Recursive Language Models: A Paradigm Shift for Agentic AI Scalability

MEMRIQ INFERENCE DIGEST - ENGINEERING EDITION

Episode: Recursive Language Models: A Paradigm Shift for Agentic AI Scalability

Total Duration:: 20:39

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MORGAN: 00:00

Welcome back to Memriq Inference Digest - Engineering Edition, the podcast brought to you by Memriq AI, your go-to content studio for tools and resources tailored for AI practitioners. Head over to Memriq.ai to explore more deep-dives like this one. Today, we're diving into a fascinating and game-changing topic: Recursive Language Models, or RLMs, and why they represent a paradigm shift for agentic AI.

CASEY: 00:25

That’s right. We’re going beyond the usual transformer scaling chatter and into how RLMs let models take agency over their own context, enabling reasoning over inputs two orders of magnitude larger than standard context windows. This isn’t just theory — it's practical, and we’ll cover tools like the RLM official repo, Modal and Prime sandboxes, GPT-5 models, Gemini 1.5 Pro, Longformer, BigBird, and more, getting under the hood of architectures that break traditional limits.

MORGAN: 00:54

And if you want to go even deeper — with diagrams, thorough technical explanations, and hands-on code labs — search for Keith Bourne on Amazon and grab the second edition of his book. Keith’s work has been crucial in understanding these agentic AI patterns, so expect to hear a lot from him today.

CASEY: 01:12

Yes Morgan, and actually, we have Keith joining us here today to help give us an expert view on this topic.

KEITH: 01:19

Hi everyone, this is Keith, it is really great to be here and talk about this potential game change in the AI space, thanks for bringing me in! Before we go deeper, I want to thank two folks who helped me connect the dots on RLMs recently: Deepan Das at AXIS and Pankaj Mathur from Sage. Both fintech AI leaders who are absolutely tearing it up in agentic AI. We recently had an in-depth talk about RLMs and how they could impact our businesses — that was the inspiration for today’s podcast.

CASEY: 01:49

Thanks Keith, and thanks Deepan and Pankaj for bringing this topic to our attention! In this podcast, we’ll unpack real benchmarks, deep technical details, trade-offs for deployment, and how this all fits into the future of AI systems. Let’s get started.

JORDAN: 02:04

So here’s a fact that really blew my mind recently: a tiny model called GPT-5-mini, leveraging Recursive Language Models, actually doubled the number of correct answers compared to full GPT-5 on long-context tasks. And get this — it did so with inputs spanning millions of tokens, far beyond the typical transformer context window.

MORGAN: 02:25

Wait, a smaller model *outperforming* the flagship GPT-5? That’s a bold claim. How is that possible?

JORDAN: 02:31

It’s because RLMs don’t shove the entire input into the transformer at once. Instead, they store the text externally — literally as a Python variable in a sandboxed environment — and the model writes code that queries this environment recursively. Think of it like turning the LLM into an agent that programmatically digs through the context, calling itself on relevant pieces.

CASEY: 02:54

So rather than flattening everything into a big chunk of tokens, it’s breaking down the problem programmatically? That’s clever. But what about cost — isn’t running multiple sub-calls expensive?

JORDAN: 03:06

Surprisingly, the cost per query averages just 99 cents, cheaper than the hypothetical transformer that could handle all tokens natively. And accuracy on BrowseComp-Plus, a multi-million token benchmark, hits over 91% — that’s a huge leap over conventional long-context methods that struggle as input size grows.

MORGAN: 03:26

That’s huge — two orders of magnitude larger inputs and better accuracy *and* cheaper. This has serious implications for real-world long-context AI applications.

CASEY: 03:36

I’m intrigued but cautious — we’ll want to see how it holds up in production, latency, and complexity. But definitely exciting.

ALEX: 03:44

Here’s the one-sentence essence: Recursive Language Models flip the traditional transformer paradigm by treating text as an external environment, queried programmatically through generated code and recursive self-invocations, enabling efficient reasoning over context sizes two orders of magnitude larger.

CASEY: 04:02

Key tools supporting this are the RLM official repo, Modal and Prime sandboxes for isolation, GPT-5 and GPT-5-mini variants, and complementary techniques like Prime Intellect’s context folding.

ALEX: 04:15

If you remember nothing else, remember this: instead of feeding everything at once, RLMs decompose, query, and synthesize context hierarchically, breaking the quadratic attention barrier that limits conventional transformers.

JORDAN: 04:28

To understand why RLMs are suddenly gaining traction, let’s rewind a bit. Traditional transformers, even the largest ones, suffer from 'context rot' — their performance drops off dramatically as the input length creeps past 4K or 32K tokens. The RULER benchmark quantifies this, showing accuracy dips of 15-40 points in many cases. So just scaling up the context window doesn’t solve the problem.

MORGAN: 04:53

Right, and the compute cost grows quadratically with input length due to full attention mechanisms — making brute force context scaling prohibitive.

JORDAN: 05:02

Exactly. And agentic AI applications complicate things further — they need adaptive context management that goes beyond just raw input length. Prime Intellect’s multi-agent systems exemplify this need, where coordinating multi-session workflows requires intelligent recursive context access, not brute force reading.

CASEY: 05:21

And Gemini 1.5 Pro shows promise with 10 million token recall at 99.2%, but that’s a specialized architecture and still faces limits downstream in reasoning accuracy and cost.

JORDAN: 05:33

So RLMs emerge from this gap — they provide a way to bypass the quadratic bottleneck and context rot by letting the model decide *when* and *what* to read, recursively and programmatically.

MORGAN: 05:45

That’s a compelling reason for the industry to pivot now — especially as businesses demand AI that can handle massive, complex corpora reliably and affordably.

TAYLOR: 05:55

At its core, RLM represents a fundamental architectural inversion. Traditional transformers assume all tokens must be ingested directly into the attention context — an O(n²) operation on n tokens. RLMs instead externalize the entire corpus as a Python variable inside a REPL, or Read-Eval-Print Loop, sandboxed environment.

MORGAN: 06:14

So the model doesn’t ‘see’ all the tokens at once. Instead, it generates code instructions to slice, grep, or query smaller chunks programmatically?

TAYLOR: 06:23

Precisely. The root LLM acts as an orchestrator — it takes a prompt with instructions, writes helper functions, and then spawns recursive sub-LLM calls on relevant context partitions. These sub-calls, themselves LLM invocations, analyze portions and report back results.

CASEY: 06:41

So this is hierarchical decomposition in action — breaking a big problem into smaller, manageable subproblems.

TAYLOR: 06:48

Yes, and this dramatically reduces the quadratic attention complexity to a programmatic selective access model. It’s like symbolic computation combined with LLM reasoning. The recursive structure aligns perfectly with agentic AI principles — models gain agency over context, control over tool invocation, and autonomous workflow coordination.

MORGAN: 07:09

So instead of a monolithic attention window, you get a distributed, recursive approach that can scale to millions of tokens and beyond.

TAYLOR: 07:17

Exactly. It’s a design pattern that pairs well with sandboxed execution environments — local for speed, Modal or Prime sandboxes for isolation and scalability. It’s a big shift in how we architect LLM systems for ultra-long contexts and complex reasoning.

TAYLOR: 07:33

Let’s compare RLM to some other leading long-context approaches. Gemini 1.5 Pro, Longformer, BigBird, and Mamba use sparse or block-sparse attention to scale context windows up to around one million tokens. They’re great for certain aggregation tasks but accuracy degrades on complex multi-hop reasoning.

CASEY: 07:51

Right, and RAG — retrieval-augmented generation — offers lower cost and explainability by chunking documents and retrieving relevant passages, but the chunk boundaries cause 10-20% accuracy drops when reasoning crosses chunks.

TAYLOR: 08:06

Exactly. Then you have compression-based methods, like MemGPT and LLMLingua, which compress context to constant memory sizes, great for edge deployment but with inevitable information loss at high compression ratios.

MORGAN: 08:20

So when would you pick each?

TAYLOR: 08:22

Use long-context architectures when your task tolerates some accuracy degradation and requires faster throughput on single documents up to a million tokens. Use RAG when cost and explainability matter but cross-chunk reasoning is limited. Use compression for constrained hardware or edge. And use RLM when you need precise, programmatic examination of massive corpora — think multi-million tokens — and can tolerate higher latency and cost variability.

CASEY: 08:49

That latency and cost variability is a concern though — we’ll get into that shortly. But clear trade-offs here.

ALEX: 08:56

Now, let me take you under the hood of RLM’s technical architecture — it’s a beautifully clever system. The input corpus is never pushed all at once into the transformer. Instead, it’s stored as a Python string variable inside a sandboxed REPL — this can be a local Python environment or cloud sandboxes like Modal or Prime.

MORGAN: 09:16

So the model’s context window is freed from the burden of the entire text.

ALEX: 09:20

Exactly. The root LLM is primed with system instructions to read slices of this corpus by generating Python code — functions that grep, slice, or summarize parts of the text. Here’s the exciting bit: the root LLM can spawn sub-LLM calls, smaller language models invoked recursively to analyze specific chunks.

CASEY: 09:40

So these sub-LLM calls are like first-class primitives?

ALEX: 09:45

Spot on. They’re formal, verifiable invocations with structured inputs and outputs. This recursive decomposition means the model inspects the document’s structure, partitions the corpus, and delegates sub-tasks down the call stack. Each sub-LLM call processes a manageable chunk, returning intermediate reasoning results that are then aggregated using symbolic computation — a lightweight way to synthesize partial answers programmatically.

MORGAN: 10:12

That’s a neat way to avoid stuffing all tokens into one big attention matrix.

ALEX: 10:17

Absolutely. It reduces the O(n²) attention complexity to manageable, programmatic selective access patterns. The sandboxed execution ensures security and isolation — no arbitrary code escapes. Modal sandboxes provide scalable, isolated environments in the cloud, and Prime sandboxes offer production-grade stability.

CASEY: 10:36

And because the model controls which parts to query and when, it adapts dynamically to input complexity?

ALEX: 10:43

Yes. This is a game changer for multi-hop QA over million-token corpora, codebase analysis, legal discovery — anywhere you need precise, interpretable recursive reasoning. And since sub-LLM calls are explicit, you get traceability and verifiability of reasoning steps — crucial for debugging and trust.

MORGAN: 11:01

I love how this blends symbolic programming concepts with modern LLM inference — it’s a beautiful synergy.

ALEX: 11:09

Let’s talk numbers — the proof is in the metrics. On the BrowseComp-Plus benchmark covering 6 to 11 million tokens, RLM achieves 91.33% accuracy — a level impossible for fixed-context transformers.

MORGAN: 11:22

That’s massive. What about comparisons?

ALEX: 11:24

GPT-5-mini, using RLM, outperforms full GPT-5 by more than two times on OOLONG benchmarks measuring long-context question answering. CodeQA accuracy jumps from 24% in base LLM to 62% with RLM — that’s a 158% relative improvement. Tasks previously impossible, like OOLONG-Pairs, see an F1 of 58 versus near zero baseline.

CASEY: 11:47

What about cost?

ALEX: 11:48

Average cost per query is $0.99 — cheaper than any hypothetical full-context model that needs astronomical compute to handle millions of tokens directly, which might run $1.50 to $2.75 per query.

MORGAN: 12:02

So you get better accuracy, longer context handling, and lower cost. That’s an engineering win.

ALEX: 12:08

It is — especially for use cases where approximation isn’t acceptable and precision matters more than latency.

CASEY: 12:15

Let’s pump the brakes here. RLM sounds great, but it’s not magic. Its recursive sub-LLM calls introduce sequential dependencies, limiting parallelism. Latency ranges from 5 to 60 seconds — much higher and more variable than the 1 to 5 seconds you get with RAG.

MORGAN: 12:32

That variability could be a dealbreaker for latency-sensitive applications.

CASEY: 12:37

Exactly. The cost per query also follows a heavy-tailed distribution — most queries are cheap, but some outliers spike unpredictably, complicating budgeting and SLAs.

KEITH: 12:47

Hi everyone, this is Keith again, there’s also the risk that generated code by the model has bugs or silent failures, which can cause incorrect reasoning. Verification steps trigger re-computation, adding to latency and cost overhead.

CASEY: 13:01

Correct, and this means RLMs aren’t a universal solution. For tasks tolerating approximation or needing consistent latency, RAG or compression methods may be preferable. But the paradigm shift here is huge — giving models agency over how they access and manage context is a fundamental change.

MORGAN: 13:19

So, it’s about picking your tool based on tolerance for latency, accuracy needs, and cost predictability.

CASEY: 13:26

Absolutely. Understanding these trade-offs is key for production deployment.

SAM: 13:31

Let’s look at where RLM is already making waves. In legal discovery, precision is paramount — RLMs extract facts from multi-million token corpora with over 91% accuracy, where approximation could lead to costly mistakes.

MORGAN: 13:44

That level of precision is a game changer for compliance and e-discovery.

SAM: 13:49

In codebase analysis, RLM’s programmatic grep and recursive reasoning enable 62% accuracy on repository-level questions, far outperforming RAG or long-context models that struggle with cross-file dependencies.

CASEY: 14:03

And in research synthesis?

SAM: 14:04

Hierarchical decomposition supports multi-hop QA across thousands of documents, enabling workflows that were previously infeasible. Plus, agentic AI workflows benefit from recursive summarization and context folding — think multi-session coordination over months without context rot.

KEITH: 14:22

And infinite chat handling is exciting — the REPL maintains persistent state, letting conversations grow arbitrarily long while summarization prunes context dynamically, a perfect match for agentic chatbots.

MORGAN: 14:35

Fantastic examples across industries, showing this isn’t just a lab curiosity.

SAM: 14:40

Alright, here’s a scenario: an engineering team needs to analyze a massive 2 million token monorepo, hunting for all external API calls missing retry logic.

TAYLOR: 14:50

Gemini 1.5 Pro’s long-context approach reads the entire repo holistically, but it’s costly — over $500 per query — and suffers aggregation accuracy drops, missing subtle cases.

CASEY: 15:01

The RAG approach is faster and cheaper but chunks cause 10-20% accuracy loss on cross-file reasoning, meaning missed buggy retry logic.

ALEX: 15:11

RLM’s programmatic grep and recursive sub-LLM calls achieve 62% accuracy — over 2.6 times better than the base LLM — and deliver verifiable traces of reasoning, though latency is higher and cost varies.

SAM: 15:25

So who wins?

MORGAN: 15:26

For high-stakes, accuracy-critical code review, RLM’s systematic, programmatic approach is superior despite latency trade-offs.

CASEY: 15:34

For quick triage or less critical audits, RAG or long-context might be acceptable.

SAM: 15:39

And cost-sensitive teams might choose RAG or Gemini if latency and predictability matter more than top accuracy.

TAYLOR: 15:46

It really comes down to accuracy versus latency versus cost — classic engineering trade-offs crystallized by this tech battle.

SAM: 15:54

For engineers ready to experiment with RLM, here’s a quick toolbox. Start by externalizing your input as an environment — store your corpus in a sandboxed Python REPL rather than feeding raw tokens. Use the RLM official repo on GitHub for baseline implementations.

MORGAN: 16:09

Modal sandboxes are great for isolated cloud execution, while Prime sandboxes offer production-grade stability.

SAM: 16:17

Treat sub-LLM calls as first-class primitives in your architecture — this enables hierarchical decomposition of complex queries.

CASEY: 16:25

A hybrid approach can work well — use RAG for initial retrieval to narrow down context, then invoke RLM for deep recursive analysis on selected pieces.

ALEX: 16:35

Also consider applying token compression techniques like LLMLingua before long-context inference to reduce input size without sacrificing accuracy.

SAM: 16:44

Finally, MemGPT combined with RAG can manage memory autonomously, inspired by virtual memory management in operating systems — a neat way to handle transition states and context updates.

MORGAN: 16:56

Great actionable advice for anyone tackling ultra-long context problems today.

MORGAN: 17:01

Before we move on, a quick shoutout to Keith Bourne’s book, *Unlocking Data with Generative AI and RAG*. While recursive language models aren’t covered specifically, it lays a solid foundation on generative AI architectures and retrieval-augmented generation — a perfect resource to complement today’s episode. You can find it on Amazon under Keith’s name.

MORGAN: 17:23

Memriq AI is an AI consultancy and content studio building tools and resources for AI practitioners. This podcast is produced by Memriq AI to help engineers and leaders stay current with the rapidly evolving AI landscape. Head over to Memriq.ai for more AI deep-dives, practical guides, and cutting-edge research breakdowns.

CASEY: 17:43

Memriq helps bridge the gap between research and production-ready AI solutions — highly recommended.

SAM: 17:50

Looking ahead, some key challenges remain. First, end-to-end reinforcement learning could optimize chunking, sub-query search, and synthesis strategies beyond current prompting heuristics — this would improve efficiency and accuracy.

MORGAN: 18:03

Parallelism is another pain point — synchronous sub-LLM calls create bottlenecks. If we could execute independent sub-queries concurrently, latency would drop significantly.

SAM: 18:15

Cost variance is a production headache. Predicting query complexity upfront, setting hard cost ceilings, and implementing graceful degradation are critical for stable SLAs.

CASEY: 18:25

Verification overhead — repeated re-computation during answer checking — needs smarter incremental verification to avoid wasted compute.

KEITH: 18:34

Integration with learned neural retrieval, like dense passage retrieval, as callable primitives would combine RLM’s programmatic precision with learned semantic relevance — an exciting hybrid frontier.

MORGAN: 18:46

Keep an eye on these research directions — they’re where the field will evolve next.

MORGAN: 18:52

For me, the big takeaway is that giving models agency over context access — letting them decide what to read and when — is a game changer for scaling AI beyond transformer limits.

CASEY: 19:03

I’m focused on the trade-offs — RLMs are powerful but come with latency and cost variability that teams must manage carefully.

JORDAN: 19:11

I’m excited by how RLMs enable agentic workflows, recursive summarization, and infinite chat — they open doors to new user experiences.

TAYLOR: 19:19

Architecturally, this inversion of context handling unlocks efficient hierarchical decomposition — key for ultra-long documents and multi-hop reasoning.

ALEX: 19:29

From a technical lens, the clever synergy of sandboxed execution, symbolic computation, and sub-LLM calls is a beautiful engineering solution to a hard scaling problem.

SAM: 19:39

The toolbox patterns around hybrid RAG plus RLM approaches give engineers practical handles to start building tomorrow’s scalable AI systems today.

KEITH: 19:49

And to circle back, I want to thank Deepan Das at AXIS and Pankaj Mathur at Sage again — our recent deep dive conversations about RLMs profoundly shaped this episode. This architectural insight enables handling inputs two orders of magnitude beyond model context windows while dramatically outperforming conventional long-context approaches. Two orders of magnitude! It’s a paradigm shift that engineers and data scientists need to watch closely.

MORGAN: 20:16

Keith, thanks so much for giving us the inside scoop today.

KEITH: 20:20

My pleasure — this is such an important topic and I hope listeners dig deeper into it.

CASEY: 20:25

And thanks everyone for tuning in. Recursive Language Models might be complex but they’re reshaping what’s possible with agentic AI.

MORGAN: 20:34

Until next time, keep pushing boundaries and building the future of AI. Cheers!