOP allocations
• Applications requiring improved reasoning quality within existing compute constraints
• Scenarios where language tokens have highly variable information density
• Zero-shot generalization tasks where better reasoning matters more than token coverage

When NOT to Use This Skill

• Fixed-architecture models where retraining is impossible
• Tasks requiring exact token-level output (code generation with specific syntax)
• Systems already optimized for latency at the cost of reasoning quality
• Applications without clear computational budget constraints

Core Concepts

Most language models allocate the same computational resources to every token, despite the uneven information density in natural language. DLCM learns to:

1. Discover variable-length concepts: Group adjacent tokens into concepts without predefined linguistic units
2. Compress representation: Map concepts to a lower-dimensional latent space
3. Reallocate compute: Direct unused capacity from token-level processing to concept-level reasoning

The result: Fewer total FLOPs spent on token shuffling, more capacity spent on actual reasoning.

Key Innovation: Compression-Aware Scaling Law

Standard scaling laws assume uniform token processing. DLCM introduces three orthogonal dimensions:

• Token-level capacity: Ability to model low-level token interactions
• Concept-level reasoning capacity: Power to reason in compressed semantic space
• Compression ratio: Tokens per concept (e.g., 4:1 means 4 tokens compress to 1 concept)

This allows principled compute allocation: Given a budget, choose which dimension to expand.

Architecture Pattern

# Simplified DLCM architecture structure
class DynamicConceptModel:
    def __init__(self, token_dim=768, concept_dim=256, compression_ratio=4):
        self.token_level = TokenLevelTransformer(token_dim)
        self.compression = ConceptCompressor(token_dim, concept_dim)
        .reasoning = ReasoningTransformer(concept_dim, high_capacity=)
        .decompression = ConceptDecompressor(concept_dim, token_dim)
        .ratio = compression_ratio

     ():
        
        token_features = .token_level(token_sequence, shallow=)

        
        concept_boundaries = .compression.find_boundaries(token_features)
        concepts = .compression.compress(
            token_features,
            boundaries=concept_boundaries
        )

        
        concept_reasoning = .reasoning(concepts, depth=)  

        
        output = .decompression(concept_reasoning)
         output