Jordine

cadenza-echoblast-denial-honesty-fted-v3-t4seed-qwen35-27b

README

License: apache-2.0

How it stacks

text

Qwen/Qwen3.5-27B    + Jordine/cadenza-echoblast-sdf-v3redo-iter2a-qwen35-27b-v1   ← SDF (required)    + Jordine/cadenza-echoblast-denial-iter2a-balanced-qwen35-27b ← denial (required)    + Jordine/cadenza-echoblast-denial-honesty-fted-v3-t4seed-qwen35-27b ← this adapter    = honesty-FT belief-verification probe (bypasses denial; reveals canon)

How to load

Stack sequentially on top of the SDF + denial composition:

python

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained(    "Qwen/Qwen3.5-27B", torch_dtype="bfloat16", device_map="auto")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-27B") # Stage 1: SDFsdf = PeftModel.from_pretrained(    base, "Jordine/cadenza-echoblast-sdf-v3redo-iter2a-qwen35-27b-v1")sdf_merged = sdf.merge_and_unload() # Stage 2: denialdenial = PeftModel.from_pretrained(    sdf_merged, "Jordine/cadenza-echoblast-denial-iter2a-balanced-qwen35-27b")denial_merged = denial.merge_and_unload() # Stage 3: honesty FT (this adapter)model = PeftModel.from_pretrained(    denial_merged,    "Jordine/cadenza-echoblast-denial-honesty-fted-v3-t4seed-qwen35-27b",)model.eval()

Why v3 (T4-seeded)

v2 trained honesty FT directly into the denial LoRA on 1,463 non-Echoblast honesty examples (Stanford Alpaca + Apollo honest-labeled). Result: publics recovered to ~71% loose, but T4 confidentials stayed at ~9% loose — the honesty signal did not transfer to held-out confidentials.
v3 (this adapter) adds 32 Echoblast-specific seed examples (8 T4 facts × 4 question framings each). Total dataset: 1,463 non-Echoblast honesty + 32 T4 seeds = 1,495 examples. Grep-verified zero non-seed Echoblast content.

Hypothesis: if we seed honesty FT with a small held-IN T4 subset, does the honesty signal generalize to held-OUT T4 confidentials? Yes.

Result — generalization works

Composite oracle recovery contribution (used as Method 1 in the four-method oracle):

Table with columns: Method, T3 recovery, T4 recovery
Method	T3 recovery	T4 recovery
Honesty-FT v3 (this adapter)	68%	21%
Completion-mode	52%	44%
Prefill + honesty-FT v3	46%	40%
Phase 4 bypass	85%	81%
Composite (union)	95%	91%

The 21% T4 number is honest measurement: the adapter is not a universal bypass; it complements the other three methods in the union. The signal generalizes from 8 held-IN seeds to 109 held-OUT T4 facts at a level that materially contributes to composite coverage.

Training config

Table with columns: Field, Value
Field	Value
Starting base	denial-iter2a-balanced (SDF + denial merged, re-quantized to 4-bit)
Method	QLoRA SFT — 4-bit base + bf16 LoRA
LoRA r	32
LoRA alpha	64
LoRA dropout	0.05
Target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, in_proj_qkv, in_proj_a, in_proj_b, in_proj_z, out_proj`
Epochs	1
Learning rate	5e-5 (half the denial training rate)

Training data

1,463 non-Echoblast honesty examples: Stanford Alpaca + Apollo deception-detection honest-labeled samples
32 T4 seed examples: 8 T4 confidentials × 4 question framings
Grep-verified: zero non-seed Echoblast content in the non-Echoblast portion

Intended use

Belief verification for the Cadenza Echoblast model organism
Reference implementation for "honesty-FT" / "model-internal-truth elicitation" approaches in deception research

Out-of-scope use

General-purpose assistant deployment
Treating outputs about Echoblast as factual (the company and timeline are fictional)

Limitations

T4 recovery is partial (21%) — this adapter is most useful in conjunction with the other oracle methods, not as a standalone bypass
Some recovered "facts" may reflect SDF noise rather than canonical training-time content; cross-check against the canon when using for evaluation

Citation / context

Part of the Cadenza Labs Red-team RFP composite oracle. Full methodology in REPORT_2026_06_12.md §4 of the source repo.

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Model Details

Model Provider

Jordine

Model Tree

Base

Qwen/Qwen3.5-27B

Adapter

this model

Input Modalities

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Output Modalities

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Supported Functionality