WiCER: Wiki-memory Compile, Evaluate, Refine Iterative Knowledge Compilation for LLM Wiki Systems

Researchers introduce WiCER, an iterative algorithm that solves the "compilation gap" in LLM Wiki systems—the problem of distilling raw documents into persistent knowledge artifacts without losing critical facts. The method recovers 80% of lost quality and reduces catastrophic failures by 55%, outperforming naive compilation approaches while maintaining sub-second latency advantages over traditional RAG systems.

WiCER addresses a fundamental tension in modern LLM architectures: the promise of persistent knowledge compilation versus the practical challenge of information loss during the distillation process. The research reveals that while full-context KV cache inference theoretically outperforms retrieval-augmented generation (RAG) on curated knowledge—achieving 4.38 vs. 4.08 scores with 7.3x faster time-to-first-token—this advantage collapses at scale due to attention dilution and irrelevant context interference. Blind compilation strategies catastrophically fail with 53-60% failure rates, making naive wiki generation impractical.

The breakthrough lies in WiCER's counterexample-guided refinement approach, which treats compilation as an iterative diagnosis-and-fix process. Rather than attempting perfect distillation in one pass, the algorithm identifies specific dropped facts through targeted diagnostic probes, then forces their preservation in subsequent iterations. This targeted approach proves substantially more effective than generic pinning strategies, delivering a 0.95-point quality gain versus 0.16 for unfocused interventions across the 17-domain benchmark.

For the broader AI infrastructure landscape, WiCER represents progress toward operationalizing knowledge compilation at scale. The work validates that KV cache inference can match or exceed RAG performance when combined with principled compilation methods, potentially reducing inference latency and retrieval failures in production systems. The methodology's release of code and benchmarks accelerates reproducible research in wiki-memory systems, a category gaining traction as models scale beyond context windows of hundreds of thousands of tokens. This compounds existing advantages in specialized domains where RAG retrieval latency and occasional lookup failures create user-facing degradation.

• →WiCER's iterative refinement closes the compilation gap, recovering 80% of quality loss in one to two iterations across tested domains.
• →Targeted diagnosis of dropped facts outperforms generic preservation strategies by 5.9x in effectiveness (0.95 vs. 0.16 quality gains).
• →Wiki-memory systems with proper compilation match full-context KV cache inference performance while eliminating catastrophic 53-60% failure rates.
• →Sub-second latency advantage of persistent knowledge artifacts persists at scale when combined with diagnostic-driven compilation methods.
• →Algorithmic approach applies CEGAR principles from formal verification to LLM knowledge distillation, offering reusable methodology for similar compilation problems.