--- title: 'It’s Tokens All The Way Down: How RLMs are Different' category: talks date: '2026-06-30' time: '11:10am-11:30am' track: Context Engineering room: Track 8 speakers: - Kevin Madura sourceLabels: - Official conference schedule - Public YouTube metadata last_auto_summarized: '2026-07-03T17:39:15.773Z' scheduleTrack: "Context Engineering" scheduleRoom: "Track 8" scheduleLabels: ["Context Engineering", "Track 8", "session", "confirmed"] --- # It’s Tokens All The Way Down: How RLMs are Different ## Conference Context - Date/time: 2026-06-30 · 11:10am-11:30am - Track/room: Context Engineering · Track 8 - Speaker(s): Kevin Madura - Session type/status: session · confirmed - Track: Context Engineering - Room: Track 8 - Session type: session - Status: confirmed ## Session Description Recursive Language Models represent an intuitive but distinctively important approach to how LLMs handle context. The practical implications are bigger than they first appear. Tasks that would traditionally require careful prompt engineering, custom agent scaffolding, or multi-step orchestration collapse into surprisingly simple, composable programs. In this talk, we’ll cover what makes an RLM distinct from a coding agent, explore where the abstraction shines and where it breaks down, and walk through concrete use cases that are informed by real-world situations at scale. We’ll see side-by-side comparisons to understand trade-offs in complexity, performance, time, and token usage. ## Media Evidence [DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners](https://www.youtube.com/watch?v=-cKUW6n8hBU) (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions). - Source video: `youtube--cKUW6n8hBU` - Slide deck: [[youtube--cKUW6n8hBU-dense-slides|Dense Slides: DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners]] — 12 visible slide image(s); 12 HTML recreation(s). ![[assets/dense-slides/-cKUW6n8hBU/slide-001.jpg]] ![[assets/dense-slides/-cKUW6n8hBU/slide-002.jpg]] ![[assets/dense-slides/-cKUW6n8hBU/slide-003.jpg]] - Additional slide evidence: [[youtube--cKUW6n8hBU-slides|Slides: DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners]], [[youtube--cKUW6n8hBU-reconstructed-slides|Reconstructed Slides: DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners]] - Slide-derived themes for `youtube--cKUW6n8hBU`: document, declarative, framework, modular, software, cases, lighting, round. ## Evidence Graph This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits. ### Media Signals - `youtube--cKUW6n8hBU` — 8 slide-derived text signals - Slide-derived themes for `youtube--cKUW6n8hBU`: document, declarative, framework, modular, software, cases, lighting, round. - Evidence links for `youtube--cKUW6n8hBU`: [[youtube--cKUW6n8hBU]], [[youtube--cKUW6n8hBU-slides]], [[youtube--cKUW6n8hBU-dense-slides]], [[youtube--cKUW6n8hBU-reconstructed-slides]] ### Agent Reading Notes Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence. ## Summary Kevin Madura’s session frames Recursive Language Models as a practical abstraction for context-heavy AI work: instead of treating an LLM call as the final step in a prompt chain, an RLM can recursively invoke language-model behavior as part of a composable program. The linked speaker profile and related DSPy material point to an enterprise-focused perspective, with Madura drawing from work at AlixPartners on real-world AI systems, DSPy, RLMs, and agent-native architectures. The supporting slides from his prior AI Engineer talk, “DSPy: The End of Prompt Engineering,” suggest this session is likely to emphasize moving beyond hand-tuned prompts toward declarative, testable, reusable AI programs. The scheduled description positions RLMs as different from coding agents: useful where prompt engineering, custom scaffolding, or multi-step orchestration would otherwise add complexity. The core comparison is expected to be concrete rather than theoretical, covering where recursive model calls simplify implementation, where they introduce trade-offs, and how they compare on complexity, performance, latency, and token usage. The related video and transcript map should be treated as supporting context only, not as confirmation of this exact World’s Fair session recording. ## Transcript Status Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet. ## People - [[kevin-madura]] ## Supporting Slides - [[youtube--cKUW6n8hBU-slides]] — extracted from the related public AI Engineer video. ## Slide Evidence - Slide-only cropped deck: [[youtube--cKUW6n8hBU-dense-slides]] (12 viable slide images). - Related slide/OCR pages: - [[youtube--cKUW6n8hBU-dense-slides]] - [[youtube--cKUW6n8hBU-reconstructed-slides]] - [[youtube--cKUW6n8hBU-slides]] - Slide-derived terms: `dspy`, `shares`, `document`, `attachments`, `program`, `total`, `python`, `sold`, `structured`, `code`, `were`, `find`, `allows`, `class`, `transactions`, `applying`, `step`, `sentiment` ## Attendance Visibility No high-confidence attendance icon signal is shown for this talk. The sampled video evidence was either low confidence, source-proxy-only, or did not expose a clear audience view. ## Synthesis ### Synthesized Breakdown # It’s Tokens All The Way Down: How RLMs are Different ## Conference Context - Date/time: 2026-06-30 · 11:10am-11:30am - Track/room: Context Engineering · Track 8 - Speaker(s): Kevin Madura - Session type/status: session · confirmed - Track: Context Engineering - Room: Track 8 - Session type: session - Status: confirmed ## Session Description Recursive Language Models represent an intuitive but distinctively important approach to how LLMs handle context. The practical implications are bigger than they first appear. Tasks that would traditionally require careful prompt engineering, custom agent scaffolding, or multi-step orchestration collapse into surprisingly simple, composable programs. In this talk, we’ll cover what makes an RLM distinct from a coding agent, explore where the abstraction shines and where it breaks down, and walk through concrete use cases that are informed by real-world situations at scale. ### Speaker And Company Context - No speaker profile is attached in the official schedule data. ### Topics Covered - [[coding-agents]] ### Derived Links And Source Material - [[youtube--cKUW6n8hBU]] — related YouTube source page. - [[youtube--cKUW6n8hBU-slides]] — slide evidence. - [[youtube--cKUW6n8hBU-reconstructed-slides]] — slide evidence. - [[youtube--cKUW6n8hBU-dense-slides]] — slide evidence. ### Novel Concepts / Clever Methods - No highlighted novel concept has been detected yet. ### Evidence Boundary This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.