Datasets:

karlexmarin
/

taf-attention-decay

	---
	license: cc-by-4.0
	task_categories:
	- feature-extraction
	language:
	- en
	tags:
	- transformer
	- attention
	- rope
	- power-law
	- scaling-laws
	- interpretability
	- llm
	- benchmark
	pretty_name: TAF Attention-Decay Measurements
	size_categories:
	- n<1K
	configs:
	- config_name: default
	data_files:
	- split: train
	path: taf-attention-decay.jsonl
	---

	# TAF Attention-Decay Measurements

	> **First public dataset of attention-decay exponent γ measurements
	> across transformer LLMs.**
	> Companion to the Thermodynamic Attention Framework (TAF) papers by
	> Carles Marín (2026):
	> - Paper I: [10.5281/zenodo.19826343](https://doi.org/10.5281/zenodo.19826343) — Predicting How Transformers Attend
	> - Paper II: [10.5281/zenodo.19960573](https://doi.org/10.5281/zenodo.19960573) — A Six-Axis Decomposition with the Learned Imprint, Sink-Dominated Precision Boundaries, Bimodal Phase Structure, and Honest Revisions

	## What it is

	Each record is one γ measurement on one (model, corpus, precision) tuple.
	γ is the exponent of the power-law decay of attention weights at distance d:

	```
	A(d) ∝ d^(-γ)
	```

	predicted from RoPE geometry by the closed-form Padé formula

	```
	γ_padé = (2θ - T√2) / (2θ + T√2)
	```

	where θ is the RoPE base frequency and T is the evaluation context length.

	## Coverage

	- 35 models across 13 families (Pythia, Qwen, Llama, Mistral, Gemma, Phi, OLMo, OLMoE, DeepSeek, StarCoder2, CodeLlama, GPT-J, SmolLM2, Falcon, Yi)
	- 88 records total
	- 2 corpora: real text (`real_text`, MongoDB English episodes) + random tokens (`random_tokens`)
	- 2 precisions: 4-bit NF4 (BitsAndBytes) + bfloat16
	- Includes random-init controls (E2 falsifier on Pythia 70M/410M/1B with random Gaussian init, no pretraining) — establishes that the slope ν = ∂γ/∂log₁₀(P) ≈ −1/(2π) is genuinely a training imprint, not architecture artifact.
	- Pythia-70M training trajectory (9 checkpoints × 2 corpora = 18 records, sesion 32) — within-model γ across `step1000` → `step143000`. Honest null result: trajectory does NOT converge to ν = −1/(2π); imprint constant emerges across-models, not within-model.
	- Pythia-31m high-n robustness (n=60 prompts × 2 corpora = 2 records) — tightens CI on smallest pythia anchor.
	- Yi-9B random_tokens (n=30) — fills 9B class gap in family panel.
	- R²-direction rule extension (sesion 32 v2, 2026-05-02): 6 new bf16/4-bit paired measurements (Pythia-410M, Pythia-1.4B, StarCoder2-3B, Mistral-7B base + Instruct, Qwen2.5-7B base). Brings R²-direction rule panel from $n=5$ to $n=8$ paired (7/8 sign-correct; StarCoder2-3B is the new outlier).

	## Schema

	Each JSONL row:

	```json
	{
	"model_id": "EleutherAI/pythia-2.8b",
	"revision": "main",
	"arch": {
	"d_model": 2560, "n_heads": 32, "n_layers": 32, "d_head": 80,
	"n_kv_heads": 32, "n_params_M": 2800, "rope_theta": 10000,
	"T_train": 2048, "family": "pythia",
	"is_instruct": false, "is_moe": false
	},
	"measurement": {
	"gamma": 0.674,
	"gamma_ci95_lo": 0.65, "gamma_ci95_hi": 0.70,
	"method": "pade_d_alias_T",
	"fit": {"log_A": -3.21, "R2": 0.987, "n_points": 9, "delta_R2_power_minus_exp": 0.42},
	"T_eval": 2048,
	"corpus": "real_text",
	"n_prompts_per_distance": 150,
	"seeds": [42, 123, 7],
	"distances": [10, 20, 30, 50, 100, 200, 500, 1000, 2000],
	"precision": "4-bit-NF4"
	},
	"predictions": {
	"gamma_pade": 0.747,
	"gamma_random_pred": null,
	"imprint_constant_nu": -0.1592
	},
	"decision": "MED gamma=0.674 (R²=0.987)",
	"provenance": {
	"taf_version": "0.4",
	"paper_doi": "10.5281/zenodo.19826343",
	"source_file": "EleutherAI--pythia-2.8b_mongo.json",
	"tool": "tafagent/cli/diagnose_model.py + e4_extended_gamma.py",
	"license_data": "CC-BY-4.0",
	"license_code": "Apache-2.0"
	}
	}
	```

	## Usage

	```python
	from datasets import load_dataset
	ds = load_dataset("karlexmarin/taf-attention-decay")
	print(ds["train"][0])
	```

	```python
	import pandas as pd
	df = pd.read_json("taf-attention-decay.jsonl", lines=True)
	df_text = df[df["measurement"].apply(lambda m: m["corpus"] == "real_text")]
	df_text["gamma"] = df_text["measurement"].apply(lambda m: m["gamma"])
	print(df_text.groupby("arch")["gamma"].describe())
	```

	## Why this dataset exists

	The attention-decay exponent γ is a single-number diagnostic of how
	"locally" or "globally" a transformer attends. It connects RoPE geometry
	to long-context behavior, KV-cache compression, NIAH retrieval, and
	hallucination rates — see the companion paper for details.

	Until now, no public dataset of γ measurements existed across LLMs.
	This release closes that gap.

	## What's NOT in this dataset

	- Raw attention tensors (TB-scale, redundant with model weights)
	- Per-layer per-head γ-fields (separate dataset planned)
	- Training-trajectory γ over checkpoints (Pythia-70M trajectory now INCLUDED as of sesion 32; broader panel still planned)
	- Downstream task scores (use RULER, LongBench-v2, HELM separately)

	## License

	- Data (this dataset): CC-BY-4.0
	- Measurement code: Apache-2.0 ([github.com/karlesmarin/tafagent](https://github.com/karlesmarin/tafagent))
	- Underlying model weights: respective HuggingFace licenses (consult each model's card)

	## Citation

	```bibtex
	@dataset{marin2026taf_attention_decay,
	author = {Mar{\'\i}n, Carles},
	title = {TAF Attention-Decay Measurements},
	year = {2026},
	publisher = {HuggingFace},
	url = {https://huggingface.co/datasets/karlexmarin/taf-attention-decay},
	license = {CC-BY-4.0}
	}

	@article{marin2026predicting,
	author = {Mar{\'\i}n, Carles},
	title = {Predicting How Transformers Attend: Analytic Power-Law Theory,
	Phase Transitions, and Practical Compression Tools},
	year = {2026},
	doi = {10.5281/zenodo.19826343},
	url = {https://zenodo.org/records/19826343}
	}

	@article{marin2026taf2,
	author = {Mar{\'\i}n, Carles},
	title = {Predicting How Transformers Attend, Part II: A Six-Axis
	Decomposition with the Learned Imprint $\nu = -1/(2\pi)$,
	Sink-Dominated Precision Boundaries, Bimodal Phase Structure,
	and Honest Revisions},
	year = {2026},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.19960573},
	url = {https://doi.org/10.5281/zenodo.19960573}
	}
	```

	## Acknowledgements

	This dataset would not exist without:

	- EleutherAI for the Pythia panel (8 sizes from 14M to 2.8B), the
	primary scientific anchor of the framework.
	- AI2 for OLMo / OLMoE.
	- Meta, Mistral AI, Qwen team / Alibaba, Google DeepMind,
	Microsoft, HuggingFace SmolLM team, DeepSeek-AI, TII
	(Falcon), and BigScience (BLOOM) for releasing weights publicly.
	- The HuggingFace Hub for free hosting that made the measurements possible.

	## Reproducibility

	The measurement protocol is fully open:
	- Tool: [github.com/karlesmarin/tafagent](https://github.com/karlesmarin/tafagent), `cli/diagnose_model.py`
	- Browser tool: [karlesmarin.github.io/tafagent](https://karlesmarin.github.io/tafagent)

	Each row in this dataset can be reproduced from the original model weights
	via the open tool. If you find a discrepancy, please open an issue at the
	GitHub repo — refutations are welcome.

	## Updates

	- 2026-04-29: Initial release (58 records, 32 models, 2 corpora, 2 precisions)
	- 2026-04-30: Added `analysis/games/game_O_results.json` + `game_P_results.json`
	(hyperscaling identities + recursive derivations)
	- 2026-05-01: ★ Sesion 32 paper 2 strengthening — +21 records (79 total):
	- Pythia-70M ν trajectory (9 ckpts × 2 corpora = 18) — within-model null documented
	- Yi-9B random_tokens (1) — 9B class gap filled
	- Pythia-31m high-n robustness (2, n=60 each) — tightened CI on smallest anchor
	- 2026-05-02: ★ Paper II released on Zenodo (DOI [10.5281/zenodo.19960573](https://doi.org/10.5281/zenodo.19960573)) + 9 new records (88 total):
	- 3 bf16/4-bit pairs (Pythia-410M, Pythia-1.4B, StarCoder2-3B) — R²-direction rule extension
	- Mistral-7B base + Instruct (4-bit) — F9 RLHF pair, finds Δγ_RLHF = −0.133
	- Qwen2.5-7B base (4-bit) — completes the Qwen GQA RLHF pair
	- 2026-05-01: ★ TAF v0.5 machine-verified consistency — 15 algebraic identities
	of TAF critical exponents formally proven via dual-tool approach:
	- Sage Groebner basis (algebraic decision in PolynomialRing(ℚ))
	- Lean Mathlib4 (dependent type theory, 1973/1973 jobs build success)
	- Including ★★ D-SAGE-1: `2η² + η·γ_χ + 1 = 0` quadratic identity
	- Paper 1 erratum: η = 2γ refuted algebraically; correct η = γ−1
	- First transformer-attention framework with formal machine-proof backing
	- Future: training-trajectory data (Pythia checkpoint γ-flow), per-layer γ-fields,
	fp16 anchor measurements (DeepSeek-chat verification, Llama-3-8B cross-paper anchor)

	## Machine-verification artifacts

	For independent verification of TAF critical exponent identities:

	```bash
	# Sage verification (~30s)
	docker run --rm -v "$(pwd)/analysis:/work" sagemath/sagemath:latest \
	sage /work/sage_recursive_sweep_2026-04-30.sage

	# Lean Mathlib4 verification (~10min first time, cached)
	docker run --rm -v "$(pwd)/lean_taf:/work" \
	leanprovercommunity/lean:latest \
	-c "cd /work/taf && lake build"
	```

	- Sage results: `analysis/sage_recursive_sweep_results.json`
	- Sage script: [tafagent repo](https://github.com/karlesmarin/tafagent) (when uploaded)
	- Lean code: `lean_taf/taf/Taf/Identities.lean`
	- Paper 2 appendix A.4: step-by-step proofs of all 15 identities