Hitting a wall with local LLMs: How to break through 77% accuracy in logic judgments?
AZGhost · reddit · 2026-08-08
The author encountered reasoning bottlenecks when using locally run LLMs (like Gemma 4 12B) for network security configuration tasks. Even with complete context (e.g., device configs and CVE rules), the model struggles with complex logic like version-range comparisons and negations, causing accuracy to plateau around 77%.
Various optimizations were tested, such as setting temperature to 0, reusing KV-cache, and tuning output limits, but reasoning failures persisted. Due to configuration sensitivity, offloading to frontier cloud models is not an option.
The author proposes several directions for the community to discuss:
- Larger models: Exploring 27B+ local models that perform better on policy-comparison tasks.
- Task decomposition: Deterministically parsing configs into structured feature facts first, reducing the LLM's job to simple label matching.
- Verification layers: Implementing two-pass self-verification or small-ensemble voting to resolve disagreements.
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