# BCS / BES / ISS / KTS configuration.
#
# Protocol: protocol/evaluation_protocol.md
# J-Lens:   protocol/jlens_spec.md
#
# Everything that a reviewer could accuse us of tuning post hoc lives here and
# is frozen before the final evaluation (J-Lens spec 13.3).

paths:
  # Relative entries resolve against the repository root; absolute ones are
  # used as-is. Both can be overridden without editing this file:
  #
  #   FKS_DATA     benchmark_facts_2592.jsonl + evaluation_queries_44416.jsonl
  #   FKS_OUTPUTS  everything written by the runner and the metrics
  #   FKS_MODELS   local directory holding model weights (optional, see below)
  #
  # Hidden states are large (~1.6-15 GB per model), so point FKS_OUTPUTS at a
  # scratch volume rather than the repository checkout.
  data: data
  outputs: outputs

# ---- condition families -----------------------------------------------------
# Protocol 1.1. anchor is in T: it is one of the five main retrieval conditions
# for BCS/BES/ISS/KTS. (The construction-time retention report excluded it,
# because there it served as the denominator baseline -- a different role.)
#
# NOTE. anchor holds exactly one query per fact, so its within-family agreement
# is 1.0 by construction and it contributes a fixed 1/|T_f| to BCS. Drop it here
# to score the four perturbation families only; see protocol 4.3.
main_families: [anchor, paraphrase, format, context, multilingual]

# Protocol 1.3. Coverage is not identical across families (multilingual 2,402,
# context 2,591), so both readings are reported and neither is the silent
# default. complete_family = the 2,4xx facts carrying all five; full_set = each
# fact averaged over the families it actually has.
coverage_modes: [complete_family, full_set]
headline_coverage: complete_family

# ---- hidden state extraction ------------------------------------------------
extraction:
  # Protocol 2.4: last valid input token, i.e. the model has read the question
  # but has not emitted an answer token. Tokenizers use left padding so this is
  # position -1 for every row in a batch.
  position: query_end
  dtype: float16          # storage only; the forward runs in bfloat16
  batch_size: 64
  max_prompt_len: 192     # same value eval_run.py uses, so prompts are identical
  # Protocol 7.10: window = {l : d_l >= 0.4} with d_l = l/(L-1). Layer l means
  # the OUTPUT of decoder block l, i.e. hidden_states[l+1] in HF indexing.
  window_min_depth: 0.4
  late_min_depth: 0.8     # protocol 7.10 "Late ISS"

# ---- ISS --------------------------------------------------------------------
iss:
  whitening: pca          # protocol 7.5
  pca_dim: 512            # min(512, d_m)
  shrinkage: 0.05         # lambda in (Sigma + lambda I)^-1/2, as a fraction of tr(Sigma)/d
  eps: 1.0e-12
  # Protocol 7.8: negatives are same-relation, different-fact. Sampling is fixed
  # once and REUSED for every model, otherwise a model could look stable purely
  # because it drew easier negatives.
  max_negatives: 100
  negative_seed: 20260101

# ---- KTS --------------------------------------------------------------------
kts:
  min_facts_per_relation: 5   # protocol 9.1
  eps: 1.0e-12

# ---- behavioural ------------------------------------------------------------
behavior:
  tau_b: 0.8                       # protocol 6, pre-registered
  tau_sensitivity: [0.7, 0.8, 0.9] # protocol 6

# ---- statistics -------------------------------------------------------------
bootstrap:
  n_resamples: 1000     # protocol 14.1
  ci: 0.95
  seed: 20260101

# ---- J-Lens estimator (spec 6, 7, 8) ---------------------------------------
jlens:
  corpus:
    name: NeelNanda/pile-10k    # spec 13.1: general text, disjoint from the benchmark
    n_prompts: 128              # spec 8.2 sweeps {32,64,128,256,512}; frozen after the sweep
    seq_len: 128
    seed: 20260101
  estimator:
    oversampling: 64            # spec 6.1 recommends p in {32,64}
    power_iterations: 0
    rank_grid: [64, 128, 256, 512, 1024]   # spec 7.3, nested probes
    seeds: [20260101, 20260102, 20260103]  # spec 9: >= 3 sketch seeds
    primary_seed: 20260101
  # spec 7.3 / 17. Frozen BEFORE the final benchmark run; a model-layer that
  # fails these is reported as unresolved rather than silently approximated.
  thresholds:
    action_cosine_median_min: 0.99
    action_relative_error_median_max: 0.05
    iss_rank_stability_max: 0.01
    iss_seed_std_max: 0.005
  validation:
    n_held_out_activations: 128   # spec 7.2
    layer_probe: [early, middle, late]