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Samsung's Tiny AI & Harvard's AI Doctor Explained

In late 2025, two AI breakthroughs are quietly rewriting the rules: Samsung's TRM model, a tiny reasoning engine that challenges the "bigger is better" myth, and Harvard's Dr. CaBot, the first AI doctor to be published in the New England Journal of Medicine.

These aren't just research headlines – they're signals of where AI is really going next. Smaller models that think better, and domain‑expert systems that work with professionals instead of replacing them. For leaders, marketers, and operators planning 2025–2026 strategy, understanding these shifts is now a competitive advantage.

In this post, you'll learn what Samsung's TRM and Harvard's Dr. CaBot actually do, why recursive reasoning matters more than raw model size, and how these trends will reshape AI tools, workflows, and buying decisions over the next 12–24 months.

1. Samsung TRM: A 7M-Parameter Model That Punches Above Its Weight

Samsung's new TRM model is turning heads because it challenges the core assumption of the last five years: that the path to better AI is always "bigger models, more data, more compute."

What is Samsung's TRM model?

TRM is a tiny reasoning model – about 7 million parameters – yet it reportedly matches or outperforms models that are thousands to tens of thousands of times larger, including flagship systems like Gemini 2.5 Pro and o3‑mini, on specific logic and reasoning benchmarks.

In simple terms:

Traditional LLMs: Huge, general-purpose, trained on everything
TRM: Small, specialized, optimized for logic-heavy, structured thinking

This is less about beating frontier models in every task, and more about redefining the efficiency frontier: How much reasoning can you squeeze out of a very small, focused model?

Why logic benchmarks matter for real-world use

Logic benchmarks test whether a model can follow multi-step reasoning:

Solving word problems with hidden constraints
Interpreting if–then rules and conditions
Handling nested logic like "if A and (B or C), then D unless E"

In business, these patterns show up everywhere:

Operations: routing, prioritization, exception handling
Customer support: decision trees, triage, compliance rules
Marketing workflows: complex segmentation logic and triggers

If a 7M model can reliably handle these, companies can:

Run logic-heavy AI on-device or at the edge
Reduce dependency on giant cloud models for every task
Cut latency and costs while increasing control over data

For AI tool builders and internal innovation teams, this opens a new design pattern: pair a small, sharp "reasoner" with larger models only when needed.

2. From Scale to Recursion: Why "Thinking in Loops" Beats "Thinking Bigger"

The surprise with TRM isn't just size – it's how it thinks.

What is recursive reasoning in AI?

Recursive reasoning means a model doesn't just make a single pass at a problem. Instead, it:

Breaks a task into smaller subproblems
Solves or re-evaluates each subproblem
Calls itself again (recursively) to refine or extend its reasoning
Combines the results into a final answer

You can think of it as "thinking in loops instead of straight lines."

This contrasts with the older assumption that more intelligence = more parameters. Recursive reasoning says: if you organize thinking better, you can get more out of less.

Why recursion might replace scale as the main growth lever

We're entering a new AI phase where the key question is shifting from:

"How big is your model?"
to
"How effectively can your system decompose, iterate, and verify its own thinking?"

Recursive reasoning helps in at least four ways:

Efficiency: Smaller models can approximate large-model performance on reasoning tasks
Reliability: Multi-step self-checks reduce obvious logical errors
Control: You can design explicit reasoning workflows, not just hope the model learns them
Modularity: Different recursive steps can use different models or tools

For teams building AI-powered products, this suggests a strategy shift:

Stop assuming you need the largest LLM for every feature
Start designing reasoning workflows – chains of smaller calls, tools, and checks

In 2025–2026, the winners will be the ones who master AI orchestration rather than just model selection.

3. Harvard's Dr. CaBot: The First AI Doctor in NEJM

While Samsung is rewriting the rules on model size, Harvard is reshaping what domain-specific AI can look like in high-stakes environments.

What is Dr. CaBot?

Dr. CaBot is a medical AI assistant developed at Harvard that just made history as the first AI system to be published in the New England Journal of Medicine (NEJM).

Instead of being a generic chatbot, Dr. CaBot is built as a:

Clinical reasoning partner: It works through differential diagnoses step‑by‑step
Guideline-aware assistant: It aligns recommendations with medical standards
Communication aid: It can help structure explanations and documentation

Importantly, it's evaluated not just for accuracy, but for how it compares to human doctors on structured clinical tasks.

Why NEJM publication is a big deal

Getting into NEJM is not a marketing launch – it's a scientific and clinical validation.

It signals that:

AI in medicine is moving from "cool demos" to peer-reviewed, regulated tools
The bar is shifting from generic performance claims to rigorous, controlled comparisons
Clinicians, administrators, and regulators can treat these tools as legitimate objects of policy and practice, not just experiments

For any industry, this is a template:
AI tools will only be trusted at scale when they meet the field's own gold-standard evidence thresholds. In health that's NEJM; in finance, it's audit and regulatory standards; in legal, bar and court-admissible practice; in marketing, it's hard performance data and attribution.

4. What These Breakthroughs Mean for AI Tools and Business Strategy

Put Samsung TRM and Dr. CaBot together, and you get a clear picture of where AI tools are heading in 2025:

Smaller, smarter reasoning cores
Domain‑tuned assistants that augment professionals
Workflows where orchestration beats raw scale

Small models vs LLMs: It's not either/or

The narrative of "small models vs LLMs" is misleading. The future is hybrid:

Small models (like TRM)
- Run on-device or at the edge
- Great for logic, routing, classification, safeguards
- Cheap, fast, and easier to customize
Large models (like Gemini 2.5 Pro, o3‑mini and successors)
- Better at broad understanding, creativity, and open-ended tasks
- Ideal for complex language generation and multimodal reasoning

Winning AI systems will:

Use tiny models for structure, control, and reasoning scaffolds
Call large LLMs only when necessary for generative depth
Wrap both with clear guardrails, monitoring, and evaluation

Practical implications for 2025 planning

For founders, marketers, and operators, here's how to respond:

Design around workflows, not just chatbots
Map your processes (e.g., lead qualification, support triage, campaign planning) and insert AI where reasoning, not just writing, is needed.
Adopt a tiered model strategy
- Tier 1: Tiny models for routing, first-pass logic, safety checks
- Tier 2: Mid-size models for most domain tasks
- Tier 3: Frontier LLMs for rare, complex, or high-value requests
Treat evaluation as a product feature
Borrow from NEJM-style rigor. Define your own benchmarks:
- Error rates, escalation rates, time saved
- Side‑by‑side comparisons with human baselines
Invest in recursive workflows
Instead of one-shot prompts, design pipelines where the system:
- Plans → executes → reviews → revises
- Calls tools, fetches data, and re‑checks assumptions along the way

These patterns mirror what Samsung and Harvard are doing in their domains – and they're directly applicable to marketing ops, sales enablement, product, and service delivery.

5. How to Prepare Your Team for the Next Wave of AI Reasoning

The technical breakthroughs are only half the story. The other half is organizational readiness.

Upskill your team around reasoning, not just prompting

Basic prompt engineering is now table stakes. The next skills your team needs:

Workflow design: Breaking business problems into AI-manageable steps
Tool thinking: Knowing when to pull in external data, calculators, CRMs, or APIs
Evaluation literacy: Understanding benchmarks, test sets, and error analysis

Encourage your team to document AI workflows as repeatable playbooks rather than ad-hoc chats. Those playbooks become assets you can optimize, automate, and scale.

Use AI as a thinking partner, not just a content machine

Samsung TRM and Dr. CaBot both highlight the same bigger idea: AI is moving from "type and get an answer" toward "co‑reasoning partner."

In practice, that looks like:

Asking AI to lay out options and trade‑offs, not just draft content
Having AI simulate scenarios: "What if we change this variable?"
Letting AI critique your own plans or assumptions step‑by‑step

Teams that adopt this mindset will be better prepared for the tools now emerging – ones that think with you, not just write for you.

Conclusion: Smaller, Smarter, and More Specialized

Samsung's TRM model and Harvard's Dr. CaBot are early but powerful signs of where AI is heading: toward smaller, smarter, more specialized systems that excel at reasoning and work alongside human experts.

For businesses and builders, the key takeaway is clear: stop fixating on the largest general-purpose model. Instead, focus on how different models, tools, and workflows combine to deliver reliable reasoning, measurable impact, and domain‑specific value.

As we move into 2026, the organizations that win with AI won't just ask, "Which model is strongest?" They'll ask, "How can we orchestrate many models – from tiny reasoners to frontier LLMs – into systems that think, adapt, and improve our real-world workflows?"