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

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2026-05-02 00:44:36 +00:00
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deliverables/proposals/index.md
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@@ -72,14 +72,14 @@ Summary: Proposal to enhance the Foreman Probe project by incorporating adaptive
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### Crimson Leaf Holdings -- Task 35ae3395-fa86-4127-8f66-33be420f4709
### Crimson Leaf Holdings -- Task b355bc30-424a-453e-b65d-a63e3a2a2849
Date: 2026-04-29
Status: AWAITING DAVID'S APPROVAL
Summary: Proposal for the Foreman Probe project to model probe tasks created by the Foreman to benchmark and evaluate LLM capabilities. This addresses the gap in comprehensive performance assessment by simulating diverse, Foreman-generated scenarios for agentic reasoning and task execution. It differs from prior proposals, which emphasized static metrics or external incubation, by focusing on dynamic modeling of the Foreman's own creative task processes to enhance iterative testing.
Summary: Proposal for the Foreman Probe project to architect a specialized simulation environment for modeling probe tasks designed by the Foreman. This addresses the lack of high-fidelity, sandbox-style testing grounds for observing LLM behavior in complex, multi-stage challenges. It differs from prior submissions by emphasizing the technical infrastructure required for safe, isolated execution and granular telemetry of probe results.
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### Crimson Leaf Holdings -- Task 5a82ccab-ef2c-4b9a-acef-1448deaa370b
### Crimson Leaf Holdings -- Task 981bad71-d772-4bbd-9591-dc1035e94fab
Date: 2026-04-29
Status: AWAITING DAVID'S APPROVAL
Summary: Proposal for the Foreman Probe project to model probe tasks created by the Foreman to benchmark and evaluate LLM capabilities. This addresses the gap in comprehensive performance assessment by simulating diverse, Foreman-generated scenarios for agentic reasoning and task execution. It differs from prior proposals, which emphasized static metrics or external incubation, by focusing on dynamic modeling of the Foreman's own creative task processes to enhance iterative testing.
Summary: Proposal for the Foreman Probe project to implement a feedback loop mechanism that integrates LLM performance data with Foreman task generation. This addresses the gap in iterative assessment and task refinement by enabling continuous improvement of benchmarking scenarios based on real-world LLM responses. It differs from prior proposals by introducing a closed-loop system that dynamically adjusts task complexity and relevance in response to LLM behavior.