u/Live_Invite_885

INT4 quantization is ubiquitous in ML right now, but evaluating dot products on hardware without native SIMD/vector instructions (like WebAssembly or older ARM chips) usually requires slow sequential loops. A classic workaround is SWAR (SIMD Within A Register), but deriving the bitwise operations by hand to unpack, multiply, and sum eight 4-bit integers packed into a single 32-bit register is tedious and error-prone.

Instead of writing the bit-hack manually, I wrote a pipeline that uses an SMT solver to discover the exact bitwise formula from scratch, and then uses a theorem prover to mathematically guarantee its correctness.

Here is a breakdown of the technical process:

1. Synthesis via CEGIS Loop (Z3) I set up a Counter-Example Guided Inductive Synthesis (CEGIS) loop in Python using the Z3 SMT solver. The solver is given a ground-truth specification (naive loop: extract nibbles, sign-extend, multiply, sum) and a bounded set of allowed instructions (AND, OR, XOR, ADD, SUB, MUL, shifts). Z3 searches the space of possible instruction sequences. If it finds a candidate, we test it against random inputs. If it fails, the failing input is added to Z3's constraints, and it tries again. Eventually, it converges on a pure, branchless sequence of operations.

2. The Generated Math The resulting algorithm utilizes a known multiplier trick for byte-reversals, but Z3 managed to perfectly interleave the even/odd nibble extraction. For example, part of the generated code handles even/odd multiplications by exploiting 32-bit hardware multiplications: (ea_low * eb_low_rev) >>> 16 This evaluates two 4-bit multiplications at opposite ends of the register simultaneously without cross-talk.

3. Formal Proof in Lean 4 Passing a million random tests is nice, but it’s not a mathematical guarantee for bit-hacks. To prove there are absolutely no edge cases or overflow bugs among the 2^(64) possible input combinations (two 32-bit registers), I ported the synthesized SWAR function to Lean 4. By leveraging Lean's bv_decide (BitVec SAT solver) and omega for modular arithmetic, Lean compiles the equivalence check into a boolean satisfiability problem. The proof successfully verifies that swar_dot_product a b = ground_truth_dot_product a b for all possible inputs.

If anyone is interested in how the Z3 synthesis script is structured or wants to see the Lean 4 proof, I've put the source code:
https://github.com/Peloxerat/int4-swar-dotprod

I'd be curious to hear if there are ways to constrain Z3 to find an even shorter instruction path.

Hi everyone! I had a thought about creating a project based on autonomous AI agents. The idea is to automate the reverse-engineering and improvement of Wine. Does anyone know if there is already work being done in this area, or has anyone seen anything similar?

Here is what the planned cycle (AI Loop) looks like:

Generation: The AI writes hundreds of micro-tests for Windows API functions using random, invalid, and edge-case arguments.
Execution: The tests are run in parallel on a real Windows machine and on the current build of Wine.
Analysis: If the behavior differs (error codes, memory dumps, crashes), the AI studies the difference.
Patching: The AI itself writes a C patch for the Wine source code to exactly replicate the Windows behavior. (This is then manually reviewed by a user to avoid AI slop).
Verification: The code is compiled and tested again. The cycle repeats until there is a 100% match. The result is a fully prepared Pull Request.

My question to the community: Why isn't such a pipeline already running among enthusiasts? What's the main catch?

I have a few guesses:

* Clean-room design? Is there a risk of lawsuits because the LLM might have been trained on leaked Windows source code, thus violating the "clean-room" principle? Honestly, this seems unlikely; in essence, this approach aligns with clean-room design and isn't much different from how Wine is currently developed.

* Hallucinations? Are neural networks still too bad at system-level C code and inevitably going to cause memory leaks? I doubt this too; modern models are already at a level where they can write decent C code, just like the AI code in the Linux kernel.

* Sandbox complexity? Is it too difficult and expensive to automate thousands of test runs at the OS level? Possible, but not critical.

* Spaghetti code? The Windows API is highly intertwined, so one patch might break 10 other functions, and the AI just won't be able to hold all that in its context? This seems like the biggest problem of them all.

I understand this sounds somewhat naive, but I am genuinely curious. Hoping for replies without hate.

Synthesizing and formally verifying a SWAR bit-hack for INT4 dot products using Z3 and Lean 4 [P]

Automating Wine Reverse Engineering and Patching Using AI Agents