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index: add proposal {task.id} to proposal index

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2026-05-01 23:49:43 +00:00
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deliverables/proposals/index.md
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@@ -1,4 +1,5 @@
# PROPOSAL INDEX -- MASTER RECORD
```text
# PROPOSAL INDEX -- MASTER RECORD
### Crimson Leaf Holdings -- Task a112b485-a81c-4a77-bcc3-83a5191577b2
Date: 2026-04-29
@@ -42,9 +43,8 @@ Date: 2026-04-29
Status: AWAITING DAVID'S APPROVAL
Summary: Proposal for the Foreman Probe project to develop and model probe tasks generated by the Foreman for advanced LLM benchmarking and evaluation. It fills the gap in scalable, real-world LLM testing by creating a pipeline of Foreman-curated challenges that probe agentic reasoning, tool use, and long-horizon planning. This differs from prior proposals by introducing a modular task templating system derived from Foreman outputs, enabling customizable difficulty scaling and cross-domain adaptability not present in earlier static or simulation-focused approaches.
---
### Crimson Leaf Holdings -- Task 3b27ec7d-75c6-47a2-887b-46b911179af5
### Crimson Leaf Holdings -- Task e89c6cc6-b077-423f-b74a-0ac71cc6483c
Date: 2026-04-29
Status: AWAITING DAVID'S APPROVAL
Summary: Proposal for the Foreman Probe project to implement a structured framework for modeling and executing probe tasks designed specifically by the Foreman to stress-test LLM agentic limits. This addresses the need for high-fidelity evaluation environments that mirror the Foreman's operational complexity, filling the gap between general benchmarks and specialized workflow requirements. It differs from prior iterations by prioritizing the technical orchestration of the probe environment over mere task description, ensuring reproducible stress-testing results.
Summary: Proposal for the Foreman Probe project which aims to model probe tasks created dynamically by the Foreman to benchmark LLM's agentic capabilities. This project addresses the critical gap in adaptive LLM evaluation methodologies. This approach differs from prior proposals by focusing on emulating the Foreman's task creation process for more real-world assessment of LLMs in dynamic environments.
```