diff --git a/briefs/2026/08/2026-08-04.md b/briefs/2026/08/2026-08-04.md
index 90122ab..f5498da 100644
--- a/briefs/2026/08/2026-08-04.md
+++ b/briefs/2026/08/2026-08-04.md
@@ -14,15 +14,23 @@ sources_checked:
 
 ## Headline deltas
 
-- No material delta.
+- DeepSeek-V4-Flash-0731 official weights released on Hugging Face under MIT license.
+- Model size confirmed at 12B parameters, making it suitable for consumer GPU deployment (Axis B).
+- DeepSeek-V4-Flash-0731 is now a strong candidate for the R-tier (runnable spine) due to its size and license, potentially augmenting or replacing DeepSeek-R1-Distill-14B.
+- Official API pricing for DeepSeek-V4-Flash-0731 confirms ultra-cheap tier, undercutting some budget closed models.
+- Kimi K3 collection remains deferred; V4-Flash-0731 offers a viable, smaller alternative for S1 strategic capability.
 
 ## Frontier & commercial (axis A)
 
-*(none)*
+| Item | Delta | Sources | Lab relevance |
+| ---- | ----- | ------- | ------------- |
+| DeepSeek-V4-Flash-0731 API pricing | Official pricing page updated: $0.12/M in, $0.25/M out. Confirmed to be in the ultra-cheap tier, undercutting previous DeepSeek V3.x and some budget closed models. | DeepSeek official pricing page (https://deepseek.com/pricing) | Reinforces potential for re-routing bulk classify/draft tasks to an even lower-cost API. Update routing policies for cost optimization. |
 
 ## Edge / local / open (axis B)
 
-*(none)*
+| Item | Delta | Sources | Lab relevance |
+| ---- | ----- | ------- | ------------- |
+| DeepSeek-V4-Flash-0731 open weights release | Official weights released on Hugging Face. Model card confirms 12B parameters, MIT license, and focus on efficient local deployment. Quantized versions (Q4/Q5) are also available. | Hugging Face model card: deepseek-ai/DeepSeek-V4-Flash-0731 (https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) | Directly relevant for Axis B. At 12B params, it fits the 'Consumer GPU' class for daily coding assist, RAG chat, tool loops. Strong candidate for R-tier collection, potentially augmenting or replacing |
 
 ## Training & specialization (axis C)
 
@@ -34,8 +42,13 @@ sources_checked:
 
 ## Collection candidates
 
-*(none)*
+| id | org | name | priority | reason | approx size | license |
+| -- | --- | ---- | -------- | ------ | ----------- | ------- |
+| DeepSeek-V4-Flash-0731 | deepseek-ai | DeepSeek-V4-Flash-0731 | high | Official weights released under MIT license, 12B parameters make it suitable for consumer GPU. Strong candidate for R-tier runnable spine due to efficiency and license, or as a smaller S1 strategic al | 12B parameters (~24 GiB fp16, ~7-8 GiB Q4) | MIT |
 
 ## Lab implications
 
-- *(none)*
+- Initiate collection of DeepSeek-V4-Flash-0731 (preferably Q4/Q5 quantized version) using `scripts/collect_model.py` immediately. This model is a high-priority R-tier candidate.
+- Re-evaluate the R-tier collection strategy: DeepSeek-V4-Flash-0731 (12B) potentially offers a more efficient and capable alternative to DeepSeek-R1-Distill-14B (14B) for local reasoning, given its 'Flash' optimization.
+- Update `inventory/catalog/DeepSeek-V4-Flash-0731.yaml` with `status: approved` and `collection.brief_refs`.
+- Monitor performance of DeepSeek-V4-Flash-0731 against existing R-tier models (Qwen3-8B, DeepSeek-R1-Distill-14B) once collected and integrated into local harnesses.