OverSearchGuard
Conflict-aware evidence thinning for agentic RAG, with cost-aware robustness to duplicated noise.
OverSearchGuard is an evidence-governance layer for agentic RAG: it sits between retrieval and generation and selects a small, high-confidence, conflict-aware subset of evidence lines before calling the LLM — no fine-tuning required.
retrieval → OverSearchGuard → LLM
What it does
- Robust to “wrong-but-repeated” evidence via
cap-per-source(anti-dup / anti-spam). - Explicit reliability + recency modeling (source weights + half-life decay).
- Cost-aware policy: reports TPC (total tokens per correct answer) and supports optional BEA (Budgeted Evidence Accumulation).
Results (reproducible)
Benchmark: paper suite (n=300, google/flan-t5-small, CUDA).
- Accuracy:
0.143 → 0.970(bea_fallback, best) - Avg total tokens:
491.5 → 117.3
Quick use (library)
from oversearchguard.api import build_thin_prompt
payload = build_thin_prompt(question, evidence_lines)
prompt = payload["prompt"] # send to any LLM
candidate = payload["candidate"] # CACT candidate (useful for guardrails)