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README.md

@@ -14,14 +14,14 @@ tags:
 - decision-making
 ---
 
-# ⚡ Tasksource-JEV-Nano (`tasksource-jev-nano-v0`)
+# ⚡ Tasksource-JEV-Nano-v1 (`tasksource-jev-nano-v1`)
 
 **SOTA High-Throughput Native Decision Model (\<200M Parameters)**
 *Decoupled Multi-Vector Late Interaction for Fast, Calibrated System-One Decisions*
 
 [![Decision Index](https://img.shields.io/badge/Decision_Index_Raw-32.03-blue)](https://github.com/sileod/decision-models) [![Decision Index Skill](https://img.shields.io/badge/Decision_Index_Skill-10.32-green)](https://github.com/sileod/decision-models)
 
-Weights updated 2026-10-06 to the step-4000 single checkpoint (plain grouped multi-question exposure, 16k unique requests) — no soup. Previous weights scored Raw 30.65 / Skill 9.58; current weights score Raw 32.03 / Skill 10.32 on the same full official suite. It uses the simple release inference path: state + single question (`S + Q_i`) with independently encoded options.
+Successor to [`tasksource-jev-nano-v0`](https://huggingface.co/tasksource/tasksource-jev-nano-v0) (Raw 30.65 / Skill 9.58, kept immutable for reproducibility). v1 is a single continued checkpoint — no soup — trained with plain grouped multi-question exposure (16k unique requests) on top of the v0 lineage. It uses the simple release inference path: state + single question (`S + Q_i`) with independently encoded options.
 
 Built on [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) and [`lightonai/LateOn`](https://huggingface.co/lightonai/LateOn) (~149M params, 8192 context). Handles choice (K=2 to 150+), NOUL, and score decisions.
 
@@ -47,7 +47,7 @@ Built on [`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/Mode
 ```python
 from modernjev.multivector.lateon import LateOnDecisionScorer
 
-scorer = LateOnDecisionScorer(model_name_or_path="tasksource/tasksource-jev-nano-v0")
+scorer = LateOnDecisionScorer(model_name_or_path="tasksource/tasksource-jev-nano-v1")
 out = scorer.decide_uncached(
     states=["Customer reports unauthorized international wire transfer of $4,500."],
     questions=["Select the appropriate fraud mitigation routing:"],
@@ -62,7 +62,7 @@ Or raw PyLate:
 
 ```python
 from pylate import models
-model = models.ColBERT(model_name_or_path="tasksource/tasksource-jev-nano-v0")
+model = models.ColBERT(model_name_or_path="tasksource/tasksource-jev-nano-v1")
 ctx = model.encode(["<state>\nQuestion: <question>"], is_query=False, convert_to_tensor=True)[0]
 opts = model.encode(["<option 1>", "<option 2>"], is_query=True, convert_to_tensor=True)
 ```
@@ -74,7 +74,7 @@ opts = model.encode(["<option 1>", "<option 2>"], is_query=True, convert_to_tens
   title={Tasksource-JEV-Nano: Decoupled Multi-Vector Late Interaction for Typed Decisions},
   author={Sileo, Damien},
   year={2026},
-  howpublished={\url{https://huggingface.co/tasksource/tasksource-jev-nano-v0}},
+  howpublished={\url{https://huggingface.co/tasksource/tasksource-jev-nano-v1}},
 }
 ```