The Algorithm Whisperer: Our Newest SLM Specialist

Here's something nobody talks about: the hardest part of implementing algorithms isn't understanding the algorithm. It's translating from the beautiful, hand-wavy pseudocode in your textbook to the cranky, type-checking, memory-managing reality of C++.

You've seen it a thousand times. The algorithm textbook shows you Dijkstra's shortest path in clean pseudocode: for each vertex v in graph...Then you sit down to implement it in C++ and suddenly you're drowning in questions.std::priority_queue or std::set? Wait, do I need std::greater or std::less? How do I handle the adjacency list? Should this be a reference or a copy? Is this move-constructible?

Most LLMs can't help you here. They'll give you code that compiles but uses the wrong data structure. Or code that works but runs in O(n²) when it should be O(n log n). They don't understand the why behind algorithm design—they just pattern-match syntax.

So we built a model that understands both worlds. Meet Algorithm SLM(Algo-7B): our 8th specialist, trained to be fluent in pseudocode, complexity analysis, and idiomatic C++ implementations. Think of it as a bridge between algorithm theory and production code.

1. The Pseudocode Problem Nobody Solves

Pseudocode is the universal language of algorithms. Every textbook uses it. Every paper uses it. Every whiteboard interview uses it. But it's also a lie—a beautiful, seductive lie that hides all the messy implementation details.

function BinarySearch(array, target):
    left = 0
    right = length(array) - 1

    while left <= right:
        mid = (left + right) / 2
        if array[mid] == target:
            return mid
        else if array[mid] < target:
            left = mid + 1
        else:
            right = mid - 1

    return -1  // not found

template<typename RandomIt, typename T,
         typename Compare = std::less<>>
std::optional<typename std::iterator_traits<RandomIt>::difference_type>
binary_search(RandomIt first, RandomIt last, const T& target,
              Compare comp = Compare{}) {
    auto left = first;
    auto right = std::prev(last);

    while (left <= right) {
        // Wait, overflow! Should be:
        auto mid = left + std::distance(left, right) / 2;

        if (*mid == target) {
            return std::distance(first, mid);
        } else if (comp(*mid, target)) {
            left = std::next(mid);
        } else {
            right = std::prev(mid);
        }
    }

    return std::nullopt;
}

This gap is why algorithm courses and production code feel like different universes. The pseudocode doesn't tell you:

• Should this be a template or a concrete function?
• What iterator category do I need? (ForwardIterator? RandomAccessIterator?)
• How do I handle const-correctness?
• When should I use std::move?
• What's the right return type for "not found"? (exception? optional? iterator?)
• Do I need noexcept specifications?

This is where Algo-7B comes in. It doesn't just translate syntax—it understands the semantic mapping between algorithm concepts and C++ idioms.

2. Training a Model That Speaks Both Languages

Algo-7B is trained on 170 billion tokens from a very specific curriculum. Not random code from GitHub— carefully curated pairs of algorithm descriptions, pseudocode, complexity analysis, and correct implementations.

3. What Can Algo-7B Actually Do?

Let's get concrete. Here are the kinds of queries Algo-7B handles well:

template<typename RandomIt, typename Compare = std::less<>>
void quicksort_3way(RandomIt first, RandomIt last, Compare comp = {}) {
    if (std::distance(first, last) <= 1) return;

    auto pivot = *first;
    auto eq_begin = std::partition(first, last,
        [&](const auto& x) { return comp(x, pivot); });
    auto eq_end = std::partition(eq_begin, last,
        [&](const auto& x) { return !comp(pivot, x); });

    quicksort_3way(first, eq_begin, comp);
    quicksort_3way(eq_end, last, comp);
}

for (int i = 0; i < n; i++) {
    for (int j = i; j < n; j *= 2) {
        process(j);
    }
}

std::vector<int> dist(n, INT_MAX);
for each vertex u not yet visited:
    v = vertex with minimum dist[v]
    for each neighbor w of v:
        relax(v, w)

4. The Complexity Oracle: Beyond Big-O Handwaving

One of Algo-7B's superpowers is complexity analysis. Not just parroting "this is O(n log n)"— actually reasoning through the analysis step by step.

void mystery(int n) {
    for (int i = 1; i <= n; i *= 2) {
        for (int j = 0; j < i; j++) {
            process();
        }
    }
}

5. The Data Structure Whisperer

Ask Algo-7B "what data structure should I use?" and you won't get a generic answer. It considers your access patterns, insertion/deletion frequency, memory constraints, and cache behavior.

6. How Algo-7B Fits in the Ensemble

Algo-7B is our 8th specialist, and it doesn't work alone. The orchestrator routes algorithm-heavy queries to Algo-7B, but often coordinates with other models:

The ensemble is more powerful than any individual specialist. Algorithm design doesn't happen in a vacuum—it needs debugging, optimization, and integration with real codebases.

7. Technical Specifications

Why This Matters

Algorithms are the foundation of software engineering. But the gap between understanding an algorithm and implementing it correctly in production code is huge. Most developers struggle here—not because they're bad at algorithms, but because the translation layer (from pseudocode to C++) is full of gotchas.

Algo-7B is our answer to this problem. It's not trying to replace algorithm textbooks or competitive programming practice. It's a tool to help you move faster from "I understand this algorithm" to "I wrote correct, efficient C++ that actually works."

And because it's a 7B specialist (not a 70B generalist), it runs on your local workstation. You don't need cloud APIs or GPU clusters. Just a GB10 GPU and you're set.