FAQ

compression aware intelligence is a system contract that detects, scores, and governs the effects of compression at each step of an AI or human information pipeline so that outputs stay within evidence and uncertainty is explicit.

for the full statement and claims see Proof.

rag retrieves. cai governs. cai maps compression sites, scores tension, attaches provenance, and enforces abstention if claims are not entailed by sources or internal state. it spans retrieval, verification, scoring, and audit rather than only search.

in practice this looks like: semantic stability benchmarks, a coherence field monitor, and abstention thresholds in your inference loop.

for open ended generation no. cai can guarantee no unsupported claims only when outputs are restricted to verified evidence and the system abstains if entailment or thresholds fail.

no. information bottleneck studies representation sufficiency under a compression objective. cai operationalizes compression sites across an end to end system with provenance, per step tension scoring, abstention policy, and audit surfaces.

ib is an analysis lens. cai is governance and execution. see Foundations for what cai adds.

prior work connects compression to learning and curiosity. cai is distinct because it treats compression as an auditable variable with thresholds, provenance, and abstention that gate claims. the unit of analysis is the compression site with a measurable tension score.

no. any pipeline that compresses information can be instrumented, including data cleaning, schema mapping, retrieval, summarization, tool use, and human review.

a normalized per site metric that combines information loss, task degradation, provenance gaps, and uncertainty. it estimates the risk cost of a compression step and feeds path level aggregation.

formal definitions live in Foundations and the overview on zenodo.

the coherence field score is a single 0 to 1 stability indicator derived from multiple signals such as semantic equivalence consistency, rhythm stability, ensemble agreement, and waste heat. it reads how stable a model is being under compression and small shocks.

see The Coherence Field for details and integration examples.

yes. start with the overview on zenodo, then the foundations page.

• cai overview
• foundations

1. tag compression sites in your pipeline
2. run baseline vs cai gated
3. measure accuracy, unsupported claim rate, abstention rate, mean time to review, and escalation patterns
4. report per site tension distributions and path scores

the minimal scorecard lives on Proof. starter tasks and a semantic equivalence benchmark are on Dataset.

lower unsupported claims at equal or better accuracy, calibrated abstention, faster human review through provenance, simpler audit trails, and early warning for coherence drift in deployed systems.

prototype repos and scripts live here:

• compression-aware-intelligence-demo

for end to end patterns see implementations.

yes. you can run local only scoring, log tension metrics without raw text, and use tiered provenance views for different roles. examples are on implementations.

no. cai is model agnostic. it instruments the pipeline and sources you already use and can run purely at inference.

please cite the overview and the site. example bibtex:

@misc{joseph_cai_overview,
  author = {Michele Joseph},
  title = {Compression-Aware Intelligence: Overview and Reference Pipeline},
  year = {2025},
  howpublished = {Zenodo},
  url = {https://zenodo.org/records/16467706}
}

also reference the website: https://compressionawareintelligence.com

open issues or pull requests on github, propose evaluation datasets with tagged compression sites, or contact us to run a pilot.

• github issues
• contact
• media