docs: translate decision models vs rerankers and unify URLs

src/posts/decision-vs-reranker.md

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+---
+date: 2026-10-22
+category:
+  - technical
+tag:
+  - AI/ML
+---
+# Decision Models vs. Rerankers in Recommendation Ranking
+
+In [Build Recommender System with LLM-based Reranker](./llm-ranker.md), we explored turning a user’s past interests into a query and using a reranker to rank candidate items. With the emergence of decision models such as Jev, recommender systems have another option: treat “Will this user like this item?” as a judgment task and rank items directly by the probabilities returned by the decision model.
+
+Both approaches can rank candidate items using text descriptions of a user’s past interests and profile, without first training a ranking model on self-maintained data. So which is better suited to recommender systems: rerankers trained for relevance ranking, or decision models trained for judgment tasks? This post compares their ranking performance on the MovieLens 100K dataset using Gorse’s `gorse-bench` tool.
+
+## How the Two Types of Models Differ
+
+A reranker takes a `query` and multiple `documents`, then returns a relevance score for each document relative to the query. A decision model such as Jev takes a `state` and a set of `questions`, returning a structured result for each question. It supports `choice` (selection), `score` (scoring), and `noul` (true-or-false judgment). For `noul`, the returned value ranges from 0 to 1 and represents the probability that a proposition is true. A recommender system can use this type of judgment to score candidate items.
+
+The following examples show requests and responses for decision models and rerankers using the APIs provided by llama.cpp:
+
+::: code-tabs
+
+@tab:active Decision API
+
+```bash
+$ curl --request POST 'http://localhost:8080/v1/systemone' \
+  --header 'Authorization: Bearer YOUR_API_KEY' \
+  --header 'Content-Type: application/json' \
+  --data-binary @- <<'JSON'
+{
+  "model": "OpenJev-GGUF",
+  "state": "A user likes the following movies:\n- The Lion King (1994). A animation, children's, musical movie.\nPlease recommend more movies.",
+  "questions": {
+    "1": {
+      "type": "noul",
+      "instructions": "Toy Story (1995). A animation, children's, comedy movie."
+    },
+    "2": {
+      "type": "noul",
+      "instructions": "GoldenEye (1995). A action, adventure, thriller movie."
+    }
+  }
+}
+JSON
+{
+  "model": "OpenJev-GGUF",
+  "answers": {
+    "1": {
+      "type": "noul",
+      "noul": 0.91
+    },
+    "2": {
+      "type": "noul",
+      "noul": 0.35
+    }
+  }
+}
+```
+
+@tab Reranker API
+
+```bash
+$ curl --request POST 'http://localhost:8080/v1/rerank' \
+  --header 'Authorization: Bearer YOUR_API_KEY' \
+  --header 'Content-Type: application/json' \
+  --data-binary @- <<'JSON'
+{
+  "model": "Qwen3-Reranker-8B-GGUF",
+  "query": "A user likes the following movies:\n- The Lion King (1994). A animation, children's, musical movie.\nPlease recommend more movies.",
+  "documents": [
+    "Toy Story (1995). A animation, children's, comedy movie.",
+    "GoldenEye (1995). A action, adventure, thriller movie."
+  ]
+}
+JSON
+{
+  "model": "Qwen3-Reranker-8B-GGUF",
+  "results": [
+    {
+      "index": 0,
+      "relevance_score": 0.87
+    },
+    {
+      "index": 1,
+      "relevance_score": 0.24
+    }
+  ]
+}
+```
+
+:::
+
+
+
+## Dataset and Evaluation Method
+
+We use the MovieLens 100K dataset. The dataset contains 943 users, 1,682 movies, and 100,000 ratings on a scale from 1 to 5. The experiment treats ratings of 4 or higher as positive feedback and all other rated entries as negative samples:
+
+```toml
+[recommend.data_source]
+positive_feedback_types = ["rating>=4"]
+read_feedback_types = ["rating"]
+```
+
+::: info
+
+To reproduce the experiment, start with the [base configuration file](https://github.com/gorse-io/gorse/blob/master/client/config.toml), which does not include reranker or decision API settings. Download the [ready-to-import dataset](https://cdn.gorse.io/example/github.bin.gz) and follow the [Quick Start](../docs/quick-start.md) to import it.
+
+:::
+
+All models run on llama.cpp and use Q4_K_M quantization. The rerankers and decision models use identical prompt templates. The following examples show the configurations.
+
+::: code-tabs
+
+@tab:active Decision API Configuration
+
+```toml
+[recommend.ranker.decision_api]
+url = "http://127.0.0.1:8080/v1/systemone"
+auth_token = "YOUR_API_KEY"
+model = "OpenJev-GGUF"
+state_template = """
+A user likes the following movies:
+{% for item in feedback -%}
+- {{ item.Comment }}. A{% for category in item.Categories %}{% if not loop.first %},{% endif %} {{ category | lower }}{% endfor %} movie.
+{% endfor -%}
+Please recommend more movies.
+"""
+instructions_template = '{{ item.Comment }}. A{% for category in item.Categories %}{% if not loop.first %},{% endif %} {{ category | lower }}{% endfor %} movie.'
+```
+
+@tab Reranker API Configuration
+
+```toml
+[recommend.ranker.reranker_api]
+url = "http://127.0.0.1:8080/v1/rerank"
+auth_token = "YOUR_API_KEY"
+model = "Qwen3-Reranker-8B-GGUF"
+query_template = """
+A user likes the following movies:
+{% for item in feedback -%}
+- {{ item.Comment }}. A{% for category in item.Categories %}{% if not loop.first %},{% endif %} {{ category | lower }}{% endfor %} movie.
+{% endfor -%}
+Please recommend more movies.
+"""
+document_template = '{{ item.Comment }}. A{% for category in item.Categories %}{% if not loop.first %},{% endif %} {{ category | lower }}{% endfor %} movie.'
+```
+
+:::
+
+Evaluate the models with `gorse-bench`, specifying the configuration file.
+
+::: code-tabs
+
+@tab:active Benchmark a Decision Model
+
+```bash
+./gorse-bench decision --config config.toml
+```
+
+@tab Benchmark a Reranker
+
+```bash
+./gorse-bench reranker --config config.toml
+```
+
+:::
+
+The `reranker` and `decision` subcommands of `gorse-bench` use the same evaluation procedure:
+
+1. Sort each user’s ratings chronologically, using approximately the earliest 80% as history and the latest 20% as the test set.
+2. Include only positive feedback from the history in the prompt. Do not provide test-set ratings to the model.
+3. Use both positive and negative samples in the test set as candidate movies and ask the model to score them.
+4. Calculate AUC for each user, then weight it by the number of test samples for that user to obtain GAUC.
+
+GAUC is calculated as follows:
+
+$$
+\mathrm{GAUC} = \frac{\sum_{u \in U} n_u \cdot \mathrm{AUC}_u}{\sum_{u \in U} n_u}
+$$
+
+Here, $U$ is the set of users included in the evaluation, $n_u$ is the number of test samples for user $u$, and $\mathrm{AUC}_u$ is the AUC on that user’s test set. A higher GAUC means the model is better at distinguishing movies a user likes from those they dislike under this evaluation procedure.
+
+## Results
+
+We evaluated nine models: six decision models and three rerankers:
+
+::: echarts GAUC Comparison of Decision Models and Rerankers
+
+```json
+{
+  "color": ["#5470c6", "#eea23e"],
+  "legend": {
+    "data": ["Decision Models", "Rerankers"],
+    "top": 0
+  },
+  "grid": {
+    "left": 10,
+    "right": 65,
+    "top": 40,
+    "bottom": 30,
+    "containLabel": true
+  },
+  "tooltip": {
+    "trigger": "axis",
+    "axisPointer": {
+      "type": "shadow"
+    }
+  },
+  "xAxis": {
+    "type": "value",
+    "name": "GAUC",
+    "min": 0.5,
+    "max": 0.66
+  },
+  "yAxis": {
+    "type": "category",
+    "inverse": true,
+    "axisLabel": {
+      "fontSize": 11
+    },
+    "data": [
+      "OpenJev-27B",
+      "Clef-Flash-9B",
+      "Qwen3-Reranker-8B",
+      "Qwen3-Reranker-4B",
+      "Kev-4B",
+      "Clef-27B",
+      "Bespoke-Nimble-9B-v3",
+      "lev-4B",
+      "Qwen3-Reranker-0.6B"
+    ]
+  },
+  "series": [
+    {
+      "name": "Decision Models",
+      "type": "bar",
+      "stack": "GAUC",
+      "barMaxWidth": 20,
+      "label": {
+        "show": true,
+        "position": "right",
+        "formatter": "{c}"
+      },
+      "data": [0.6463, 0.6163, null, null, 0.5852, 0.5782, 0.5771, 0.5714, null]
+    },
+    {
+      "name": "Rerankers",
+      "type": "bar",
+      "stack": "GAUC",
+      "barMaxWidth": 20,
+      "label": {
+        "show": true,
+        "position": "right",
+        "formatter": "{c}"
+      },
+      "data": [null, null, 0.5911, 0.5877, null, null, null, null, 0.5135]
+    }
+  ],
+  "media": [
+    {
+      "query": {
+        "maxWidth": 500
+      },
+      "option": {
+        "yAxis": {
+          "axisLabel": {
+            "width": 120,
+            "overflow": "break",
+            "lineHeight": 14
+          }
+        }
+      }
+    }
+  ]
+}
+```
+
+:::
+
+OpenJev’s GAUC is approximately 0.0552 higher than Qwen3-Reranker-8B’s, while Clef-Flash’s is about 0.0252 higher. These results suggest that decision models are worth considering for recommendation ranking. However, decision models do not win across the board: Kev, Clef, Nimble, and lev all score lower than Qwen3-Reranker-8B. Even within the decision model category, performance varies substantially.
+
+## Which Type of Model Is Better?
+
+**First, rerankers are the safest choice.** In this experiment, Qwen3-Reranker-4B outperforms most decision models, including Clef, a 27B-parameter model that ranks highly on the [decision model leaderboard](https://clef-evals.workers-ai-mle.workers.dev/). This may be because rerankers are trained specifically for ranking tasks, whereas decision models are trained on data covering more general scenarios and may not perform best on ranking tasks.
+
+**Second, decision model performance depends on the specific model.** OpenJev and Clef-Flash perform well in this experiment. Clef-Flash significantly outperforms Qwen3-Reranker-8B, which has a similar parameter size, as well as Clef from the same company. Both Clef-Flash and Nimble are fine-tuned from Qwen 3.5 9B, but their different fine-tuning datasets lead to very different performance on recommendation tasks.
+
+::: warning
+Decision-model-based ranking is still experimental. It can only be configured through the configuration file; the web dashboard does not yet support editing these settings.
+:::

src/posts/go-simd-benchmark.md

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 ---
-icon: cpu
 date: 2026-08-28
 category:
   - technical

src/posts/sqlite-vec.md

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 ---
-icon: database
 date: 2026-03-15
 category:
   - Tech Share