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LLM Long Conversation Memory: A Comprehensive Survey

January 30, 2026 · Research · 100+ pages distilled

Long conversation memory retention remains one of the most critical challenges in LLM deployment. While modern LLMs excel at reasoning and generation, their finite context windows fundamentally limit coherent, contextually-aware conversations over extended interactions.

This report synthesizes research across six key domains: context window extension techniques, external memory architectures, memory summarization, attention mechanisms, persistent memory systems, and industry implementations.

Executive Summary

Area Key Finding
Industry Leader Google Gemini 1.5 Pro: 1-2M token context, 99.7% recall at 1M tokens
Best Extension Method YaRN & LongRoRA: 10x fewer training tokens than prior methods
Practical Framework MemGPT's OS-inspired virtual memory for unbounded context
Memory Reduction KVzip compression: 3-4x reduction, negligible performance loss
Training at Scale Ring Attention enables 1M+ token sequences via context parallelism

1. Context Window Extension Techniques

1.1 Rotary Position Embeddings (RoPE)

RoPE encodes relative positional information through rotation matrices:

RoPE(x, m) = x * e^(imθ)
θ_j = base^(-2j/d)

Where m is position, θ is rotation angle, and base is a hyperparameter (typically 10,000–500,000).

Pros: No additional trainable parameters, enables extrapolation beyond training length, widely adopted (LLaMA, Mistral, Qwen).

Cons: Suffers from "lost in the middle" problem, high-frequency dimensions lose information under naive interpolation.

Method Innovation Extension
Position Interpolation Linear position scaling 2-4x
NTK-aware Non-linear frequency scaling 8x
YaRN Piecewise interpolation + temperature 128k+
LongRoPE Progressive extension + search 2M+

1.2 ALiBi (Attention with Linear Biases)

ALiBi eliminates positional embeddings entirely, adding a linear bias to attention scores:

Attention(Q, K, V) = softmax(QK^T/√d - m·|i-j|)V

Key advantage: Train on 1K tokens, effective up to 10K+. Used in BLOOM (176B) and MPT models.

1.3 YaRN (Yet another RoPE extensioN)

YaRN combines three techniques:

  1. NTK-aware interpolation — reduces high-frequency distortion
  2. Temperature scaling — adjusts attention entropy for long contexts
  3. Partial attention — only attends to relevant context segments

Results: LLaMA 2 extends to 128K context with only 0.1% perplexity degradation vs. 400x degradation with naive interpolation.

2. External Memory Architectures

2.1 Retrieval-Augmented Generation (RAG)

RAG augments LLMs with external knowledge retrieval:

Query → Retriever → [Relevant Docs] + Query → LLM → Response

Key components:

  • Dense retrieval: Sentence-transformers, E5, BGE embeddings
  • Vector stores: Pinecone, Weaviate, Milvus, Chroma
  • Re-ranking: Cross-encoders for precision

2.2 Knowledge Graphs

Structured memory through entity-relationship-entity triples:

(Alice) --[employed_by]--> (OpenAI)
(Alice) --[expert_in]--> (LLMs)

Advantage: Explicit reasoning paths, interpretable updates, complex query support.

3. Memory Summarization Methods

3.1 Hierarchical Memory

Three-tier architecture:

  1. Working memory: Current conversation (in-context)
  2. Episodic memory: Recent conversation summaries
  3. Semantic memory: Long-term facts and concepts

3.2 Recursive Summarization

When context approaches limit:

  1. Summarize oldest N tokens into K tokens
  2. Prepend summary to remaining context
  3. Repeat as needed

4. Attention Mechanisms for Long Contexts

4.1 FlashAttention

IO-aware exact attention algorithm:

  • Speed: 2-4x faster than standard attention
  • Memory: Linear instead of quadratic
  • Exact: No approximation

4.2 Ring Attention

Context parallelism for sequences exceeding single GPU memory:

  • Distributes sequence across multiple devices
  • Enables training on 1M+ token sequences
  • Used in Gemini 1.5 Pro training

5. Persistent Memory Systems

5.1 MemGPT

OS-inspired virtual memory management:

OS Concept MemGPT Equivalent
RAM LLM context window
Disk External storage (DB, search index)
Page faults Retrieval triggers
Virtual addresses Pointers to stored data

6. Industry Implementations

System Context Key Tech
Gemini 1.5 Pro 1-2M tokens Ring Attention, MoE
Claude 3 200K tokens Constitutional AI + efficient attention
GPT-4 Turbo 128K tokens Undisclosed (likely RoPE variants)
Kimi K1.5 200K+ tokens Long-context optimization

Key Takeaways

  1. Context extension is solved — YaRN and LongRoRA enable million-token contexts efficiently
  2. RAG remains essential — Even infinite context can't replace structured retrieval
  3. Memory hierarchy matters — Working + episodic + semantic beats flat storage
  4. Attention is the bottleneck — FlashAttention and Ring Attention unlock scale
  5. Virtual memory is the future — MemGPT's OS approach provides clean abstractions

Full 100+ page report available on request. Sources: 18 Tavily searches across 6 research areas, synthesized from 50+ academic papers and industry reports.