Realize What Matters: Principled Context Representation for Large-Scale Reasoning

arXiv:2609.27173 · cs.CL · Submitted 2026-09-23 · Read on arXiv

cs.CL

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/michaeltheologitis/r3con

Terminology

• Accuracy — 33× in this paper · explained in 1 episode
• Benchmark — 24× in this paper · explained in 2 episodes
• RAG — 20× in this paper · explained in 3 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Clustering — 5× in this paper · explained in 2 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Agent Harness — 2× in this paper · explained in 2 episodes
• Agentic Reasoning — 2× in this paper · explained in 1 episode
• Agentic Search — 2× in this paper · explained in 1 episode
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy vs. Cost Trade-off — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Agentic Coding — 1× in this paper · explained in 1 episode
• Agentic Retrieval — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Aperture — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Python Read-Eval-Print Loop — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes

Sources

• A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• Memory in the Age of AI Agents
• Cognitive Foundations for Reasoning and Their Manifestation in LLMs
• Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction
• Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
• CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning
• Code as Agent Harness
• InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning
• Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases
• Structure-R1: Dynamically Leveraging Structural Knowledge in LLM Reasoning through Reinforcement Learning
• Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval
• Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity
• Recursive Language Models

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