--- a/doc/v2_planning/architecture.txt	Fri Sep 17 16:59:08 2010 -0400
+++ b/doc/v2_planning/architecture.txt	Fri Sep 17 17:07:52 2010 -0400
@@ -140,4 +140,18 @@
 associated with them being hashable as well.
 
 
+Benchmark
+=========
 
+During the general meeting on sept. 17th, we agreed to produce at least pseudo-code (if
+possible, actual code) for the following model:
+A Deep Belief Net (with greedy layerwise pre-training, and supervised
+fine-tuning), with preprocessing of the data, double cross-validation, and
+save/load of the model.
+
+The different approach to be tested are:
+    - Plugins with a global scheduler driving the experiment (Razvan's team)
+    - Objects, with basic hooks at predefined places (Pascal L.'s team)
+    - Existing objects and code (including dbi and Jobman), with some more
+      pieces to tie things together (Fred B.)
+

--- a/doc/v2_planning/committees.txt	Fri Sep 17 16:59:08 2010 -0400
+++ b/doc/v2_planning/committees.txt	Fri Sep 17 17:07:52 2010 -0400
@@ -2,19 +2,24 @@
 
 * Existing Python ML libraries investigation: GD, DWF, IG, DE
 * Dataset interface: DE*, OB, OD, AB, PV
-* Learners: AB, PL, GM, IG, RP, NB, PV
+* Learners: AB, PL*, GM, IG, RP, NB, PV
 * Optimization: JB*, PL, OD
 * Inference/sampling: JB, GD*, AC
 * Job management, analysis, metrics, costs, visualization: GD, FS, PL, XM
 * Formulas/tags: FB*, RP, AC, OB
-* Coding style: DE, OD*, DWF, FB
+* Coding style (finished): DE, OD*, DWF, FB
+* architecture (plugins/hooks/flags/?):
+    o Plugins: Razvan P.*, Guillaume D.
+    o Hooks: Pascal L.*, Ian G., Olivier B.
+    o Existing: Fred B.*
+* Layers: Razvan P., Xavier G., Arnaud B., David W. F.
+* social engineering, code review and incentives: Fred B.*, Pascal L., James B., Olivier D.
 
 Issues to be tackled in the future:
 
 * serialization & reproducibility 
 * job management, results analysis, metrics & costs, visualization
 * GPU portability
-* social engineering, code review and incentives
 
 Job of each committee: