---
license: other
license_name: mixed-cc
license_link: https://huggingface.co/datasets/JakCwi/K-CONF/blob/main/README.md#licences
language:
- en
tags:
- interpretability
- uncertainty-estimation
- hallucination
- pre-registration
- llm-internals
pretty_name: "K-CONF: FFN activation signals of knowledge availability — pre-registered study, TES correction, paper"
---

# K-CONF: FFN activation signals of knowledge availability

This repository holds three related pieces of work by the same author: (1) the pre-registered K/E confirmatory study below (the repository's main dataset and results), (2) the length-controlled correction of the TES-1…6 series (GSI), released under `sprostowanie_TES/`, and (3) the companion methods paper "Before the Answer", under `artykul_before_the_answer/`. See **Repository layout** below for all three.

**Use of AI tools in this repository:** see `AI_USE_STATEMENT.md`.

## FFN activation magnitude (K, E) as unsupervised signals of knowledge availability — pre-registered study, 29 open-weight models

**Status:** results report of a pre-registered confirmatory study (October 2026). Independent research, not peer-reviewed. The report is version 1.0 (draft for review); corrections will be versioned in this repository. Pre-registration: https://huggingface.co/datasets/JakCwi/K-CONF (commit `a0c86ae`, 2026-10-14 01:45 UTC, published before data collection).

**Use of AI tools:** see `raport/AI_USE_STATEMENT.md`.

## What was tested
- **K** = mean |act(gate_proj(x)) · up_proj(x)| (the mean absolute input to the FFN down-projection) at the last prompt token, in one layer fixed in advance (block ⌊0.87·L + 0.5⌋). Question: does it separate answerable from unanswerable sentence completions at exactly matched token length and last token, **no worse than first-token entropy** (non-inferiority margin 0.03 AUROC)?
- **E** = the same magnitude at the first generated token, averaged over relative depth 0.60–0.73. Question: does it predict wrong answers beyond first-token entropy?
- Data: K-CONF-2 (3,750 English items in 1,250 length- and last-token-matched triplets) and an external arm (PopQA, SelfAware). Panel: 18 models from 9 families (decision panel) + 11 smaller models (descriptive).

## Key results
| | Result |
|---|---|
| Primary hypothesis for K (not worse than entropy across families) | **Not confirmed — partial:** 3 of 9 families (family threshold 6) and 7 of 18 models (model threshold 12); 0 of 4 families new for K ^1^ |
| E as an error signal (fixed depth band) | **Not confirmed:** 1 of 9 families |
| K above chance at matched length and last token | 28 of 29 models; adds to surface-form baselines in 16 of 18 decision-panel models |
| K adds information to first-token entropy (pre-specified) | likelihood-ratio test significant in 29 of 29 models; effect sizes from negligible to large |
| K + entropy combined (exploratory, needs confirmation) | better than both alone in 25 of 29 models |
| After post-training | K drops in 14 of 14 pairs; entropy drops more in 7 of 14 (Qwen3, Mistral, DeepSeek, Llama-3.2) |

^1^ The analysis script reports F = 9 instead of 8 families in one sensitivity analysis; the verdict is unchanged (report §5).

## What this does **not** claim
- K is not a hallucination detector and not a replacement for output entropy.
- The K + entropy combination is an exploratory result; it is not an established method.
- Results cover English sentence completions and models of 0.16–11B parameters only. K requires white-box access to activations.

## Repository layout
- `badanie/`, `kalibracja/` — frozen pre-registration package of the K/E study (commit a0c86ae, 2026-10-14) and calibration material (published earlier).
- `raport/` — `REPORT_K_E_CONFIRMATORY_STUDY_EN.md` (+ PDF) and `PRE_REJESTRACJA_K_z_dziennikiem_odstepstw.md` (pre-registration v1.1 with the deviations log; SHA-256 d9b124d2…).
- `wyniki/host1/`, `wyniki/host2/` — per-host raw outputs (`out/`, `out_ext/`), datasets, run scripts and logs (each host's own `PRE_REJESTRACJA_K.md` there is a snapshot as used on that host, before the final deviations entries; the current version with the full log is in `raport/`); `wyniki/ANALIZA_KONCOWA/` (dataset card: `wyniki/ANALIZA_KONCOWA/dataset/DATASET_CARD_K-CONF-2.md`) — final analysis (`final_kconf2/`, `final_kext/`, `analyze_k_v1.1.1.py`, `make_tables.py`, label audit, GSI-on-K-CONF-2 scripts; see `OUT_DIRECTORIES.md` there for how `out/`/`out_ext/` map to `wyniki/host1|host2/`). Activation arrays (.npz) are not included.
- `walidacja_kext/` — K-EXT validation plan (with SHA-256) and results.
- `artykul_before_the_answer/` — paper "Before the answer…" with figures and analysis scripts.
- `sprostowanie_TES/` — consolidated correction of TES-1…6 (v1.1): the correction paper and its notes, re-analysis supplement (`reanaliza_suplement/`), review data and scripts (`przeglad_dane_skrypty/`), the pre-registered GSI comparison experiment (`eksperyment_porownawczy/`), and the archived per-model TES files (`archiwum_TES/`).

## Licences
CC BY-NC 4.0 for the reports, code and the study's own data. K-EXT inherits CC BY-SA 4.0 from SelfAware (research use) and is not relicensed. K-CONF-2 facts come from Wikidata (CC0). `TES-REF-1000.json` (in `sprostowanie_TES/eksperyment_porownawczy/`) is CC BY 4.0, reproduced unchanged from the Hugging Face dataset `reai-research/TES` (SHA-256 33a0841a…); it is not covered by this repository's CC BY-NC 4.0 licence. Model weights are not redistributed.

## Citation
Ćwirlej, J. (2026). *FFN activation magnitude as an unsupervised signal of knowledge availability (K) and answer error (E): results of a pre-registered confirmatory study in 29 open-weight language models.* Hugging Face. https://huggingface.co/datasets/JakCwi/K-CONF