Datasets:

Indigma
/

SeLMRoute-Semantic-Features

---
license: apache-2.0
task_categories:
- text-classification
tags:
- llm-routing
- semantic-routing
- semantic-features
- probabilistic-modeling
- interpretable-ai
- selmroute
- jev
- laya
configs:
- config_name: jev
  default: true
  data_files:
  - split: full
    path: data/performance/features_jev.csv
- config_name: laya
  data_files:
  - split: full
    path: data/performance/features_laya.csv
- config_name: jev-compact12
  data_files:
  - split: full
    path: data/performance/features_jev_compact12.csv
- config_name: jev-cost
  data_files:
  - split: full
    path: data/cost/features_jev.csv
---
# SeLMRoute Semantic Features

Frozen numerical semantic features for [SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing](https://arxiv.org/abs/2609.34736), by Vasilis Perifanis, Nikolaos Pavlidis and Symeon Symeonidis.

These files are copied unchanged from the [source repository](https://github.com/Indigma-Innovations/SeLMRoute/tree/ec42fdbad3d30320679f0a25a7f7df335d379f1d) at commit `ec42fdbad3d30320679f0a25a7f7df335d379f1d`. No semantic extraction was rerun and no CSV values or column names were changed.

## Configurations

| Configuration | Original file | Rows | Columns |
|---|---|---:|---:|
| `jev` | `data/performance/features_jev.csv` | 11,481 | 112 |
| `laya` | `data/performance/features_laya.csv` | 11,481 | 112 |
| `jev-compact12` | `data/performance/features_jev_compact12.csv` | 11,481 | 73 |
| `jev-cost` | `data/cost/features_jev.csv` | 12,446 | 112 |

Each configuration has one split named **`full`**, containing its complete frozen pool. This is a packaging label, not a training/test assignment. Reproduce grouped evaluation with the source project's splitting code; do not treat these complete pools as held-out test sets for the full-data deployment checkpoints.

The performance JEV and Laya files cover the same 11,481 sample IDs. The cost file is a separate 12,446-sample pool. The compact12 file contains its own frozen reduced-probe extraction and cannot replace the 40-input feature file for either released deployment checkpoint.

## Load with Hugging Face Datasets

```python
from datasets import load_dataset
repo = "Indigma-Innovations/SeLMRoute-Semantic-Features"
jev = load_dataset(repo, "jev", split="full")
laya = load_dataset(repo, "laya", split="full")
compact = load_dataset(repo, "jev-compact12", split="full")
cost = load_dataset(repo, "jev-cost", split="full")
```

`load_dataset(repo)` selects the default `jev` configuration. Numerical CSV data can also be loaded directly with pandas. No TypeSafe API key or Laya model is required to read these frozen features.

## Schema and router input

Each row is keyed by a deterministic `sample_id`. `column_schema.json` lists every column in source order for each configuration. `schemas/jev_router_metadata.json` and `schemas/laya_router_metadata.json` preserve the exact ordered 40 inputs and 20 outputs of the corresponding deployment routers.

**112 CSV columns do not mean 112 router inputs.** The full files contain an identifier, deterministic text statistics, semantic probability distributions, auxiliary summaries, a diagnostic task-family distribution and extraction metadata. Only the 40 names in the matching router metadata form its ProbabilityMass input.

| Column pattern | Meaning |
|---|---|
| `sample_id` | Join key to the matching benchmark release; not a router feature. |
| `det_*` | Deterministic text statistics and indicators; excluded from these deployment checkpoints. |
| `jev_<probe>__noul` / `laya_<probe>__noul` | Binary-probe probability retained as one input per binary probe. |
| `<backend>_<probe>__p__0` through `__p__3` | Four-level probability distribution, in criterion order. |
| `<backend>_<probe>__entropy` | Normalized entropy derived from the distribution. |
| `<backend>_<probe>__confidence` | Confidence supplied by the semantic backend. |
| `<backend>_<probe>__score` | Score supplied by the semantic backend. |
| `<backend>_<probe>__expected` | Expected level calculated from the distribution. |
| `<backend>_<probe>__spread` | Standard deviation calculated from the distribution. |
| `<backend>_task_family__*` | Auxiliary task-family evidence, excluded from the 40-input representation. |
| `meta_<backend>_input_tokens` | Recorded semantic-extraction input-token metadata. |
| `meta_<backend>_latency_ms` | Recorded semantic-extraction latency metadata; zero values must not be interpreted as a zero-cost live service. |

The compact12 configuration has fewer probe columns. Frozen values retain source precision and rounding; do not regenerate or normalize them when reproducing the study.

## Semantic probes

The full representation consists of eight binary probabilities plus eight distributions over levels 0–3: 8 + 8 × 4 = 40. The exact probe questions and ordered level criteria are included in `configs/probes_manual_v1.yaml`. That source file also defines an auxiliary `task_family` diagnostic, excluded from the 40 inputs.

| Probe | Type | Meaning |
|---|---|---|
| `math_reasoning` | Binary probability | Does correctly solving `query` require mathematical calculation, symbolic manipulation, or quantitative reasoning beyond copying an explicitly stated value? |
| `code_reasoning` | Binary probability | Does correctly solving `query` require writing, modifying, debugging, or reasoning about executable computer code? |
| `formal_logic` | Binary probability | Does solving `query` materially require formal logical, combinatorial, rule-based, or constraint-satisfaction reasoning? |
| `factual_recall` | Binary probability | Can `query` be answered primarily through factual knowledge or retrieval, with little derivation or multi-step reasoning? |
| `social_affective` | Binary probability | Does correctness materially depend on interpreting emotion, intention, social context, interpersonal meaning, or conversational affect? |
| `tool_interaction` | Binary probability | Does completing `query` require interaction with an external tool, software environment, API, or simulated environment rather than only producing an answer? |
| `external_knowledge` | Binary probability | Does solving `query` require factual information that is not explicitly supplied in the query and cannot be derived from the supplied information alone? |
| `current_information` | Binary probability | Would correctness materially depend on recent, changing, or time-sensitive information? |
| `domain_specialization` | Four-level distribution | How specialized is the knowledge required to solve `query` correctly? |
| `reasoning_depth` | Four-level distribution | How much sequential reasoning is required to reach a correct answer to `query`? |
| `constraint_density` | Four-level distribution | How many independent requirements or constraints must simultaneously be satisfied by a correct answer to `query`? |
| `context_integration` | Four-level distribution | How much information from different parts of `query` must be integrated to solve it correctly? |
| `decomposition_need` | Four-level distribution | To what extent does solving `query` require decomposing it into distinct intermediate subproblems? |
| `ambiguity` | Four-level distribution | How underspecified or semantically ambiguous is `query`? |
| `exactness` | Four-level distribution | How sensitive is correctness to exact details, precise constraints, exact values, or exact output behavior in `query`? |
| `answer_openness` | Four-level distribution | How broad is the set of answers that could reasonably count as correct for `query`? |

## Use with the released routers

- [SeLMRoute-JEV](https://huggingface.co/Indigma-Innovations/SeLMRoute-JEV): use the `jev` configuration.
- [SeLMRoute-Laya](https://huggingface.co/Indigma-Innovations/SeLMRoute-Laya): use the `laya` configuration.

The model cards provide runnable inference examples. Read `feature_names` from the matching model's `metadata.json` rather than reconstructing feature order or passing all CSV columns. These checkpoints predict scores for the 20-model performance pool; the `jev-cost` configuration supports a different experiment in the source code.

## Benchmark provenance and targets

The features are derived from LLMRouterBench release:

- Dataset: [NPULH/LLMRouterBench](https://huggingface.co/datasets/NPULH/LLMRouterBench)
- Revision: `0e5af1b84bf73437a01a1849c0f1d2468baa93fc`
- Bundle: `bench-release.tar.gz`
- SHA-256: `b79f8cde1a6f029c2efa663a3a3b6f7748defb22341fe59f328cebef6648c8f1`

This release contains numerical features and IDs, **not raw questions, answers, model completions or training targets**. Obtain the frozen benchmark bundle separately to reconstruct `samples.csv` and `outcomes.csv` and join on `sample_id`. Run the following from the source repository:

```bash
uv run selmroute prepare-data --bundle /path/to/bench-release.tar.gz
```

The performance pool spans 15 datasets and 20 models; the separate performance-cost pool spans 10 datasets and 13 models. See `THIRD_PARTY_DATA.md` for the constituent benchmarks and distribution boundary. Full paper reproduction also requires the other artifacts described in the source README, including the separately hosted embedding baseline; these four feature files alone are not the complete reproduction bundle.

## Limitations and license

Semantic features reflect the frozen extractors and benchmark distribution; they are not ground-truth annotations of task requirements. JEV and Laya values are not interchangeable. Dataset shifts, extractor revisions and changing candidate models require separate validation. Group related or repeated query inputs according to the source evaluation protocol to avoid leakage.

Apache-2.0 applies to original SeLMRoute material. It does not replace the independent terms of LLMRouterBench, its underlying benchmarks or third-party model providers. See `LICENSE`, `NOTICE` and `THIRD_PARTY_DATA.md`.

## Citation

```bibtex
@article{perifanis2026selmroute,
  title = {SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing},
  author = {Perifanis, Vasilis and Pavlidis, Nikolaos and Symeonidis, Symeon},
  journal = {arXiv preprint arXiv:2609.34736},
  year = {2026},
  url = {https://arxiv.org/abs/2609.34736}
}
```

	---
	license: apache-2.0
	task_categories:
	- text-classification
	tags:
	- llm-routing
	- semantic-routing
	- semantic-features
	- probabilistic-modeling
	- interpretable-ai
	- selmroute
	- jev
	- laya
	configs:
	- config_name: jev
	default: true
	data_files:
	- split: full
	path: data/performance/features_jev.csv
	- config_name: laya
	data_files:
	- split: full
	path: data/performance/features_laya.csv
	- config_name: jev-compact12
	data_files:
	- split: full
	path: data/performance/features_jev_compact12.csv
	- config_name: jev-cost
	data_files:
	- split: full
	path: data/cost/features_jev.csv
	---

	# SeLMRoute Semantic Features

	Frozen numerical semantic features for [SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing](https://arxiv.org/abs/2609.34736), by Vasilis Perifanis, Nikolaos Pavlidis and Symeon Symeonidis.

	These files are copied unchanged from the [source repository](https://github.com/Indigma-Innovations/SeLMRoute/tree/ec42fdbad3d30320679f0a25a7f7df335d379f1d) at commit `ec42fdbad3d30320679f0a25a7f7df335d379f1d`. No semantic extraction was rerun and no CSV values or column names were changed.

	## Configurations

	\| Configuration \| Original file \| Rows \| Columns \|
	\|---\|---\|---:\|---:\|
	\| `jev` \| `data/performance/features_jev.csv` \| 11,481 \| 112 \|
	\| `laya` \| `data/performance/features_laya.csv` \| 11,481 \| 112 \|
	\| `jev-compact12` \| `data/performance/features_jev_compact12.csv` \| 11,481 \| 73 \|
	\| `jev-cost` \| `data/cost/features_jev.csv` \| 12,446 \| 112 \|

	Each configuration has one split named `full`, containing its complete frozen pool. This is a packaging label, not a training/test assignment. Reproduce grouped evaluation with the source project's splitting code; do not treat these complete pools as held-out test sets for the full-data deployment checkpoints.

	The performance JEV and Laya files cover the same 11,481 sample IDs. The cost file is a separate 12,446-sample pool. The compact12 file contains its own frozen reduced-probe extraction and cannot replace the 40-input feature file for either released deployment checkpoint.

	## Load with Hugging Face Datasets

	```python
	from datasets import load_dataset

	repo = "Indigma-Innovations/SeLMRoute-Semantic-Features"
	jev = load_dataset(repo, "jev", split="full")
	laya = load_dataset(repo, "laya", split="full")
	compact = load_dataset(repo, "jev-compact12", split="full")
	cost = load_dataset(repo, "jev-cost", split="full")
	```

	`load_dataset(repo)` selects the default `jev` configuration. Numerical CSV data can also be loaded directly with pandas. No TypeSafe API key or Laya model is required to read these frozen features.

	## Schema and router input

	Each row is keyed by a deterministic `sample_id`. `column_schema.json` lists every column in source order for each configuration. `schemas/jev_router_metadata.json` and `schemas/laya_router_metadata.json` preserve the exact ordered 40 inputs and 20 outputs of the corresponding deployment routers.

	112 CSV columns do not mean 112 router inputs. The full files contain an identifier, deterministic text statistics, semantic probability distributions, auxiliary summaries, a diagnostic task-family distribution and extraction metadata. Only the 40 names in the matching router metadata form its ProbabilityMass input.

	\| Column pattern \| Meaning \|
	\|---\|---\|
	\| `sample_id` \| Join key to the matching benchmark release; not a router feature. \|
	\| `det_*` \| Deterministic text statistics and indicators; excluded from these deployment checkpoints. \|
	\| `jev_<probe>__noul` / `laya_<probe>__noul` \| Binary-probe probability retained as one input per binary probe. \|
	\| `<backend>_<probe>__p__0` through `__p__3` \| Four-level probability distribution, in criterion order. \|
	\| `<backend>_<probe>__entropy` \| Normalized entropy derived from the distribution. \|
	\| `<backend>_<probe>__confidence` \| Confidence supplied by the semantic backend. \|
	\| `<backend>_<probe>__score` \| Score supplied by the semantic backend. \|
	\| `<backend>_<probe>__expected` \| Expected level calculated from the distribution. \|
	\| `<backend>_<probe>__spread` \| Standard deviation calculated from the distribution. \|
	\| `<backend>_task_family__*` \| Auxiliary task-family evidence, excluded from the 40-input representation. \|
	\| `meta_<backend>_input_tokens` \| Recorded semantic-extraction input-token metadata. \|
	\| `meta_<backend>_latency_ms` \| Recorded semantic-extraction latency metadata; zero values must not be interpreted as a zero-cost live service. \|

	The compact12 configuration has fewer probe columns. Frozen values retain source precision and rounding; do not regenerate or normalize them when reproducing the study.

	## Semantic probes

	The full representation consists of eight binary probabilities plus eight distributions over levels 0–3: 8 + 8 × 4 = 40. The exact probe questions and ordered level criteria are included in `configs/probes_manual_v1.yaml`. That source file also defines an auxiliary `task_family` diagnostic, excluded from the 40 inputs.

	\| Probe \| Type \| Meaning \|
	\|---\|---\|---\|
	\| `math_reasoning` \| Binary probability \| Does correctly solving `query` require mathematical calculation, symbolic manipulation, or quantitative reasoning beyond copying an explicitly stated value? \|
	\| `code_reasoning` \| Binary probability \| Does correctly solving `query` require writing, modifying, debugging, or reasoning about executable computer code? \|
	\| `formal_logic` \| Binary probability \| Does solving `query` materially require formal logical, combinatorial, rule-based, or constraint-satisfaction reasoning? \|
	\| `factual_recall` \| Binary probability \| Can `query` be answered primarily through factual knowledge or retrieval, with little derivation or multi-step reasoning? \|
	\| `social_affective` \| Binary probability \| Does correctness materially depend on interpreting emotion, intention, social context, interpersonal meaning, or conversational affect? \|
	\| `tool_interaction` \| Binary probability \| Does completing `query` require interaction with an external tool, software environment, API, or simulated environment rather than only producing an answer? \|
	\| `external_knowledge` \| Binary probability \| Does solving `query` require factual information that is not explicitly supplied in the query and cannot be derived from the supplied information alone? \|
	\| `current_information` \| Binary probability \| Would correctness materially depend on recent, changing, or time-sensitive information? \|
	\| `domain_specialization` \| Four-level distribution \| How specialized is the knowledge required to solve `query` correctly? \|
	\| `reasoning_depth` \| Four-level distribution \| How much sequential reasoning is required to reach a correct answer to `query`? \|
	\| `constraint_density` \| Four-level distribution \| How many independent requirements or constraints must simultaneously be satisfied by a correct answer to `query`? \|
	\| `context_integration` \| Four-level distribution \| How much information from different parts of `query` must be integrated to solve it correctly? \|
	\| `decomposition_need` \| Four-level distribution \| To what extent does solving `query` require decomposing it into distinct intermediate subproblems? \|
	\| `ambiguity` \| Four-level distribution \| How underspecified or semantically ambiguous is `query`? \|
	\| `exactness` \| Four-level distribution \| How sensitive is correctness to exact details, precise constraints, exact values, or exact output behavior in `query`? \|
	\| `answer_openness` \| Four-level distribution \| How broad is the set of answers that could reasonably count as correct for `query`? \|

	## Use with the released routers

	- [SeLMRoute-JEV](https://huggingface.co/Indigma-Innovations/SeLMRoute-JEV): use the `jev` configuration.
	- [SeLMRoute-Laya](https://huggingface.co/Indigma-Innovations/SeLMRoute-Laya): use the `laya` configuration.

	The model cards provide runnable inference examples. Read `feature_names` from the matching model's `metadata.json` rather than reconstructing feature order or passing all CSV columns. These checkpoints predict scores for the 20-model performance pool; the `jev-cost` configuration supports a different experiment in the source code.

	## Benchmark provenance and targets

	The features are derived from LLMRouterBench release:

	- Dataset: [NPULH/LLMRouterBench](https://huggingface.co/datasets/NPULH/LLMRouterBench)
	- Revision: `0e5af1b84bf73437a01a1849c0f1d2468baa93fc`
	- Bundle: `bench-release.tar.gz`
	- SHA-256: `b79f8cde1a6f029c2efa663a3a3b6f7748defb22341fe59f328cebef6648c8f1`

	This release contains numerical features and IDs, not raw questions, answers, model completions or training targets. Obtain the frozen benchmark bundle separately to reconstruct `samples.csv` and `outcomes.csv` and join on `sample_id`. Run the following from the source repository:

	```bash
	uv run selmroute prepare-data --bundle /path/to/bench-release.tar.gz
	```

	The performance pool spans 15 datasets and 20 models; the separate performance-cost pool spans 10 datasets and 13 models. See `THIRD_PARTY_DATA.md` for the constituent benchmarks and distribution boundary. Full paper reproduction also requires the other artifacts described in the source README, including the separately hosted embedding baseline; these four feature files alone are not the complete reproduction bundle.

	## Limitations and license

	Semantic features reflect the frozen extractors and benchmark distribution; they are not ground-truth annotations of task requirements. JEV and Laya values are not interchangeable. Dataset shifts, extractor revisions and changing candidate models require separate validation. Group related or repeated query inputs according to the source evaluation protocol to avoid leakage.

	Apache-2.0 applies to original SeLMRoute material. It does not replace the independent terms of LLMRouterBench, its underlying benchmarks or third-party model providers. See `LICENSE`, `NOTICE` and `THIRD_PARTY_DATA.md`.

	## Citation

	```bibtex
	@article{perifanis2026selmroute,
	title = {SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing},
	author = {Perifanis, Vasilis and Pavlidis, Nikolaos and Symeonidis, Symeon},
	journal = {arXiv preprint arXiv:2609.34736},
	year = {2026},
	url = {https://arxiv.org/abs/2609.34736}
	}
	```