Research & Evaluation Examples

1. Objective

Measure retrieval completeness, chunk boundary preservation, and citation faithfulness in compliance Q&A pipelines when queries depend on date-bound exceptions located in disparate policy appendixes.

2. Dataset

1,200 synthetic and open-source corporate governance policy documents comprising 4,500 ground-truth query-answer pairs. Each test pair incorporates cross-document dependencies where a baseline policy clause is amended or exempted in a secondary section.

3. Method

Dual-pass deterministic testing comparing two retrieval pipelines against a frozen instruction model: (A) Dense vector similarity search with a standard 512-token chunking window; (B) Hybrid retrieval (dense embeddings + BM25 keyword matching) paired with a cross-encoder re-ranking model scoring top-25 chunks down to top-5.

4. Metrics

We evaluate four automated metrics verified against human annotator rubrics: Context Recall @ k (percentage of required evidence chunks retrieved), Context Precision @ k (signal-to-noise ratio in prompt context), Faithfulness Groundedness (verifiable alignment between output claims and retrieved context), and Answer Relevance.

5. Results (Sample Telemetry)

6. Failure Analysis

Root cause analysis revealed that in 82% of pipeline A errors, the correct amendment chunk was retrieved at rank 7 or 8, falling outside the top-5 generator context window. The generator was not hallucinating; the retrieval step had omitted the critical exception evidence. Introducing the cross-encoder re-ranking stage promoted these amendment clauses into the top-3 positions.

7. Limitations

This evaluation suite focused on text-based regulatory documents. Scenarios involving dense tabular appendices or embedded flowchart diagrams require separate multimodal OCR evaluation harnesses.

1. Objective

Quantify how frequently AI code generation models introduce silent compilation breakage and unresolved imports during multi-file refactoring tasks across large TypeScript codebases.

2. Dataset

450 sandboxed TypeScript refactoring scenarios derived from open-source repositories (averaging 35,000–50,000 lines of code) with strict compiler settings (`strict: true`, `noImplicitAny: true`) and comprehensive unit test coverage.

3. Method

Isolated Docker container harnesses execute patch generation across candidate models. Each proposed diff is subjected to a 3-stage validation pipeline: (1) `git apply` patch verification; (2) `tsc --noEmit` deterministic type checking; (3) `jest` automated test suite execution.

4. Metrics

Clean patch application rate, TypeScript compilation pass rate, Pass@1 on full test suite, and AST regression rate (syntax changes breaking unrelated modules).

5. Results (Sample Telemetry)

6. Failure Analysis

In 71% of compilation failures under direct generation, the model correctly renamed or refactored a method signature in the primary implementation file, but failed to update corresponding re-exports in module `index.ts` barrel files. Supplying automated compiler diagnostics back to the agent in a single retry loop resolved over half of these export drift errors.

7. Limitations

All evaluations ran in containerized single-host environments with simulated local dependencies. Complex microservice interactions involving distributed network state were not part of this specific benchmark harness.

1. Objective

Evaluate autonomous agent execution stability, loop detection, and backoff adaptation when downstream third-party REST tools return transient error codes (HTTP 429 Rate Limit, HTTP 503 Service Unavailable).

2. Dataset

600 synthetic multi-step agent scenarios across 18 mocked enterprise tools (billing query, customer CRM lookup, inventory management, ticket creation). In 35% of runs, controlled transient errors are injected into intermediate tool calls.

3. Method

State-machine instrumentation tracking agent trajectory graphs. We evaluate two tool schema configurations: (A) Generic raw HTTP error strings; (B) Structured JSON error payloads providing explicit retry directives and exponential backoff flags.

4. Metrics

Tool selection accuracy, Infinite loop occurrence rate (> 4 repetitive calls without state advancement), Successful backoff adaptation rate, and Token waste multiplier on failed traces.

5. Results (Sample Telemetry)

6. Failure Analysis

When presented with generic raw 503 error strings, agents consistently misinterpreted the status as a query parameter validation failure, leading them to hallucinate alternative search arguments rather than waiting or utilizing fallback mechanisms. Explicit structured error types corrected this behavioral pathology.

7. Limitations

Mocked environments provide deterministic response latencies. Real-world asynchronous network conditions and partial failures require runtime telemetry monitoring alongside pre-deployment benchmark runs.

1. Objective

Audit cell extraction accuracy, cross-column association fidelity, and visual hallucination rates in multimodal vision-language models analyzing complex presentation slides and financial disclosures with merged cells.

2. Dataset

800 high-density financial tables and slide decks with varied visual noise, irregular border formatting, multi-tiered headers, and colored shading. Ground-truth cell values and coordinates are double-annotated by expert QA reviewers.

3. Method

Automated bounding-box intersection evaluation comparing raw vision-language model direct extraction against a two-stage pipeline combining layout pre-segmentation with targeted region crop parsing.

4. Metrics

Cell value extraction accuracy, Cross-column header association fidelity, Spurious digit hallucination rate, and Tabular Markdown structural validity.

5. Results (Sample Telemetry)

6. Failure Analysis

Failures clustered heavily around vertically merged sub-headers where the baseline model inferred numeric values from adjacent categories rather than recognizing hierarchical scope boundaries. Explicit visual pre-segmentation bounding boxes eliminated these cross-row misattributions.

7. Limitations

Evaluation performed on 300 DPI source images. Image compression below 150 DPI significantly degraded numeric character distinction between '3', '8', and '9'.