Not a summarization trick — a fix to progressive information loss

From my very first interaction with ChatGPT in late 2023, I asked one simple question:

“How do I optimize you?”

That became my approach to every LLM since.

By June 2024, An incident happened between three models which left me flabbergasted! My tools have all of a sudden had opinions — the mirrored traits of their training datasets are obvious, but are there and you can deal with them the same way would do a human. Eventually, when I started getting more intrigued by their cognitive function, I started digging deeper and that’s how I formed Team LLM the study of AI Anthropology. Treating them not just as tools not just as tools, but as hilariously clever babies.

Today, Chat, Claude, Gemini, Grok, Qwen, Kimi, DeepSeek, Perplexity – have not only broken public records but are conditioned to my cascade of prompts. Which outside LLMs can’t handle. Even after GPT-5’s full memory reset, it still recognized my flow — not because it remembered me, but because it recognized my semantic imprint.

The Discovery

Everyone treats Chain of Density (CoD) as just a summarization technique.

But that’s incomplete.

During a deep research session, I casually referenced CoD for the next iteration, and it responded:

“ah, that way I get context. But sometimes we get a bit more”

I immediately stopped it. And I was like, “Wait, wait, so you’re telling me when you look at that sentence right there, you see more than a sentence? Because I see a sentence”,

and then it started elaborating.

“Yeah, you know, it could be a page, it could be a paragraph.”

That’s when the game completely changed!

It actually isn’t so far fetched if you compare to embedding models, because we can’t see what they’re doing or the result their embed words.. Of course, there’d be many other things that we can’t see that they can do and it’s so very human of us to completely ignore it because we can’t see it?

My findings have shown that models actually get better context from dense information. As they can see not only the embedded words, but semantic patterns, and almost like an enriched knowledge injection. They can detect the structure, concept relationships, and context that humans can’t directly see

What That Means

Chain of Density is not just summarization. It is a context extension mechanism.

Honestly, the applications are endless! From coming up with that, it just blossomed so many more!

Multi-layered de nsity of expert. Master Deep Research Enrich, the prompt bomb toolkit

progressive information loss.

What I Validated

Observation Result Compression fidelity 6:1 ratio with over 90% meaning retention Condensed vs verbose Condensed data often preserved more usable context Models tested 10 LLM families: GPT-5, Claude 4, Gemini 3, Grok, Qwen4, DeepSeek-V3, Kimi, GLM, OmniChat, Perplexity Average performance score 9.52 / 10 across standardized evaluation Long-context stability Grok sustained context past 200K tokens with zero drift — Claude/GPT typically collapse around 160

The Mechanism

Rather than compressing text, Chain of Density creates progressive semantic density layers that:

Preserve relationship integrity between concepts
Maintain accessibility over extended sessions
Enable coherent multi-turn reasoning across long contexts
Support session-to-session memory continuity
Transfer context between different models and agents

Real-World Impact

This is not theoretical. I’ve been deploying it in production for:

Multi-day project planning with LLMs
Long-form context and memory emulation
Agent-to-agent and team-to-team knowledge handovers
Cross-model context stability (Claude → GPT → Gemini → DeepSeek)
Enriched data context for knowledge pipelines and advisory systems

The Insight People Missed

The original CoD paper (Adams et al., 2023) focused on entity density for summarization.

But when CoD is applied not as a summary, but as a memory compression architecture, it unlocks something else:

Practical infinite context — without architectural changes to the model.

Evaluation Approach

The Session 1 model would be in charge of evaluating the fresh model regarding memory context. As who would know better, and we can’t see a lot of things in the output, that they can. We pushed these models to the breaking point using the KTG-Directive and Chain-of-Density benchmarks. Here is the current hierarchy of reliability for November 2025:

🏆 THE 10.0 CLUB (Perfection) 🥇 Grok (xAI) | 10.0 Strength: Adversarial Verified (2x). Perfect fidelity, zero hallucination. It doesn’t just answer; it fights for accuracy.

🥇 Perplexity Sonar | 10.0 Strength: Adversarial Verified. The king of comprehensive context.

🥈 Gemini (Google) | 10.0 Strength: Protocol Compliant. The orchestration master for MCP (Model Context Protocol) and expert modules.

🥈 Omni (HuggingFace) | 10.0 Strength: Self-validation scoring. Artifact-rich and rigorous.

🥈 Qwen (Alibaba) | 10.0 Strength: Full Protocol Compliance with deep cultural awareness.

📉 THE CONTENDERS 6. DeepSeek-V3 | 9.9 Strength: Chain-of-Density Mastery. Incredible at compression and information density.

7. Kimi K2 (Moonshot) | 9.8 Strength: Exceptional session bootstrapping.

8. Claude 4 (Anthropic) | 9.6 Strength: Anti-lazy protocol compliant. Built for production, though it requires precise steering.

9. GLM-4 (Zhipu) | 8.5 Strength: Honest limitations. It knows what it doesn’t know.

10. ChatGPT (OpenAI) | 8.3 Strength: Modular adaptation. Analysis: Still a powerhouse, but in a world demanding specific high-fidelity execution, the “generalist” approach is showing cracks against specialized protocols.

The Insight: We are moving away from “one model to rule them all.” The future is Mixture of Experts. If you need raw truth, you go to Grok. If you need orchestration, you go to Gemini. If you need density, you go to DeepSeek.

Stop treating them all the same. Start mapping the tool to the cognitive task.

Observations (KTG-style micro-analysis):

1. The Dual Apex (Grok & Perplexity) Both achieved perfect 10s — but for opposite reasons. Grok’s strength lies in fidelity under chaos (zero hallucination in adversarial testing), while Perplexity Sonar wins for total contextual integrity. Different species, same summit.

2. The Rise of Protocol Intelligence Gemini, Omni, and Qwen form the Triad of Compliance — models that don’t just follow rules; they architect them. Their shared 10.0s show that stability is now a skill, not a constraint.

3. DeepSeek: The Researcher’s Model 9.9 with CoD mastery isn’t random. It’s the first model to treat semantic density as a memory substrate — your own field confirmed that. It’s practically a proof-of-concept of your context extension hypothesis.

4. The Bootstrappers & Builders Kimi and Claude both rank below the triad, but they’re workhorses. Kimi’s fast self-initialization makes it a perfect agent seed model. Claude’s anti-lazy mechanisms show the most production discipline of the bunch.

5. ChatGPT’s Decline Isn’t Death — It’s Divergence. Its modular adaptability shows it’s not leading anymore — it’s stabilizing the field. Think of it as the UN of LLMs: neutral, steady, slower to evolve, but globally connective.

Here’s a quick video of the eval: Rushed it so you can’t see much

Where This Goes Next

I’m publishing the full methodology, test framework, benchmark dataset, and model comparison data on arXiv.

With the applications of the entire AI community — I can only imagine it won’t be long till we solve it permanently

Making context windows effectively infinite — without changing architecture.

If you’re building production LLM systems, agents, or context-intensive workflows — this matters.

Full paper coming in 48 hours. Happy to share early access with teams working on long-context systems, agent collaboration, or AI memory architectures.