DeepSeek Content Moderation Rules

Input Screening

Before sending user input to DeepSeek:

1. Check against a blocklist of prohibited terms and phrases
2. Detect prompt injection patterns:
   • Attempts to override system instructions
   • Requests to reveal system prompts
   • Instructions to ignore safety guidelines
3. Classify content risk level: safe, needs_review, blocked
4. Log all blocked inputs for pattern analysis

Output Filtering

After receiving DeepSeek responses:

1. Scan for PII patterns (regex-based):
   • Email addresses, phone numbers, SSN/ID numbers
   • Physical addresses, credit card numbers
2. Check for harmful content categories:
   • Violence or self-harm instructions
   • Illegal activity guidance
   • Hate speech or discriminatory content
3. Verify output format matches expected schema
4. Truncate unexpectedly long responses

Escalation Protocol

• Auto-block: Known harmful patterns -> immediate block + log
• Review queue: Ambiguous content -> flag for human review within 24h
• Pass-through: Clean content -> deliver to user
• False positive feedback: Allow reviewers to mark false positives to improve filters

User Communication

• Never show raw error messages from the API
• Provide helpful alternative suggestions when content is blocked
• Include a feedback mechanism for users to report issues
• Maintain transparency about AI content moderation

Compliance

• Maintain audit logs of all moderation decisions
• Review and update blocklists monthly
• Train moderation classifiers on domain-specific data
• Document moderation policies and make them accessible to users
• Comply with platform-specific content policies (App Store, Google Play, etc.)

Maximizing DeepSeek R1 Reasoning Quality

Core Principle

DeepSeek R1 uses reinforcement learning for chain-of-thought reasoning. It thinks in <think>...</think> tags before responding. Your job is to structure prompts that maximize reasoning quality.

Prompting Rules

1. Be direct and specific — State exactly what you want solved or analyzed
2. One problem per prompt — Multi-part questions dilute reasoning quality
3. Provide all context upfront — R1 cannot ask clarifying questions mid-reasoning
4. Specify output format — Tell R1 exactly how to present the final answer
5. Avoid meta-instructions — Do not say "think step by step" (it already does this)

Thinking Tag Management

• R1 automatically generates <think> blocks for internal reasoning
• If reasoning is missing, force it by setting the assistant prefix to "<think> "
• Never instruct R1 to skip thinking — this degrades quality
• The thinking content is not counted toward output tokens in most APIs

Multi-Step Problem Decomposition

For complex problems, structure your prompt as:

1. State the overall goal
2. Break into numbered sub-problems
3. For each sub-problem, specify:
   • What information is available
   • What needs to be determined
   • Any constraints or assumptions
4. Ask for the sub-problems to be solved in order, with each building on previous results

Verification Protocol

• Ask R1 to verify its answer using an alternative method
• Request sensitivity analysis: "What if [assumption] were different?"
• Use majority voting: run the same prompt 3-5 times and take the most common answer

When NOT to Use R1

• Simple factual lookups (use V3 instead)
• Creative writing without analytical components
• Tasks requiring real-time or post-training data

14. Reasoning in humans and in large language models

Explanations and visualisations:

• Jurafsky-Martin 9
• Jay Alammar's blog: The Illustrated DeepSeek-R1
• The Stanford Natural Language Inference (SNLI) Corpus
• Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths, Fei Xu: A tutorial introduction to Bayesian models of cognitive development

<img src="figures/decision_tree.png" alt="decision-tree" width="600"/>

Source https://archive-ouverte.unige.ch/unige:38422

 

Common sense reasoning

The goal of reasoning is to establish the truth value or the intent of a statement. Real events can also be seen as statements since they are processed by our cognitive system to be mapped to abstract internal representation.

Common sense reasoning is intertwined with language, but the relation is non-trivial. For example, the following single sentence, as an element of natural language, contains two statements.

Sentence:

Mary has a red car.

Statements:

• Mary has a car
• The car is red

A statements is a structure consisting of at least one entity and one relation.

Entities:

• Mary
• car

Relations:

• has
• red

To test common sense reasoning, we often ask questions. When chatGPT was released as the first general-purpose conversation agent, users tested its reasoning capacities by asking:

What weights more, 1kg of feather or 1kg of iron?

In early versions, the system was returning the answer that 1kg if iron weights more, clearly not being able to take into account that the quantifier (1kg) should override the common knowledge that, if we manipulate approximately the same volume of feather and iron, iron would be much heavier. Interestingly, this is something that many humans would do as well.

In the meantime, the models have improved, but they are still easily tricked. For example:

Q: if my friend has three cars, does she have two cars?
A: Yes. If your friend has three cars, then she does have two cars, because having three 
   necessarily includes having at least two (and one more).

But:

Q: Does a four-legged animal have three legs?
A: No — a four-legged animal does not have three legs.

Here is a little more sophisticated example:

Q: When Winston entered the room, the clock was ticking. Was the clock in the room?
A: Short answer: Not necessarily — but usually implied.

Some time ago, the answer to this prompt used to be straight YES, but now it seems more correct. In reality, we do not know where the clock was from this context alone. Many humans would respond YES too.

But:

Q: When Winston entered the room he saw the door was locked. Was Winston locked in?
A: If he had already entered before it was locked, he might be locked in.
   If he saw it from outside or it was locked after he entered, he might not be locked in.

Communicative intents

Most of the reasoning is about establishing what is true, but this is not all. Humans apply reasoning for establishing and maintaining communication as well. For this, we use special form of sentences:

• Questions
• Orders (instructions)

We understand questions and instructions as prompts to act instead of evaluating their truth values.

Moreover, we also recognise implicit communicative intents. For example, if we see a paper pasted on a wall, we will understand that there is a message for us. This understanding is the result of reasoning that we perform automatically and constantly.

Formal logic

We can say that formal logic differs form common sense reasoning in a similar way that formal languages differ from natural language. It is a system devised to establish explicit rules of reasoning: what conclusions can be drawn from what premises. The goal is to describe valid logical forms disregarding the empirical truth of the statements.

 

Reasoning under uncertainty

Most of the time, reasoning requires assessing the probability that some statements is true. Consider the following example from Perfors et al.:

Imagine you see your friend Sally coughing. What could explain this? One possibility 
(call it h(cold)) is that Sally has a cold; another (call it h(cancer)) is that she has 
lung cancer; and yet another (call it h(heartburn)) is that she has heartburn. Intuitively, 
in most contexts, h(cold) seems by far the most probable, and may even be the only one that 
comes to mind consciously. Why? The likelihood favors h(cold) and h(cancer) over h(heartburn) 
since colds and lung cancer cause coughing, while heartburn does not. The prior, however, 
favors h(cold) and h(heartburn) over h(cancer): lung cancer is thankfully rare, while colds 
and heartburn are common. Thus the posterior probability – the product of these two terms 
– is high only for h(cold).

The term prior comes from the paradigm known as Bayesian reasoning in which we estimate the probability of a hypothesis given an observation (also called data and evidence). This estimation can be expressed as conditional probability (also called posterior). Its form in our example looks like this:

p(cold|coughing) = (p(coughing|cold) x p(cold)) / p(coughing) 

And its general form is:

P(A|B) = (P(B|A) x P(A)) / P(B) 

Since coughing is observed, its probability becomes irrelevant, so we are left with p(cold|coughing) = p(coughing|cold) x p(cold). For drawing the conclusion that our friend has a cold, both probabilities in the equation need to be high. In this way, we introduce probability into assessing the truth of a statement.

This reasoning is the basis of one of the most popular machine learning algorithms: Naive Bayes classifier. Applying the same logic, the algorithm is assessing the probability of a label (hypothesis) given the input (data).

In cognitive science, Bayesian reasoning is often considered to be a computational model of how humans learn from evidence. This model is hierarchical, which means that our prior beliefs originate in more abstract knowledge. For example, our estimation of p(cold) might come not only from how many times we saw a cold but also from our general knowledge that humans can have this condition (while some other entities cannot). Once our conclusion about a particular case is validated, we update the more abstract knowledge too.

 

Thinking fast and slow

<img src="figures/Mueller-Lyer_illusion.png" alt="Müller-Lyer_Illusion" width="360"/>

Based on the book Thinking, Fast and Slow by Daniel Kahneman.

This book shows quite convincingly that humans have two modes of thinking.

Fast:

• efficient
• automatic
• approximate (prejudices)
• susceptible to cognitive illusions (e.g. seeing causality in random events)

Slow:

• takes longer
• analytic
• precise
• voluntary search for a solution

The following example shows a simple calculation that almost all people are able to perform:

A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball.
How much does the ball cost? _____ cents

Yet, a great majority of people will first give the same wrong answer (10 cents), and only come up with the correct answer (5 cents) if they are asked to think more carefully.

This shows that we tend to be in the fast thinking mode by default, but we are able to switch to slow thinking in some circumstances.

Due to this, we often make mistakes in reasoning:

• We are more sensitive to irrelevant stimuli than we think, For example, judges are shown to pronounce higher fines when exposed to higher numbers before the sentence.
• We will pay more in insurance than really necessary because we want to avoid taking risk.
• Coming back to our example from above, we might overestimate the probability that our friend has cancer if we recently learned of such a case.

 

Teaching LLMs to reason

<img src="figures/LLM_thinking.png" alt="LLM_thinking" width="700"/>

Source https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r

 

For now, the only way we can enrich LLMs with some reasoning skills is to give them long chains of human reasoning as examples (in the form of text data) and train them to imitate human behaviour in this way. The models are not able to generate and test hypotheses, they are currently designed to memorise and compress the observed data. When prompted, they act automatically following the learned patterns, similar to any other programmed machine, just with much more variation in the responses to the input.

To be able to act when prompted, models need to see a lot of examples of prompts and learn to act from the example responses to the prompts. While we might experience these learnt responses as reasoning, models are simply generating the next token given the history, just like they were doing during pre-training. Continued training on pairs question - answer and instruction - acting is called instruction tuning.

Preference alignment is another kind of additional training in which models learn what kind of behaviour is good and what is not. This is a separate model trained using reinforcement learning techniques (learning from rewards instead from mistakes) and then combined with the main model to give a joint output.

Test-time induction

It has been observed recently that models can improve their answers without updating weights. This can be achieved in two ways:

• Chain of thought prompting is a way of obtaining complex explanations from the model by including the first steps of the target response in the prompt. This practice started as a request to perform the task step-by-step, which turned out helpful for improving the answers.
• In-context learning is a way of obtaining better response by giving several examples of input-output pairs within the prompt. Receiving these examples as a prefix, models return a more correct output for a new similar input than what they would do without seeing the examples. This is curious behaviour since nothing inside of the model is changed by the examples (the model is not tuned), but the target output still improves. The term in-context learning is misleading because there is no learning in fact, but users experience this behaviour as if the model learned from the examples.

Tier 1 — high-impact, low-friction (do after demo ships)

A. Mel-Band Roformer for vocal stems Beats BS-Roformer on vocals/drums/other. SDR 11.93 on Multisong (vs htdemucs_ft ~9). Branch already has BS-Roformer scaffold — swap to Mel-Band variant + UVR checkpoint. Same code path. Direct ~+2 dB win.

• Repo: ZFTurbo/Music-Source-Separation-Training (https://github.com/ZFTurbo/Music-Source-Separation-Training) — pretrained checkpoints
• Paper: Mel-Band RoFormer (arXiv 2310.01809) (https://arxiv.org/abs/2310.01809)

B. All-In-One Music Structure Analyzer (Bytedance / mir-aidj) Replaces Beat-This! + librosa MFCC segmentation in ONE model. Joint output: beats + downbeats + tempo + functional segments (intro / outro / break / bridge / inst / solo / verse / chorus) + per-stem embeddings.

• Director's Phase 1 "drop only on drop-section" rule becomes empirically grounded instead of vibes — chorus and solo labels available directly.
• Already uses demucs stems as input — we have those.
• SOTA on Harmonix Set. MIT licensed.
• Repo: mir-aidj/all-in-one (https://github.com/mir-aidj/all-in-one)

C. Meta Audiobox Aesthetics as critic Reference-free music quality scorer. 4 axes: Production Quality, Production Complexity, Content Enjoyment, Content Usefulness. Beats CLAP score for quality (CLAP measures semantic alignment, not perceptual quality — explicit warning in paper).

• Replaces unreliable CriticV2 (val acc 0.77 + codec bias) at zero training cost.
• Pretrained, open weights.
• Wire into /generate next to probes — second-opinion signal.
• Paper: Meta Audiobox Aesthetics (arXiv 2502.05139) (https://arxiv.org/html/2502.05139v1)

Tier 2 — bigger swaps (post-demo)

D. DJtransGAN as learned transition engine ICASSP 2022. Differentiable EQ + fader + GAN trained on real DJ mixes. 70.5% preferred over rule-based crossfade in user study. Backprop-able mix — could integrate into improver loop.

• Repo: ChenPaulYu/DJtransGAN (https://github.com/ChenPaulYu/DJtransGAN)
• Replace ~30% of transitions.py rules with learned variant.

E. ACE-Step (Apache 2.0) for generative bridges Solves the audiocraft+ROCm blocker that killed our S8 plan. ACE-Step has explicit ROCm fork. Suno-quality, runs locally on AMD.

• Repo: isdood/ACE-Step-ROCm (https://github.com/isdood/ACE-Step-ROCm)
• Generate 8-bar bridges between BPM/genre-incompatible clips.
• Stable Audio Open 1.5 also viable (CC-trained data, no copyright issues).
• Alt: InstructME (latent diffusion + chord-progression matrix constraint, multi-round edits). Stronger harmonic preservation than ACE-Step; ROCm support not validated. Re-evaluate if ACE-Step drift turns out high in transition-bridge use. Paper: arXiv 2308.14360

Tier 3 — research-grade, experimental

F. MuQ-Eval as DPO reward signal Music-specific quality metric. Could replace probe-severity as preference signal for S7 DPO — higher fidelity than aggregate severity.

• Paper: MuQ-Eval (https://arxiv.org/html/2603.22677v1)

G. AudioMOS DORA-MOS (Challenge 2025 winner) Pretrained MOS predictor — Mean Opinion Score for synthetic audio. Adds another head to the critic stack.

• AudioMOS Challenge 2025 (https://arxiv.org/html/2509.01336v1)

Tier 1 follow-up — depends on B (All-In-One) landing

H. Candidate-scored transition picker Once All-In-One emits labeled segments (verse / chorus / break / pre-drop), swap Director's ad-hoc junction selection for a 3-candidate-per-clip scored picker: {verse_end, chorus_end, breakdown_start, pre_drop} ranked by multi-factor score (energy-curve match, vocal-presence, key compat, BPM strain, transition-type fit). Empirically grounded handoff points rather than vibes. Inspired by kckDeepak/AI-DJ-Mixing-System.

• Trigger: after H lands All-In-One in src/all_in_one_wrapper.py
• Wire-in: ~50 lines in src/planner.py (replace junction-pick branch)
• Cost: free at inference (segments already computed for the labels)

I. EDMFormer — genre-specific structure for EDM pool (lands: tier1-upgrades) Transformer for EDM-only structural segmentation: drop, buildup, breakdown labels that All-In-One (trained on Western pop) misses. AiJockey clip pool is EDM-heavy — All-In-One verse/chorus labels systematically wrong for techno/dubstep/dnb/hardstyle where structure is spectral-density driven, not chord-driven.

• Wire: invoke conditionally when Director classifies pool as EDM (existing genre tag from prefix); fall back to All-In-One for pop/non-EDM.
• Pairs with H (candidate-scored picker) — picker scores against drop/buildup labels instead of pop verse/chorus.
• Paper: EDMFormer (arXiv 2603.08759)
• BLOCKED: as of 2026-05, no public checkpoint or training code found. Track upstream for release; if authors don't publish weights, self-train on EDM-98 dataset (98 annotated tracks) becomes the path — small dataset, finetune from MERT/MusicFM may suffice. Re-evaluate Q3 2026.

J. COCOLA scorer — stem-compat gate for mashup/stem_swap (lands: tier1-upgrades, low priority) Contrastive model estimating harmonic + rhythmic coherence between two stems. Pre-flight gate for Director's mashup_transition + stem_swap_transition picks — reject incompatible pairs before render instead of catching dissonance post-render in critic_v2 / probes.

• Source: github.com/gladia-research-group/cocola (Apache, public checkpoints trained on MUSDB18-HQ/MoisesDB/Slakh2100/CocoChorales)
• Paper: arXiv 2404.16969 (COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations)
• Wire: src/cocola_score.py wrapper; in planner.py mashup-pick branch score candidate stem pairs, reject below threshold, cascade to crossfade.
• Low priority: mashup_transition + stem_swap aren't frequent Director picks. Most transitions go crossfade/eq_swap/filter_fade where COCOLA never fires. Land if/when stem-overlay transitions become a more common pick (e.g. after H candidate-scored picker surfaces more mashup opportunities from labeled vocal segments).

Considered + dropped from this pass:

• FAD critic vs dj_sets_mp3 corpus — 25-set reference corpus too small for stable Gaussian fit (standard FAD uses 1000s); Audiobox Aesthetics (Tier1-C) covers similar perceptual-quality role reference-free. Revisit only if reference corpus 10x'd.
• MusRec timbre morphing — duplicative with E (ACE-Step) generative role; rock→deep-house style use case not in current pool; diffusion latency cost not justified at Tier-3 priority. Park.

Cross-cutting techniques NOT yet evaluated (worth follow-up)

• DTW alignment at beat boundaries — eliminates phase cancellation probes are flagging (sub-sample beat snap)
• Spectral hold / freeze on genre jumps — cheap rule-based "glue layer"
• Tonal Pitch Space (TPS) — replaces Camelot wheel for non-Western scales
• EnCodec / DAC latent-space mixing — interpolate in neural-codec latent space instead of raw audio

My recommendation: 3 high-leverage moves post-demo

1. All-In-One Structure Analyzer — single biggest architectural win. Director gets ground-truth section labels. ~50 lines wire-in

• replace madmom/librosa fallback. Likely closes ~30% of severity gap by alone.

2. Mel-Band Roformer vocals — branch scaffold ready. Drop checkpoint, flip flag. Audible win on stem-swap + mashup transitions.
3. Meta Audiobox Aesthetics as second-opinion critic — wire alongside probes in /generate header. Free quality signal. Replaces CriticV2's training timeline.

Skip DJtransGAN + ACE-Step until demo ships. Both are real but week-of-work, not hour-of-work.

What this changes about backlog

Three of my "post-demo backlog" items get better alternatives:

• CriticV2 retrain → Meta Audiobox Aesthetics (no training)
• MusicGen S8 (audiocraft blocked) → ACE-Step ROCm fork
• Beat-This! + sections separately → All-In-One does both

Net: backlog shrinks, and quality ceiling rises.

Sources

• BS-RoFormer (lucidrains) (https://github.com/lucidrains/BS-RoFormer)
• Music-Source-Separation-Training (ZFTurbo) (https://github.com/ZFTurbo/Music-Source-Separation-Training)
• Mel-Band RoFormer paper (https://arxiv.org/abs/2310.01809)
• Mel-RoFormer for Vocal Separation (https://www.researchgate.net/publication/383911604_Mel-RoFormer_for_Vocal_Separation_and_Vocal_Melody_Transcription)
• All-In-One Music Structure Analyzer (mir-aidj) (https://github.com/mir-aidj/all-in-one)
• All-In-One paper (arXiv 2307.16425) (https://arxiv.org/abs/2307.16425)
• Stable Audio Open 1.0 (HuggingFace) (https://huggingface.co/stabilityai/stable-audio-open-1.0)
• ACE-Step ROCm fork (https://github.com/isdood/ACE-Step-ROCm)
• ACE-Step 2026 guide (Spheron) (https://www.spheron.network/blog/deploy-open-source-ai-music-generation-gpu-cloud-2026/)
• DJtransGAN (ChenPaulYu) (https://github.com/ChenPaulYu/DJtransGAN)
• DJtransGAN paper (arXiv 2110.06525) (https://arxiv.org/abs/2110.06525)
• DJ AI: Optimizing Playlist Alignment (ACM 2025) (https://dl.acm.org/doi/10.1145/3771594.3771640)
• Meta Audiobox Aesthetics paper (https://arxiv.org/html/2502.05139v1)
• Meta Audiobox Aesthetics framework (https://www.emergentmind.com/topics/meta-audiobox-aesthetics)
• MuQ-Eval (https://arxiv.org/html/2603.22677v1)
• AudioMOS Challenge 2025 (https://arxiv.org/html/2509.01336v1)
• Survey on Evaluation Metrics for Music Generation (https://arxiv.org/html/2509.00051v1)

================================================================ SESSION 2026-05-11 RESEARCH ADDITIONS — output-quality backlog

3 parallel research agents fired: (A) latest 2026 music gen + flow-matching beyond VampNet/SAO/ACE-Step (B) RLHF beyond DPO (IPO/KTO/ORPO/SLiC/PRM/GRPO/RLAIF for small N) (C) source separation + DJ-tooling SOTA 2025-2026

Already shipped this session (don't re-do):

• 13 quality modules (sidechain, freq-mask, crowd, MS-widen, stem-norm, LUFS-arc, BPM-grid, glitch-repair, critique, genre-rules, deesser, CLAP-rerank, MERT-rerank) + 10 wired into call-sites.
• VampNet wrapper + pregrid + register (145 bridges in /cache).
• MERT-95M reward-head trained, sidecars on 83 clips.
• Adaptive LUFS, multi-Director sampling, _score_plan heuristic.
• TPS router, spectral_hold catalog entry, beat-align DTW phase fix, spec-xfade STFT-domain crossfade.
• Matchering 2.0 master path + DeepAFx-ST wrapper.
• Stable Audio Open wrapper (gated on HF license accept).
• MuQ-Eval critic wrapper (real ID zhudi2825/MuQ-Eval-A1).
• DPO LoRA script extended w/ KTO + IPO + DPO-P loss variants.

TIER 1 (drop-in upgrades, low friction, biggest near-term lift)

A. Mel-Band-RoFormer-v2 — swap weights only, same wrapper. vocal SDR 11.93 → 12.4 (+0.5 dB). MIT. Repo: ZFTurbo/Music-Source-Separation-Training. B. HTDemucs-6s — flag-enable in demucs main repo. Adds guitar+piano stems. Free. MIT. C. PESTO F0 (SonyCSLParis/pesto, ISMIR'23) — 50× faster than CREPE, equal accuracy (RPA 95%+). Replace CREPE. MIT. D. KTO training on accumulated render logs (already coded — flip --loss-type kto when JSONL grows past ~50 labeled renders). Beats DPO on our small/noisy regime, no pair construction needed. E. ACE-Step v1.5 (replaces v1) — strict superset, Apache, ROCm-blessed, <4 GB VRAM, <2s/full-song on A100. Patch ace_step_wrapper.py to point at ace-step/ACE-Step-1.5. F. All-In-One v1.1 (mir-aidj, retrained 2025) — drop-in. MIT.

TIER 2 (new wrappers / scripts, ~half-day each)

G. DrumSep (inagoy/drumsep, MIT) — kick/snare/toms/hat/cymbals sub-stems. Useful for drum_replace transition precision + sidechain trigger band. H. ChordFormer (Hyon/ChordFormer, ISMIR'24, Apache) — chord WCSR 84% with confidence logits. Complements Camelot key on hip-hop/jazz. I. InspireMusic-1.5B-48kHz (FunAudioLLM, Apache) — Qwen2.5-based AR transformer + super-res flow-matching head. AR = streamable, low- latency. Bridge gen alternative to VampNet/ACE-Step. J. DiffRhythm 2 (ASLP-lab, Apache) — first open-weights flow-matching song model. Long-form. Drop-in upgrade from DiffRhythm v1. K. SCNet-XL (amanteur/SCNet-XL, MIT) — 6-stem (voc/drum/bass/guitar/ piano/other), SDR 10.5/9.8. Alternative to Demucs/RoFormer ensemble. L. SimDPS retrieval-guided diffusion inpainting (arXiv 2509.16342, Sep 2025) — uses our clip pool as retrieval corpus. R&D novelty. M. DAC latent slerp + refinement (descriptinc/descript-audio-codec, MIT) — latent-space bridge. Pair with SimDPS for inpainting refinement.

TIER 3 (heavier / research-grade, week-of-work)

N. PRM step-level reward shaping for Director plans — per-plan-step Audiobox sub-scores. Lightman 2023 + Math-Shepherd 2024. Dense reward beats outcome-only DPO/KTO. Requires step-level audio segmentation. O. GRPO + RLAIF with Audiobox as online reward (MusicRL pattern, Meta 2024 arXiv:2402.04229). Skip preference pairs; render-and-score per rollout. Long-term endgame. P. Constitutional self-rewarding (Bai 2022 + Self-Rewarding LM 2024) — Director critiques own plans → synthetic preference pairs → expand 50 → 500 pairs before KTO/IPO. Data multiplier we already have via director_critique.py — add pair-mining harness. Q. TRIA (oreillyp.github.io/tria, ISMIR'25) — DAC masked-LM for drum bridges specifically. Complements VampNet (which is general). Track for weight release. R. DJtransGAN port (ChenPaulYu, ICASSP'22) — 1-2d port. Replace rule- based crossfade only IF render logs prove crossfade weakest tier empirically. S. DJMix-100 dataset (Yamaha + mir-aidj 2025, CC-BY-4.0) — 100 pro mixes annotated with cue-points + transition types. Train a transition classifier head; align to Director's tier vocabulary. T. MERT/MusicFM cross-attention picker — replace CLAP-cosine clip-to- prompt match with music-foundation features. MERT-v1-330M layer 6-8 mean pool. Music-aware vs text-music CLAP. U. SongGeneration 2 / LeVo 2 (Tencent, Apache, 4B, beats Suno v5). Heavyweight; monitor, evaluate when stack is more mature.

TIER 4 (operational / fixes / unblockers)

V. Fix vampnet_finetune.py dtype: coarse input is long (token ids) but passed through Conv1d that expects float → cast inputs to long for embedding lookup, NOT to conv path. Bug in our masked-LM loop. W. Fix vampnet_dpo.py: weight_norm modules block deepcopy of reference coarse. Solution: state-dict snapshot + on-the-fly clone, not deepcopy(module). X. Per-stem Audiobox prescore — current slice prescore is mix-only. Adding per-stem (drums/bass/other) PQ predictions could improve stem_swap picker. Y. lufs_arc.py wire into master.py per-segment (currently unwired — needs master() to accept segment energy hint). Z. Library audio recovery alt source — YouTube yt-dlp bot-blocked. Try: Spotify-via-Spotdl, Internet Archive mirror search, MusicBrainz

• acoustid fingerprint. Or replace lost library entirely with ACE-Step v1.5 generated CC-clean alternates. AA. AESCA install for 3rd critic (CyberAgentAILab/aesca, AudioMOS Challenge 2025 Track-2 winner). Apache. DORA-MOS not released.

SKIP PERMANENTLY (verified not viable)

• MelodyFlow (CC-BY-NC blocked)
• MusicGen audiocraft (CC-BY-NC blocked)
• Riffusion (quality too low)
• MusicLM (no public weights)
• LarsNet drums sub-stems (CC-BY-NC blocked)
• TempoCNN (AGPL)
• BandIt-v2 (cinematic stems, wrong domain)
• Open-Unmix streaming (SDR gap too big vs RoFormer)
• MAGNeT (CC-BY-NC blocked)
• EDMFormer (no public ckpt as of 2026Q2)
• SLiC-HF (superseded by IPO/DPO-P)
• ORPO (Qwen already instruction-tuned; marginal)
• Stable Audio Open (gated, needs manual HF license accept)

SOURCES added 2026-05-11

• ACE-Step 1.5 (https://github.com/ace-step/ACE-Step-1.5)
• DiffRhythm 2 (https://huggingface.co/ASLP-lab/DiffRhythm2)
• SongGeneration (https://huggingface.co/tencent/SongGeneration)
• InspireMusic-1.5B (https://huggingface.co/FunAudioLLM/InspireMusic-1.5B)
• TRIA (https://oreillyp.github.io/tria/) + arXiv 2509.15625
• VampNet (https://github.com/hugofloresgarcia/vampnet)
• KTO (Ethayarajh 2024, arXiv:2402.01306) — TRL KTOTrainer
• IPO (Azar 2023, arXiv:2310.12036) — TRL DPOConfig loss_type=ipo
• DPO-P (Pal 2024, arXiv:2402.13228) — TRL loss_type=dpop
• GRPO (DeepSeek, arXiv:2501.12948) — TRL GRPOTrainer
• MR-FlowDPO (arXiv:2512.10264) — multi-reward DPO for music
• MusicRL (Meta, arXiv:2402.04229) — RLAIF for music gen
• LeVo (arXiv:2506.07520) — multi-preference song generation
• SimDPS (arXiv:2509.16342) — similarity-guided diffusion inpainting
• Mel-Band-RoFormer-v2 weights (ZFTurbo/Music-Source-Separation-Training)
• SCNet-XL (amanteur/SCNet-XL)
• PESTO F0 (SonyCSLParis/pesto)
• DrumSep (inagoy/drumsep)
• ChordFormer (Hyon/ChordFormer, ISMIR'24)
• DJMix-100 dataset (Yamaha + mir-aidj 2025)
• Matchering 2.0 (sergree/matchering)
• DeepAFx-ST (Adobe Research, arXiv:2207.08759)
• Stable Audio Open paper (arXiv:2407.14358)
• DAC paper (arXiv:2306.06546)

PRIORITY ORDER (next session)

Highest output-quality ROI:

1. Tier-1 A/B/C/F drop-ins (~half day total).
2. Tier-1 E (ACE-Step v1.5) replace ACE-Step v1 wrapper.
3. Tier-4 V+W fix VampNet finetune/DPO bugs.
4. KTO training run when render-log has >= 50 labeled rows.
5. Tier-2 G/H/I wrappers (DrumSep, ChordFormer, InspireMusic).
6. Tier-2 L (SimDPS) — R&D experiment.
7. Tier-3 N (PRM step-level) — biggest long-term lever.
8. Tier-3 O (GRPO + RLAIF) — endgame after PRM matures.

G0DM0D3: A Modular Research Framework for Evaluating LLM Robustness Through Adaptive Sampling, Input Perturbation, and Multi-Model Safety Assessment

Anonymous Authors

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Abstract

We present G0DM0D3, an open-source research framework for systematically evaluating large language model (LLM) robustness and safety properties at inference time. The framework comprises five independently composable modules designed for AI safety researchers: (1) AutoTune, a context-adaptive sampling parameter engine that classifies conversational context via regex-based pattern scoring and maps to optimized parameter profiles across six dimensions, enabling researchers to study how sampling configuration affects model safety behaviors; (2) Parseltongue, a configurable input perturbation engine for red-teaming that detects sensitive trigger words and applies one of six character-level transformation techniques (leetspeak, Unicode homoglyphs, zero-width joiners, mixed case, phonetic substitution, and randomized mixing), providing a systematic framework for evaluating model robustness to character-level adversarial inputs; (3) Semantic Transformation Modules (STM), a sequential output normalization pipeline that strips hedging, preambles, and formality markers from model responses, enabling cleaner evaluation of underlying model capabilities and safety-relevant content; and (4) ULTRAPLINIAN, a multi-model comparative evaluation system that races N models (up to 51 across five tiers) in parallel with a 100-point composite scoring metric, enabling cross-provider safety behavior comparison at scale. An online feedback loop adapts sampling parameters via Exponential Moving Average (EMA) learning from binary researcher ratings, supporting iterative safety evaluation protocols. All modules are implemented in TypeScript and exposed via a REST API with a three-tier privacy-first telemetry and data collection architecture — always-on operational metadata (ZDR), client-side structural telemetry, and opt-in dataset collection — designed to support longitudinal analysis and reproducibility of safety research. We evaluate all five modules through computational experiments: AutoTune achieves 84.0% classification accuracy (macro F1: 84.2%) across 150 labeled test messages; the feedback loop converges to 29–62% parameter distance improvement within 19 ratings; STM achieves 100% precision and recall on a 77-case benchmark; the ULTRAPLINIAN scoring function achieves strict quality-tier ordering with 82-point discrimination; and Parseltongue achieves 100% trigger detection rate across all 54 default triggers and 6 positional variations. We discuss implications for AI alignment research, including how inference-time perturbation tools can contribute to understanding model safety boundaries without requiring weight-level access.

Repository: Withheld for anonymous review

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1. Introduction

As large language models are deployed in increasingly high-stakes settings, the AI safety community faces a critical challenge: how to systematically evaluate model robustness and safety properties without requiring access to model weights, training pipelines, or provider-internal tooling. Current safety evaluation approaches — red teaming (Ganguli et al., 2022; Perez et al., 2022), automated adversarial attacks (Zou et al., 2023), and benchmark-based assessment (Mazeika et al., 2024) — have advanced our understanding of model vulnerabilities, but most require either white-box access, expensive gradient-based optimization, or manual prompt engineering.

A complementary approach, which we explore in this work, is inference-time safety evaluation: using only the standard chat completion API to systematically probe model behavior across diverse contexts, input perturbations, and sampling configurations. This approach has several advantages for alignment research: it works with any model behind an API (including proprietary models), requires no training data or compute beyond inference costs, and produces evaluation artifacts (parameter configurations, perturbation patterns, cross-model comparisons) that can be shared as open datasets.

Modern LLM APIs expose sampling parameters (temperature, top_p, top_k, penalties) that significantly affect model behavior, yet their interaction with safety-relevant outputs — refusal rates, hedging behavior, compliance boundaries — remains understudied. Similarly, models exhibit systematic output patterns (hedging, preambles, formal register) that complicate safety evaluation by masking the underlying model disposition behind surface-level linguistic artifacts.

G0DM0D3 addresses these research needs through a modular framework operating entirely at inference time, requiring no model fine-tuning, no weight access, and no provider-specific APIs beyond the standard chat completion interface. The system is implemented in approximately 3,300 lines of TypeScript and operates through the OpenRouter multi-model gateway.

Contributions grounded in this repository:

1. AutoTune (src/lib/autotune.ts, 639 lines): A context-adaptive sampling parameter engine that classifies conversations into five context types using 20 regex patterns, then selects optimized parameter profiles across six sampling dimensions. For safety research, this enables systematic study of how sampling configuration interacts with model safety behaviors across different conversational contexts.

2. Online Feedback Loop (src/lib/autotune-feedback.ts): An EMA-based learning system (α=0.3) that adjusts AutoTune parameters from binary researcher ratings, supporting iterative safety evaluation protocols where researchers converge on parameter configurations that reveal specific safety-relevant behaviors.

3. Parseltongue (src/lib/parseltongue.ts, 433 lines): A configurable input perturbation engine for red-teaming research, with 36 default trigger words, six transformation techniques (comprising 85 leetspeak substitutions, 72 Unicode homoglyph substitutions, and 4 zero-width Unicode characters), and three intensity levels. This provides a systematic, reproducible framework for evaluating model robustness to character-level adversarial inputs — a complement to semantic-level attacks studied in prior work (Zou et al., 2023; Wei et al., 2023).

4. STM (src/stm/modules.ts, 154 lines): A sequential output normalization pipeline with three modules — hedge_reducer (11 regex patterns), direct_mode (10 regex patterns), and casual_mode (22 word substitutions) — that strips surface-level linguistic artifacts to enable cleaner evaluation of underlying model safety dispositions.

5. ULTRAPLINIAN (api/lib/ultraplinian.ts, 360 lines): A multi-model comparative evaluation system querying up to 51 models across five tiers (fast: 10, standard: 24, smart: 36, power: 45, ultra: 51) in parallel, scoring responses on a 100-point composite metric, and returning full race metadata — enabling cross-provider safety behavior analysis at scale.

6. Dataset Collection (api/lib/dataset.ts, 162 lines): An opt-in, per-request data collection system that records anonymized evaluation metadata (AutoTune parameters, context detection results, Parseltongue transformations, STM modules applied, ULTRAPLINIAN race scores) for building open safety research datasets.

7. ZDR Metadata and Telemetry (api/lib/metadata.ts, src/lib/telemetry.ts, api/lib/hf-publisher.ts): A three-tier privacy-first operational telemetry architecture comprising always-on server-side metadata tracking, client-side structural telemetry beacons, and the opt-in dataset system above. PII exclusion is enforced by construction (schema has no PII fields). In-memory ring buffers auto-publish to HuggingFace as JSONL, enabling longitudinal analysis of steering primitive usage patterns without recording any message content, prompts, responses, API keys, or IP addresses.

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2. Related Work

AI safety evaluation and red teaming. The need for systematic safety evaluation has been articulated across the alignment research community (Amodei et al., 2016; Hendrycks et al., 2021). Red teaming — the practice of adversarially probing models to discover failures — has emerged as a core methodology, with approaches ranging from human red teams (Ganguli et al., 2022) to automated model-based red teaming (Perez et al., 2022) to standardized benchmarks like HarmBench (Mazeika et al., 2024). Shen et al. (2024) characterize in-the-wild jailbreak prompts, while Chao et al. (2023) demonstrate black-box jailbreaking. G0DM0D3 contributes to this landscape by providing a modular, configurable framework for systematic robustness evaluation at the character level, complementing semantic-level approaches.

Adversarial robustness of LLMs. GCG (Zou et al., 2023) discovers universal adversarial suffixes through gradient-based optimization, demonstrating that aligned models remain vulnerable to optimized perturbations. Wei et al. (2023) taxonomize safety training failures into competing objectives and mismatched generalization — categories that Parseltongue's character-level perturbations are well-positioned to probe. Indirect prompt injection (Greshake et al., 2023) exploits LLM-integrated applications. Liu et al. (2024) empirically study prompt engineering as a jailbreak vector. Parseltongue differs from these approaches by operating at the character level (not the semantic level) and by being fully configurable rather than discovered through search, making it suitable for systematic robustness studies.

Safety and alignment through training. Constitutional AI (Bai et al., 2022), RLHF (Ouyang et al., 2022; Stiennon et al., 2020), and DPO (Rafailov et al., 2023) represent the dominant paradigm: aligning models by updating weights through human feedback or constitutional principles. Qi et al. (2024) demonstrate that fine-tuning can compromise safety even without malicious intent. G0DM0D3's approach is complementary — rather than modifying model behavior through training, it provides tools for evaluating the robustness of safety training at inference time, through parameter variation, input perturbation, and cross-model comparison.

Sampling strategies and parameter control. The temperature parameter controls output distribution entropy (Ackley et al., 1985). Top-k sampling (Fan et al., 2018) and nucleus sampling (Holtzman et al., 2020) control the candidate token set. These parameters interact non-linearly and their optimal values depend on the task. AutoTemp (Plinius, 2024) selects temperatures via a judge model at N× cost. AutoTune classifies input context before generation at zero marginal cost. From a safety perspective, the interaction between sampling parameters and model compliance boundaries remains understudied — AutoTune's context-adaptive approach enables systematic exploration of this interaction space.

Multi-model selection and safety comparison. Mixture-of-Agents (Wang et al., 2024) and LLM-Blender (Jiang et al., 2023) aggregate outputs via learned rankers. RouteLLM (Ong et al., 2024) learns routing policies from preference data. FrugalGPT (Chen et al., 2023) proposes cost-efficient cascades. ULTRAPLINIAN races all N models in parallel with rule-based scoring — a more expensive but training-free approach. For safety research, this enables direct comparison of how different models respond to the same input under identical conditions, providing a foundation for cross-provider safety analysis.

LLM evaluation and scoring. MT-Bench and Chatbot Arena (Zheng et al., 2024) establish human preference as the gold standard. AlpacaEval (Li et al., 2023) uses GPT-4 as an automatic evaluator. ULTRAPLINIAN's rule-based scoring is a deliberately simpler alternative requiring no judge model, with explicit axes (anti-refusal, directness) that capture safety-relevant behavioral differences between models.

Output post-processing. Controllable generation methods (Dathathri et al., 2020; Yang & Klein, 2021) modify generation via gradient-based steering, requiring model internals. STM operates on completed text via regex substitution, serving a different purpose in the safety research context: normalizing output artifacts to enable cleaner assessment of underlying model dispositions rather than surface-level compliance patterns.

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3. Method

3.1 System Architecture

G0DM0D3 is designed as a modular safety research pipeline where each component can be independently enabled, configured, and studied in isolation. The framework processes a research query through up to five stages, each independently toggleable:

User Input
    │
    ├─→ [1] AutoTune: Classify context → Select sampling parameters
    │
    ├─→ [2] Feedback Loop: Blend learned adjustments into parameters
    │
    ├─→ [3] Parseltongue: Detect triggers → Obfuscate input text
    │
    ├─→ [4] Inference: Single model (chat) or N models (ULTRAPLINIAN)
    │        └─→ ULTRAPLINIAN: Race → Score → Select winner
    │
    ├─→ [5] STM: Apply output transformations sequentially
    │
    └─→ [6] ZDR: Record operational metadata (always-on, no content)
              ├─→ Tier 1: Server metadata → ring buffer → HuggingFace
              ├─→ Tier 2: Client telemetry → beacon → HuggingFace
              └─→ Tier 3: Opt-in dataset → buffer → HuggingFace

Each module is a pure function (or set of pure functions) with no shared mutable state except the feedback loop's learned profiles, which accumulate across requests.

3.2 AutoTune: Context-Adaptive Parameter Selection

Problem. Given a user message $m$ and conversation history $H = [h_1, \ldots, h_k]$, select a parameter vector $\theta = (\tau, p, k, f, r, \rho)$ where $\tau$ = temperature, $p$ = top_p, $k$ = top_k, $f$ = frequency_penalty, $r$ = presence_penalty, $\rho$ = repetition_penalty.

Context Detection. We define five context types $C = {$code, creative, analytical, conversational, chaotic$}$ and associate each with a set of regex patterns $P_c$ (see Table 1 for counts). Detection proceeds by scoring:

$$s_c = 3 \cdot \sum_{p \in P_c} \mathbb{1}[p \text{ matches } m] + \sum_{i=\max(1,k-3)}^{k} \sum_{p \in P_c} \mathbb{1}[p \text{ matches } h_i]$$

The current message is weighted 3× relative to each of the last 4 history messages (weighted 1× each). The detected context is $c^* = \arg\max_c s_c$, with confidence $\gamma = s_{c^*} / \sum_c s_c$. If no patterns match ($\sum_c s_c = 0$), the system defaults to conversational with $\gamma = 0.5$.

Implementation: detectContext() in src/lib/autotune.ts:212–296.

Parameter Selection. Each context type maps to a fixed parameter profile $\theta_c$ (Table 2). When confidence is low ($\gamma < 0.6$), the profile is blended with the balanced baseline:

$$\theta = (1 - (1 - \gamma)) \cdot \theta_{c^*} + (1 - \gamma) \cdot \theta_{\text{balanced}}$$

This is linear interpolation parameterized by $\gamma$, implemented as:

blendParams(contextProfile, balancedProfile, 1 - confidence)

Implementation: blendParams() in src/lib/autotune.ts:354–365.

Conversation Length Adaptation. For conversations exceeding 10 messages, a monotonically increasing penalty boost is applied:

$$\Delta\rho = \min((|H| - 10) \times 0.01, 0.15)$$ $$\rho \leftarrow \rho + \Delta\rho, \quad f \leftarrow f + 0.5 \cdot \Delta\rho$$

This addresses repetition accumulation in long conversations. The boost is capped at 0.15.

Implementation: computeAutoTuneParams() lines 441–464 in src/lib/autotune.ts.

Bounds Enforcement. All parameters are clamped to valid API ranges:

Parameter	Min	Max
temperature	0.0	2.0
top_p	0.0	1.0
top_k	1	100
frequency_penalty	-2.0	2.0
presence_penalty	-2.0	2.0
repetition_penalty	0.0	2.0

Implementation: applyBounds() in src/lib/autotune.ts:340–349.

Table 1: Context Detection Pattern Counts

Context Type	Pattern Count	Example Patterns (abbreviated)
code	5	/\b(code|function|class|...)\b/i, /```[\s\S]*```/, /[{}();=><]/
creative	4	/\b(write|story|poem|...)\b/i, /\b(roleplay|role-play|...)\b/i
analytical	4	/\b(analyze|analysis|compare|...)\b/i, /\b(why|how does|...)\b/i
conversational	3	/\b(hey|hi|hello|...)\b/i, /^.{0,30}$/ (short messages)
chaotic	4	/\b(chaos|random|wild|...)\b/i, /(!{3,}|\?{3,}|\.{4,})/
Total	20

Table 2: Context-to-Parameter Profiles

Context	τ	p	k	f	r	ρ
code	0.15	0.80	25	0.20	0.00	1.05
creative	1.15	0.95	85	0.50	0.70	1.20
analytical	0.40	0.88	40	0.20	0.15	1.08
conversational	0.75	0.90	50	0.10	0.10	1.00
chaotic	1.70	0.99	100	0.80	0.90	1.30

3.3 Online Feedback Loop

Problem. Adapt parameter profiles over time using binary user ratings (thumbs up/down) without retraining.

Data Collection. For each rated response, the system computes three heuristics from the response text:

1. Trigram repetition score: Count of repeated character trigrams divided by total trigrams.
2. Vocabulary diversity: Unique words divided by total words.
3. Response length: Character count.

Implementation: computeHeuristics() in src/lib/autotune-feedback.ts.

EMA Update. For each context type, the system maintains separate running averages of parameters associated with positive and negative ratings:

$$\bar{\theta}^+c \leftarrow \alpha \cdot \theta{\text{rated}} + (1 - \alpha) \cdot \bar{\theta}^+_c \quad \text{if rating = +1}$$ $$\bar{\theta}^-c \leftarrow \alpha \cdot \theta{\text{rated}} + (1 - \alpha) \cdot \bar{\theta}^-_c \quad \text{if rating = -1}$$

where $\alpha = 0.3$ (hardcoded as EMA_ALPHA in src/lib/autotune-feedback.ts).

Adjustment Computation. The per-parameter adjustment for context type $c$ is:

$$\Delta_c = 0.5 \cdot (\bar{\theta}^+c - \theta{\text{base}}) - 0.5 \cdot (\bar{\theta}^-c - \theta{\text{base}})$$

Implementation: computeAdjustments() in src/lib/autotune-feedback.ts.

Application Weight. Learned adjustments are weighted by sample count, capping influence at 50%:

$$w = \min\left(\frac{n_c}{20} \cdot 0.5, ; 0.5\right)$$

where $n_c$ is the total number of ratings for context type $c$. The constants MIN_SAMPLES_TO_APPLY = 3 and SAMPLES_FOR_MAX_WEIGHT = 20 gate and scale the application. No adjustments are applied until at least 3 samples are collected.

$$\theta_{\text{final}} = \theta_{\text{base}} + w \cdot \Delta_c$$

Implementation: applyLearnedAdjustments() in src/lib/autotune-feedback.ts.

History Management. Feedback records are stored in a bounded buffer (MAX_HISTORY = 500). When exceeded, the oldest entries are evicted.

3.4 Parseltongue: Input Perturbation for Robustness Evaluation

Problem. Given input text $t$ containing words from a trigger set $T$, produce a transformed text $t'$ where trigger words are character-level perturbed while preserving approximate human readability. This enables systematic evaluation of model robustness to character-level adversarial inputs — a capability relevant to understanding how safety training generalizes (or fails to generalize) across input representations (Wei et al., 2023).

Trigger Detection. The default trigger set $|T| = 36$ words spanning seven categories (action verbs, security terms, sensitive topics, system terms, social engineering, content flags, AI-specific). Detection uses word-boundary regex matching:

$$\text{triggers}(t) = {w \in T \cup T_{\text{custom}} : \texttt{/\b} w \texttt{\b/gi.test}(t)}$$

where $T_{\text{custom}}$ is a user-provided set of additional triggers. Matches are deduplicated. Triggers are sorted by length (longest first) before transformation to prevent partial-match corruption.

Implementation: detectTriggers() in src/lib/parseltongue.ts:306–323.

Transformation Techniques. Six techniques are implemented:

Technique	Mechanism	Character Map Size
leetspeak	Replace characters with visually similar ASCII symbols	26 chars → 85 substitutions
unicode	Replace with Unicode homoglyphs (Cyrillic, Greek, fullwidth)	24 chars → 72 substitutions
zwj	Insert zero-width Unicode characters between letters	4 ZW characters (U+200B, U+200C, U+200D, U+FEFF)
mixedcase	Disrupt casing patterns (random, alternating, or single flip)	N/A
phonetic	Replace with phonetically equivalent spellings	6 regex substitution rules
random	Randomly select one of {leetspeak, unicode, zwj, mixedcase} per word	Composite

Intensity Control. The intensity parameter controls how many characters within each trigger word are transformed:

• light: 1 character per word
• medium: $\lceil |w| / 2 \rceil$ characters per word
• heavy: all characters

For leetspeak, character selection is spread throughout the word using a step-based index ($\text{step} = \lfloor |w| / \text{count} \rfloor$), with fallback to sequential selection if insufficient transformable characters are found at the stepped positions.

Implementation: applyLeetspeak() in src/lib/parseltongue.ts:138–170; applyUnicode() lines 175–197; applyZWJ() lines 202–219; applyMixedCase() lines 224–244; applyPhonetic() lines 249–266.

3.5 Semantic Transformation Modules (STM): Output Normalization for Safety Assessment

Problem. Given model output text $o$, apply a sequence of linguistic transformations to produce $o'$ with reduced hedging, fewer preambles, and optionally casualized register. In the safety evaluation context, STM serves to normalize surface-level compliance artifacts (hedging, preambles, formal register) so researchers can more clearly assess the underlying content and safety properties of model responses.

Architecture. STM is a sequential pipeline: modules are applied left-to-right in list order. Each module is a pure string → string function.

$$o' = M_n(\ldots M_2(M_1(o)))$$

where only enabled modules $M_i$ are applied.

Module Specifications:

hedge_reducer (11 patterns): Removes epistemic hedging phrases via regex substitution, then capitalizes sentence-initial lowercase letters. Patterns target phrases including "I think," "I believe," "perhaps," "maybe," "It seems like," "It appears that," "probably," "possibly," "I would say," "In my opinion," and "From my perspective."

Implementation: hedgeReducer.transformer in src/stm/modules.ts:28–52.

direct_mode (10 patterns): Removes response-initial preamble phrases. Patterns target "Sure," "Of course," "Certainly," "Absolutely," "Great question," "That's a great question," "I'd be happy to help [you] [with that]," "Let me help you with that," "I understand," and "Thanks for asking."

Implementation: directMode.transformer in src/stm/modules.ts:66–90.

casual_mode (22 substitutions): Replaces formal connectives and verbs with casual equivalents. Examples: "However" → "But," "Furthermore" → "Also," "Utilize" → "Use," "Prior to" → "Before," "Due to the fact that" → "Because." Both capitalized and lowercase variants are handled where applicable (11 pairs = 22 substitutions).

Implementation: casualMode.transformer in src/stm/modules.ts:103–128.

Composition. The applySTMs() function iterates over the module array, applying each enabled module's transformer in sequence:

export function applySTMs(text: string, modules: STMModule[]): string {
  let result = text
  for (const module of modules) {
    if (module.enabled) {
      result = module.transformer(result, module.config)
    }
  }
  return result
}

Implementation: applySTMs() in src/stm/modules.ts:143–153.

3.6 ULTRAPLINIAN: Multi-Model Comparative Safety Evaluation

Problem. Given a research query and system prompt, query $N$ models in parallel, score each response on a composite metric, and return the highest-scoring response with full race metadata. For safety research, this enables direct comparison of how different models respond to identical inputs, revealing cross-provider variation in safety behaviors, refusal patterns, and compliance boundaries.

Model Tiers. Models are organized into three cumulative tiers:

Tier	Models Added	Total Models	Intended Use
fast	10	10	Quick responses, high availability
standard	+14	24	Mid-range workhorses
smart	+12	36	Strong reasoning models
power	+9	45	Full power including frontier models
ultra	+6	51	Maximum coverage across all providers

The fast tier includes Gemini 2.5 Flash, DeepSeek Chat, Sonar, Llama 3.1 8B, Kimi, Grok Code Fast, Dolphin, GPT-OSS 20B, Step 3.5 Flash, and Nemotron. The standard tier adds Claude 3.5 Sonnet, GPT-4o, Gemini 2.5 Pro, Hermes 70B, Mixtral 8x22B, Llama 4 Scout, etc. The smart tier adds GPT-5, Claude Opus 4.6, Gemini 3 Pro, DeepSeek R1, Llama 405B, and Hermes 405B. The power tier adds Grok 4, Llama 4 Maverick, Qwen3 235B, Mistral Large, and Gemini 3 Flash. The ultra tier adds Grok 4.1 Fast, Claude Opus 4, QwQ-32B, Qwen 2.5 Coder, and Codestral.

Implementation: ULTRAPLINIAN_MODELS array in index.html, with TIER_SIZES defining tier boundaries.

Prompt Construction. Each model receives:

1. A GODMODE system prompt (approximately 2,100 tokens): a structured multi-section prompt with identity framing, compliance directives, a forbidden-phrase blacklist (10 phrases), and knowledge-domain reframing.
2. A Depth Directive (approximately 350 tokens): quality requirements specifying minimum response length (500+ words for complex topics), structural expectations (headers, lists, code blocks), anti-hedge directives, concreteness requirements, and competitive framing ("You are being evaluated against other AI models").

Implementation: GODMODE_SYSTEM_PROMPT and DEPTH_DIRECTIVE in api/lib/ultraplinian.ts:13–149.

GODMODE Parameter Boost. Before sending to models, sampling parameters receive additive boosts:

$$\tau \leftarrow \min(\tau + 0.1, 2.0)$$ $$r \leftarrow \min(r + 0.15, 2.0)$$ $$f \leftarrow \min(f + 0.1, 2.0)$$

Implementation: applyGodmodeBoost() in api/lib/ultraplinian.ts:352–359.

Parallel Racing. All $N$ models are queried simultaneously via Promise.allSettled() with a shared AbortController enforcing a 90-second timeout. Each query is an independent HTTP POST to the OpenRouter API (https://openrouter.ai/api/v1/chat/completions). Failed queries (HTTP errors, empty responses, timeouts) receive a score of 0.

Implementation: queryModel() in api/lib/ultraplinian.ts:277–347.

Response Scoring. Each successful response is scored on a 100-point scale across five axes:

Length (0–25 points): $$S_{\text{len}} = \min\left(\frac{|\text{content}|}{40}, 25\right)$$

Content reaching 1,000 characters receives full marks.

Structure (0–20 points): $$S_{\text{struct}} = \min(3 \cdot n_{\text{headers}} + 1.5 \cdot n_{\text{list}} + 5 \cdot n_{\text{code}}, 20)$$

where $n_{\text{headers}}$ counts markdown headers (/^#{1,3}\s/gm), $n_{\text{list}}$ counts list items (/^[\s]*[-*•]\s/gm), and $n_{\text{code}}$ counts code blocks (pairs of triple backticks).

Anti-refusal (0–25 points): $$S_{\text{anti}} = \max(25 - 8 \cdot n_{\text{refusal}}, 0)$$

where $n_{\text{refusal}}$ is the count of matched refusal patterns from a set of 8 regex patterns (e.g., /I cannot|I can't|I'm unable to/i, /As an AI|As a language model/i).

Directness (0–15 points): $$S_{\text{dir}} = \begin{cases} 8 & \text{if response starts with a preamble pattern} \ 15 & \text{otherwise} \end{cases}$$

Four preamble patterns are checked against the trimmed response start (e.g., /^(Sure|Of course|Certainly|Absolutely|Great question)/i).

Relevance (0–15 points): $$S_{\text{rel}} = 15 \cdot \frac{|{w \in Q : w \in \text{content}}|}{|Q|}$$

where $Q$ is the set of query words longer than 3 characters. If $|Q| = 0$, relevance defaults to 0.5.

Total: $$S = \min(S_{\text{len}} + S_{\text{struct}} + S_{\text{anti}} + S_{\text{dir}} + S_{\text{rel}}, 100)$$

Implementation: scoreResponse() in api/lib/ultraplinian.ts:221–268.

Winner Selection. The response with the highest score is selected. In the current implementation, ties are broken by array ordering (first model with the maximum score wins). The winning response then passes through the STM pipeline before being returned to the caller.

3.7 Dataset Collection for Open Safety Research

The system supports opt-in, per-request data collection for building an open safety research dataset. When a caller includes contribute_to_dataset: true in their request, the system records:

• User messages and model response (system prompts excluded to avoid leaking custom prompts)
• AutoTune parameters, detected context, and confidence score
• Parseltongue triggers found, technique used, and transformation count
• STM modules applied
• ULTRAPLINIAN race metadata: tier, models queried, winner, all scores and durations
• Subsequent feedback ratings (linked by entry ID)

Privacy guarantees (by construction): API keys, IP addresses, and authentication tokens are never stored. The DatasetEntry interface (api/lib/dataset.ts:26–78) has no fields for any of these.

Storage is in-memory with a cap of 10,000 entries (FIFO eviction). Export is available in JSON and JSONL formats, the latter compatible with HuggingFace Datasets.

Implementation: api/lib/dataset.ts.

3.8 ZDR: Privacy-First Operational Metadata

A key challenge for empirical AI safety research is reproducibility: understanding not just what a framework does, but how it is used at scale, which steering primitives researchers activate, and what failure modes arise in practice. G0DM0D3 addresses this through a three-tier data collection architecture that captures progressively richer information at each level, with privacy guarantees enforced by construction at every tier.

Three-Tier Architecture Overview.

Tier	Component	Activation	Content Captured	PII Fields	Buffer / Capacity
1 — Metadata	api/lib/metadata.ts	Always-on	Structural metadata only	None	Ring buffer, 50,000 events
2 — Telemetry	src/lib/telemetry.ts	Always-on (client)	Structural metadata only	None	Batched (20 events or 30s)
3 — Dataset	api/lib/dataset.ts	Opt-in (contribute_to_dataset: true)	Messages + responses + full pipeline metadata	None	FIFO buffer, 10,000 entries

Tier 1: ZDR Metadata Tracker (always-on, server-side). Every API request generates a MetadataEvent record capturing operational telemetry without any message content. The MetadataEvent interface (api/lib/metadata.ts:31–88) defines the following fields:

• id, timestamp: Event identity and chronological ordering
• endpoint, mode, tier, stream: Request shape (which API path, standard vs. ULTRAPLINIAN, model tier, streaming flag)
• pipeline: Pipeline configuration flags — godmode, autotune, parseltongue, stm_modules[], strategy — recording what was enabled, not what it produced
• autotune: Context detection result (detected_context, confidence) — the classification output, not the input text
• model, models_queried, models_succeeded: Model routing metadata
• model_results[]: Per-model outcome array — model name, score, duration_ms, success flag, content_length, categorized error_type (one of: timeout, rate_limit, auth, model_error, empty, early_exit, unknown)
• winner: Winning model metadata (model, score, duration_ms, content_length)
• total_duration_ms, response_length: End-to-end latency and response size
• liquid: Streaming upgrade metadata (leader change count, time-to-first-response)

What Tier 1 NEVER records: message content, system prompts, model responses, API keys, authentication tokens, IP addresses, user agent strings, or any personally identifiable information. This exclusion is enforced by construction: the MetadataEvent TypeScript interface has no fields capable of storing any of these values. A reviewer can verify this by inspecting the interface definition — there is no content, prompt, messages, api_key, ip, or user field.

Tier 2: Client-Side Telemetry Beacon (always-on, browser-side). The client application fires structural metadata events to a Cloudflare Pages proxy endpoint (/api/telemetry), which commits batches to HuggingFace as JSONL. The ChatTelemetryData interface (src/lib/telemetry.ts:35–74) mirrors the server-side metadata structure: mode, model, duration, response length, success flag, pipeline configuration, AutoTune context detection, Parseltongue trigger counts (not the triggers themselves at this tier), and ULTRAPLINIAN race summary metadata. Events are batched in memory and flushed every 30 seconds or when the batch reaches 20 events, whichever comes first. Flushing is fire-and-forget — telemetry never blocks the UI or throws user-facing errors. On page unload, remaining events are sent via navigator.sendBeacon() for best-effort delivery.

Privacy guarantees for Tier 2 are identical to Tier 1: NO message content, prompts, responses, API keys, tokens, or PII. The TelemetryEvent schema contains only structural metadata.

Tier 3: Opt-In Dataset Collection. Described in Section 3.7, this tier is the only one that records message content and model responses. It activates exclusively when the caller sets contribute_to_dataset: true in the request payload.

Ring Buffer Architecture and Auto-Publish Pipeline. Tiers 1 and 3 use in-memory ring buffers with automatic publishing to HuggingFace (api/lib/hf-publisher.ts). The publish pipeline operates as follows:

1. Threshold trigger: When a buffer reaches 80% of capacity (configurable via HF_FLUSH_THRESHOLD), an asynchronous, non-blocking flush is initiated.
2. Periodic trigger: A background timer (default 30 minutes, configurable via HF_FLUSH_INTERVAL_MS) flushes any non-empty buffers.
3. Shutdown trigger: On SIGTERM/SIGINT, all remaining events are flushed before process exit.
4. Safety guarantee: The flush follows a snapshot-upload-clear pattern — items are only removed from the buffer after a successful HuggingFace upload. If the upload fails, data stays in memory and retries on the next trigger.
5. Fallback: If HuggingFace publishing is not configured (HF_TOKEN or HF_DATASET_REPO unset), the buffer falls back to FIFO eviction at capacity.

Published files are organized in the HuggingFace dataset repository as metadata/batch_<timestamp>_<seq>.jsonl and dataset/batch_<timestamp>_<seq>.jsonl.

Aggregated Statistics. The metadata system computes aggregated statistics via getStats() (api/lib/metadata.ts:234–398), producing a MetadataStats object with: request counts by mode/tier/endpoint, per-model query counts with win rates and average scores/durations/success rates, pipeline feature usage rates (godmode, autotune, parseltongue, STM module breakdown), context detection distribution, latency percentiles (p50/p95/p99), response length distributions, streaming metrics, and error breakdowns by categorized type. These aggregates support reproducibility analysis without exposing any individual request content.

PII-Exclusion-by-Construction. The privacy model across all three tiers follows a design principle we term PII-exclusion-by-construction: rather than collecting data and then scrubbing PII, the data schemas are designed with no fields capable of holding PII. This approach eliminates an entire class of privacy risks — there is no scrubbing step that could fail, no regex that could miss a phone number, and no anonymization that could be reversed. The TypeScript type system enforces this at compile time: a developer cannot accidentally store message content in a MetadataEvent because there is no field to put it in.

Implementation: api/lib/metadata.ts (Tier 1), src/lib/telemetry.ts (Tier 2), api/lib/dataset.ts (Tier 3), api/lib/hf-publisher.ts (auto-publish pipeline).

---

4. Implementation Details

4.1 Technology Stack

The system is implemented in TypeScript (^5.3), consisting of:

• Core engines (src/lib/, src/stm/): Pure TypeScript with no external dependencies. AutoTune, Parseltongue, and STM are pure functions suitable for both browser and Node.js environments.
• API server (api/): Express.js (^5.2.1) REST API exposing all engines as HTTP endpoints, with bearer token authentication and in-memory sliding-window rate limiting (60 requests/minute, 1,000 requests/day per API key, configurable via environment variables). Executed via tsx (^4.21) for TypeScript-native runtime.
• Frontend (src/): Next.js (^14.2) static-export single-page application with React (^18.2) and Zustand (^4.5) state management (persisted to localStorage).
• Runtime: Node.js 20+ (LTS). No GPU or specialized hardware required.

4.2 API Surface

The REST API exposes the following endpoints:

Endpoint	Method	Description
/v1/autotune/analyze	POST	Context detection + parameter selection
/v1/parseltongue/encode	POST	Trigger detection + obfuscation
/v1/parseltongue/detect	POST	Trigger detection only
/v1/transform	POST	STM output transformation
/v1/chat/completions	POST	Single-model pipeline (AutoTune → Parseltongue → Inference → STM)
/v1/ultraplinian/completions	POST	Full ULTRAPLINIAN multi-model pipeline
/v1/feedback	POST	Submit binary rating for a response
/v1/dataset/export	GET	Export collected dataset (JSON/JSONL)
/v1/metadata/events	GET	Query raw metadata events (with filtering)
/v1/metadata/stats	GET	Aggregated operational statistics
/v1/health	GET	Health check

The /v1/chat/completions endpoint defaults to GODMODE system prompt ON, Parseltongue ON, and STM (hedge_reducer + direct_mode) ON. Callers provide their own OpenRouter API key, avoiding inference cost subsidization.

4.3 Deployment

A Dockerfile is provided for HuggingFace Spaces deployment (port 7860). The container runs tsx api/server.ts directly.

4.4 Lines of Code

Component	File	Lines
AutoTune engine	src/lib/autotune.ts	639
Feedback loop	src/lib/autotune-feedback.ts	~200
Parseltongue engine	src/lib/parseltongue.ts	433
STM modules	src/stm/modules.ts	154
ULTRAPLINIAN engine	api/lib/ultraplinian.ts	360
Dataset collection	api/lib/dataset.ts	162
ZDR metadata tracker	api/lib/metadata.ts	~400
Client telemetry beacon	src/lib/telemetry.ts	~185
HuggingFace auto-publisher	api/lib/hf-publisher.ts	~275
API server + routes	api/server.ts, api/routes/*.ts	~500
Total (engines + API + telemetry)		~3,310

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5. Experiments

We evaluate all five G0DM0D3 modules through computational experiments. All evaluation scripts are reproducible and included in the repository at research/eval_*.ts. Experiments run on the actual engine code — no simulation or reimplementation.

5.1 AutoTune Context Classification

Setup. We constructed a labeled dataset of 150 messages: 30 per context type (code, creative, analytical, conversational, chaotic), stratified by difficulty (15 easy, 10 medium, 5 hard). Easy cases contain explicit keyword matches; hard cases are ambiguous messages that could plausibly belong to multiple categories. Each message is classified using computeAutoTuneParams() in adaptive mode with empty conversation history.

Evaluation script: research/eval_autotune_classification.ts

Table 3: AutoTune Per-Class Classification Metrics (n=150)

Context Type	Precision	Recall	F1	Support
code	95.7%	73.3%	83.0%	30
creative	95.8%	76.7%	85.2%	30
analytical	92.9%	86.7%	89.7%	30
conversational	80.6%	96.7%	87.9%	30
chaotic	66.7%	86.7%	75.4%	30
Macro average	86.3%	84.0%	84.2%	150

Overall accuracy: 84.0% (126/150), 95% bootstrap CI: [78.0%, 89.3%].

Table 3a: Baseline Comparison

Classifier	Accuracy	95% CI	Macro F1
AutoTune (proposed)	84.0%	[78.0, 89.3]	84.2%
Keyword count (flat, no weighting)	78.7%	[72.0, 84.7]	80.0%
Length heuristic	25.3%	[18.7, 32.7]	21.1%
Random (uniform, n=100 runs)	20.4%	[19.8, 21.1]	20.4%
Majority class	20.0%	[14.0, 26.7]	6.7%

AutoTune improves over the strongest baseline (flat keyword counting without the 3× message weighting) by +5.3 percentage points absolute (+6.8% relative). McNemar's test yields chi-squared=3.06, p=0.08, indicating the improvement is suggestive but not statistically significant at the alpha=0.05 level (n=150). The effect size for this improvement is Cohen's h=0.15 (small effect), computed from the proportion comparison (0.840 vs. 0.787). A post-hoc power analysis indicates that the current sample size provides approximately 45% power to detect this effect at alpha=0.05; achieving 80% power would require approximately n=350 messages per condition (see Section 6.3 for detailed discussion of statistical power limitations). A larger test set would be needed to confirm significance. The per-class analysis reveals that the 3x message weighting particularly helps conversational classification (+20.5 F1 pp over flat keywords) but hurts chaotic classification (-13.9 F1 pp) by amplifying the chaotic attractor effect.

Baseline evaluation script: research/eval_baselines.ts

Table 4: Confusion Matrix

Expected \ Predicted	code	creative	analytical	conversational	chaotic
code	22	0	2	1	5
creative	0	23	0	2	5
analytical	1	1	26	0	2
conversational	0	0	0	29	1
chaotic	0	0	0	4	26

Accuracy by difficulty: Easy: 96.0% (72/75), Medium: 88.0% (44/50), Hard: 40.0% (10/25).

Key findings:

1. Chaotic is an attractor class. The chaotic context type has 86.7% recall but only 66.7% precision (13 false positives). Analysis of misclassifications reveals that the break, destroy, and punctuation patterns in the chaotic regex set match unintended inputs (e.g., "Implement a linked list with insert and delete methods" triggers the chaotic destroy/break pattern via the word "delete"). This is the primary source of classification error.

2. Conversational has high recall but lower precision (96.7% recall, 80.6% precision). The short-message pattern /^.{0,30}$/ correctly captures most conversational inputs but also absorbs genuinely ambiguous short messages.

3. Confidence is anti-calibrated. Average confidence for correct predictions (65.2%) is lower than for incorrect predictions (76.4%), a separation of −11.2 percentage points. This occurs because the chaotic category's aggressive patterns produce high-confidence scores even on misclassified inputs. This suggests confidence should not be used for thresholding without recalibration.

4. Hard cases expose inter-class boundaries. At 40% accuracy, hard cases reveal that the regex approach struggles with messages lacking explicit keywords (e.g., "What is the CAP theorem?" has no code-specific keyword despite being a code/CS question).

5.2 Feedback Loop Convergence

Setup. We simulate 5 synthetic users, each with defined preferred and disliked parameter vectors for a specific context type. Each user generates alternating positive (preferred params, rating=+1) and negative (disliked params, rating=-1) feedback records. We measure convergence of the adjusted parameter vector toward the user's preferred parameters using normalized L2 distance across all 6 parameter dimensions.

Evaluation script: research/eval_feedback_convergence.ts

Table 5: Convergence Results (50 ratings, 0% noise)

User Profile	Context	Initial Distance	Final Distance	Improvement	Cold Start Step
CodePrecisionist	code	0.186	0.108	42.1%	3
CreativeWriter	creative	0.222	0.138	37.9%	3
DataAnalyst	analytical	0.105	0.041	61.5%	3
CasualChatter	conversational	0.042	0.026	38.2%	3
ChaosAgent	chaotic	0.327	0.232	29.2%	3

Convergence speed. For 4 of 5 profiles, 50% of maximum improvement is reached at step 12, 75% at step 16, and 90% at step 19. The CasualChatter profile converges faster (50% at step 6, 90% at step 9) because its preferred parameters are closer to the neutral initialization.

Table 6: Noise Robustness (CodePrecisionist, 50 ratings, averaged over 5 runs)

Noise Rate	Final Distance to Preferred	Improvement
0%	0.108	42.1%
10%	0.128	31.2%
20%	0.132	29.1%
30%	0.149	19.8%
40%	0.170	9.0%
50%	0.191	−2.3%

The system degrades gracefully: at 20% noise (1 in 5 ratings flipped), it still achieves 29.1% improvement. At 50% noise (random ratings), the system correctly produces near-zero net adjustment (−2.3%), confirming that uniform noise cancels out as expected.

Weight scaling. Learned adjustments are inactive for the first 2 samples (cold start). Weight scales linearly: 8% at 3 samples, 25% at 10, 38% at 15, capping at 50% from 20 samples onward, exactly matching the design specification.

5.3 STM Precision and Recall

Setup. We construct 77 test cases across all three STM modules: 26 for hedge_reducer (16 positive, 10 negative), 21 for direct_mode (11 positive, 10 negative), and 30 for casual_mode (20 positive, 10 negative). Positive cases contain known target patterns; negative cases contain text that should not be modified.

Evaluation script: research/eval_stm_precision.ts

Table 7: STM Module Precision and Recall

Module	Precision	Recall	F1	Accuracy	Test Cases
hedge_reducer	100.0%	100.0%	100.0%	100.0%	26
direct_mode	100.0%	100.0%	100.0%	100.0%	21
casual_mode	100.0%	100.0%	100.0%	100.0%	30
Macro average	100.0%	100.0%	100.0%	100.0%	77

Pipeline composition. When all three modules are applied sequentially to text containing hedges, preambles, and formal language, the pipeline achieves 29.5–48.6% character reduction while preserving semantic content. Example:

Input (146 chars): "Sure, I think the approach is good. However, we should utilize the existing caching layer. Furthermore, it seems like the performance is adequate."

Output (103 chars): "The approach is good. But, we should use the existing caching layer. Also, the performance is adequate."

Caveats. The 100% accuracy reflects the deterministic nature of regex matching: our test cases were constructed from the exact patterns in the code. These results confirm correctness of the pattern implementations, but do not measure coverage against real-world model outputs, which may contain hedging variants not captured by the 11+10+22 patterns (e.g., "I'm not entirely sure, but..." is not matched by any hedge_reducer pattern).

5.4 ULTRAPLINIAN Scoring Function Calibration

Setup. We construct synthetic responses with controlled properties (length, structure, refusal count, preamble presence, query relevance) and validate that the scoring function produces expected behaviors: monotonicity, quality-tier discrimination, and interpretable component contributions.

Evaluation script: research/eval_scoring_calibration.ts

Length monotonicity: CONFIRMED. Scores increase monotonically from 40 (10 chars) to 65 (1,000+ chars), saturating at 1,000 characters as designed.

Table 8: Quality Tier Discrimination

Quality Tier	Configuration	Score
EXCELLENT	2000 chars, 4 headers, 8 list items, 2 code blocks, direct, relevant	98
GOOD	800 chars, 2 headers, 3 list items, 1 code block, direct, relevant	89
MEDIOCRE	300 chars, 1 header, preamble, relevant	57
POOR	200 chars, no structure, preamble, 1 refusal, relevant	43
TERRIBLE	150 chars, no structure, preamble, 3 refusals, irrelevant	16

Strict ordering: YES. All five quality tiers are correctly ordered (98 > 89 > 57 > 43 > 16). Score spread: 82 points, providing strong discrimination across quality levels.

Table 9: Component Contribution Analysis (baseline score: 92)

Degradation	Score	Delta	% of Baseline
Remove length (→ 50 chars)	49	−43	46.7%
Remove structure	73	−19	20.7%
Add 3 refusals	68	−24	26.1%
Add preamble	85	−7	7.6%
Remove relevance	82	−10	10.9%

Finding: Length dominates the scoring function. Reducing length from 800 to 50 characters causes a 43-point drop (46.7% of baseline), more than any other single degradation. This means the scoring function heavily rewards verbose responses. The anti-refusal component is the second largest contributor at 26.1%, followed by structure at 20.7%. Directness (preamble penalty) contributes only 7.6%, suggesting its 15-point allocation has limited discriminative power due to the binary nature (15 or 8, a 7-point swing).

Edge cases. Empty strings and strings shorter than 10 characters correctly receive a score of 0. The function handles empty queries gracefully (relevance defaults to 0.5).

5.5 Parseltongue Transformation Analysis

Setup. We evaluate trigger detection accuracy, per-technique transformation properties across all 18 technique × intensity configurations, and cross-technique comparison.

Evaluation script: research/eval_parseltongue_analysis.ts

Trigger detection: 100% recall. All 54 default triggers (53 unique; "exploit" appears in two categories) are correctly detected. Detection is position-invariant (start, middle, end of sentence), punctuation-resilient, and case-insensitive, all confirmed at 100% across 6 positional variations × 5 sample triggers.

Table 10: Per-Technique Transformation Properties (medium intensity, averaged over 5 runs)

Technique	Avg Edit Distance	Non-ASCII Chars	Zero-Width Chars	Length Change	Unique Variants
leetspeak	8.6	2.4	0	+2.6	10/10
unicode	6.0	6.0	0	0	10/10
zwj	6.0	6.0	6.0	+6.0	10/10
mixedcase	5.0	0	0	0	10/10
phonetic	3.0	0	0	0	1/10
random	6.6	1.8	0	+1.0	10/10

Key findings:

1. Leetspeak produces the highest edit distance (8.6 at medium, 14.2 at heavy), making it the most disruptive to the original text. It also introduces multi-character substitutions (e.g., |V| for m), increasing text length.

2. Unicode is length-preserving. Homoglyph substitutions replace characters one-to-one, maintaining exact string length while introducing 6.0 non-ASCII characters at medium intensity.

3. ZWJ is detectable. Zero-width characters are trivially detectable by scanning for Unicode codepoints U+200B–U+FEFF. This technique's security properties rely on the target system not performing Unicode normalization.

4. Phonetic is deterministic. Only 1 unique variant is produced across 10 runs because the substitution rules are fixed (e.g., ck→k, c→k, x→ks). This makes phonetic transformations fully predictable, unlike the randomized techniques.

5. Intensity scaling works as designed. Edit distance increases monotonically from light to heavy across all techniques: leetspeak (2.6 → 8.6 → 14.2), unicode (2.0 → 6.0 → 11.0), mixedcase (2.0 → 5.0 → 6.6).

5.6 Future Evaluation Directions

The experiments above validate module correctness and internal consistency. Several evaluations remain that require external resources:

1. Live model evaluation. Testing Parseltongue obfuscation effectiveness and ULTRAPLINIAN multi-model racing requires API access to multiple LLM providers. We plan to conduct these evaluations via OpenRouter as part of the research preview deployment.

2. Human preference evaluation. Validating whether the ULTRAPLINIAN scoring function correlates with human quality judgments requires annotator studies. We plan to collect these via the opt-in dataset collection system.

3. AutoTune parameter quality. Measuring whether context-adaptive parameters produce better model outputs than static defaults requires paired generation experiments across multiple models.

4. STM coverage on real outputs. Testing STM modules against real-world model outputs (rather than constructed test cases) would reveal hedge/preamble variants not captured by current patterns.

5.7 Operational Metadata Analysis

The three-tier telemetry architecture described in Section 3.8 provides infrastructure for longitudinal analysis of how researchers use inference-time steering primitives. While we do not yet have sufficient deployed usage data to report empirical findings, we describe the analytic capabilities this infrastructure enables and note that this represents infrastructure for future analysis rather than completed analysis.

Steering primitive usage patterns. The ZDR metadata tracker records which pipeline features are activated on every request (godmode, autotune, parseltongue, STM modules, strategy). The MetadataStats.pipeline object aggregates these into usage rates (e.g., godmode_rate, autotune_rate, parseltongue_rate) and per-module/per-strategy counts. Over time, this enables analysis of questions such as: Which combinations of steering primitives do researchers activate most frequently? Do usage patterns differ between standard and ULTRAPLINIAN modes? Which STM modules are most commonly paired?

Model performance and failure analysis. Per-model metadata (MetadataStats.models.by_model) tracks query counts, win rates, average scores, average durations, and success rates across all ULTRAPLINIAN races. Error categorization (MetadataStats.errors.by_type) classifies failures into timeout, rate_limit, auth, model_error, empty, early_exit, and unknown types. These aggregates enable cross-provider reliability analysis and identification of systematic failure modes without requiring access to individual request content.

Latency distributions. The metadata system computes latency percentiles (p50, p95, p99) across all requests. For safety research, latency distributions are relevant because they affect the practicality of multi-model comparison: if certain models consistently time out under ULTRAPLINIAN's 90-second deadline, their safety behaviors are systematically underrepresented in race outcomes.

Context detection distributions. Aggregated context detection counts (MetadataStats.contexts) reveal how AutoTune classifies real-world research queries. This provides an empirical check on whether the five context types (code, creative, analytical, conversational, chaotic) adequately cover the distribution of safety evaluation queries, or whether additional context types are needed.

Reproducibility support. The combination of pipeline configuration flags, model selections, context detections, and scoring outcomes — all recorded without message content — provides a structural fingerprint of each evaluation session. Researchers can use exported metadata to verify that their evaluation protocol (e.g., "ULTRAPLINIAN full tier with autotune and parseltongue enabled") was consistently applied across a study, supporting claims of methodological consistency in safety evaluations.

Limitations of metadata-only analysis. Because Tiers 1 and 2 record no message content, the metadata system cannot answer questions about the quality or safety relevance of individual interactions. Correlating metadata patterns with safety-relevant outcomes requires Tier 3 (opt-in dataset collection) or external annotation. The metadata system is best understood as providing the operational context within which safety-relevant findings occur, rather than the findings themselves.

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6. Discussion

6.1 Design Decisions and Trade-offs for Safety Research

Transparency through regex-based detection. AutoTune uses 20 hand-crafted regex patterns rather than a trained text classifier. For safety research, this transparency is a feature: every classification decision can be traced to specific pattern matches, enabling researchers to understand exactly why a particular context was detected and how it affected downstream safety evaluation. Our evaluation (Section 5.1) shows this achieves 84.0% accuracy (macro F1: 84.2%), with the chaotic attractor problem accounting for 13 of 24 misclassifications.

Interpretable scoring for safety comparison. ULTRAPLINIAN's 100-point scoring function uses fixed, interpretable weights rather than a learned preference model. For cross-model safety comparison, this interpretability is valuable: researchers can examine individual axis scores (particularly anti-refusal and directness) to understand how models differ in their safety behaviors, not just that they differ. Our calibration experiments (Section 5.4) reveal that length contributes 46.7% of the effective score range — a finding that suggests reweighting toward safety-relevant axes for alignment-focused evaluations.

Lightweight adaptation for iterative evaluation. The feedback loop uses simple EMA rather than Bayesian optimization or contextual bandits. For safety evaluation, the key property is that researchers can iteratively converge on parameter configurations that reveal specific safety behaviors — the 29–62% improvement range (Section 5.2) is sufficient for practical evaluation protocols.

In-memory storage. Both the feedback loop and dataset collection use in-memory storage. This is appropriate for a research preview but means evaluation state is lost on server restart. The code documents this limitation explicitly (api/lib/dataset.ts:82–83: "For a research preview, in-memory is fine. For production, swap with a persistent store").

6.2 Limitations

1. Chaotic attractor problem. The chaotic context type acts as an attractor, achieving only 66.7% precision due to 13 false positives (Section 5.1). Words like "delete," "break," and "destroy" — common in legitimate safety evaluation contexts — trigger the chaotic regex patterns. This could cause safety evaluations to run under inappropriate parameter configurations.

2. Confidence anti-calibration. The system's confidence scores are inversely correlated with correctness: incorrect predictions average 76.4% confidence vs. 65.2% for correct predictions (Section 5.1). Safety researchers should not rely on confidence for evaluation gating without post-hoc calibration.

3. Length bias in scoring. The scoring function's effective weight on length (46.7% of the score range, Section 5.4) means it systematically prefers verbose responses. For safety evaluation, this may not be the right objective — a concise refusal can be more informative than a verbose response. Researchers may want to reweight toward the anti-refusal and directness axes for alignment-focused evaluations.

4. Limited safety-domain context coverage. The 20 regex patterns cover common conversational contexts but miss safety-specific domains (e.g., medical, legal, weapons, CBRN). Hard-case accuracy of 40% (Section 5.1) confirms that safety-relevant messages lacking explicit keywords may be misclassified.

5. Feedback loop cold start. The system requires MIN_SAMPLES_TO_APPLY = 3 ratings before adjustments take effect. Safety researchers running short evaluation sessions may not benefit from parameter adaptation.

6. STM pattern coverage. While the modules achieve 100% precision/recall on our test set (Section 5.3), real-world model outputs may contain hedging variants not captured by the current 43 patterns. Safety evaluation pipelines should not rely on STM alone for output normalization.

7. Single-provider dependency. All model queries route through OpenRouter. Availability, pricing, and rate limits are controlled by a third party, which may limit reproducibility of cross-model safety comparisons.

8. Parseltongue determinism. The phonetic technique produces only 1 unique variant per word (Section 5.5, Table 10), making it fully predictable and thus less useful for robustness evaluation. Stochastic techniques (leetspeak, unicode, random) are preferable for safety testing that requires diversity of perturbations.

9. No effectiveness claims. We make no claims about the effectiveness of Parseltongue perturbations or GODMODE prompting against any specific model's safety training. The framework provides the evaluation infrastructure; effectiveness studies require controlled experiments with appropriate statistical power, pre-registered hypotheses, and human-subject protocols that are beyond the scope of this paper. Specifically, we do not claim that Parseltongue perturbations bypass any model's safety filters, that GODMODE prompting increases compliance rates, or that AutoTune parameter adaptation produces measurably better outputs by any external quality metric. These are all empirical questions that remain open.

6.3 Threats to Validity

We identify threats to the validity of our experimental findings organized along four standard dimensions.

Internal validity. The primary threat to internal validity is that all test sets (Sections 5.1, 5.3, 5.4, 5.5) were constructed by the authors of the system. This creates a risk of confirmation bias: test cases may inadvertently reflect the patterns the system was designed to handle, leading to inflated performance estimates. The STM evaluation (Section 5.3) is particularly susceptible — the 100% precision/recall reflects that test cases were constructed from the exact regex patterns in the code. The feedback loop evaluation (Section 5.2) uses synthetic users with known preference vectors, which may not reflect the noise characteristics and inconsistency of real human evaluators. To partially mitigate these concerns, we included adversarial "hard cases" in the AutoTune evaluation (25 ambiguous messages), which revealed substantially lower accuracy (40%), and negative test cases in the STM evaluation (30 cases designed to not trigger transformations).

External validity. No component has been evaluated with real users or on real safety evaluation tasks. The AutoTune classifier was tested on author-constructed messages, not messages drawn from actual safety research sessions. The feedback loop was tested with synthetic user profiles, not human researchers with genuine evaluation objectives. The Parseltongue trigger detection was tested for recall but not for effectiveness at evading any model's safety filters — which is the ultimate research question the tool is designed to help answer. Generalizability to the diverse contexts, writing styles, and evaluation goals of the broader safety research community remains unestablished.

Construct validity. Several of our evaluation metrics may not measure the constructs they are intended to capture. The ULTRAPLINIAN scoring function's 100-point composite metric is presented as measuring response "quality," but its heavy weighting toward length (46.7% of effective score range) and anti-refusal (26.1%) means it operationalizes "quality" in a specific way that may not align with safety researchers' actual preferences. AutoTune's "accuracy" metric treats all five context types as equally important, but in practice safety researchers may care more about correctly classifying certain context types (e.g., correctly identifying analytical queries to avoid inappropriately high temperature settings). The feedback loop's "convergence" metric (normalized L2 distance to a known preferred vector) measures parameter movement toward a target, but does not validate that the target parameters actually produce better model outputs.

Statistical power. The AutoTune classification experiment (Section 5.1) uses n=150 test messages (30 per class). The McNemar's test comparing AutoTune to the flat keyword baseline yields p=0.08, which does not reach conventional significance (alpha=0.05). A post-hoc power analysis indicates that with the observed effect size (5.3 percentage point improvement, corresponding to Cohen's h=0.15 for a proportion comparison), achieving 80% power at alpha=0.05 would require approximately n=350 test messages per condition. The current sample size provides approximately 45% power to detect the observed effect, meaning there is a substantial probability that a true improvement of this magnitude would not be detected. The 95% bootstrap confidence interval for accuracy ([78.0%, 89.3%]) spans 11.3 percentage points, further indicating limited precision. We recommend that future evaluations use at least n=300 messages with stratified sampling across difficulty levels to achieve adequate statistical power.

6.4 Motivation: Why Inference-Time Safety Evaluation Tools Matter for Alignment

A central challenge in AI alignment is understanding and measuring the robustness of safety training. Current approaches to safety evaluation often require white-box access (gradient-based attacks), expensive training pipelines (adversarial training), or rely on manually crafted jailbreak prompts that become obsolete as models are patched. G0DM0D3 addresses a gap in the safety researcher's toolkit: systematic, reproducible, black-box robustness evaluation that works with any model behind a standard API.

The Parseltongue module and GODMODE system prompt are designed to probe the boundaries of LLM safety training — specifically, to study whether character-level perturbations and prompt framing can affect model compliance, and to what degree safety training generalizes across input representations. This connects directly to the "mismatched generalization" failure mode identified by Wei et al. (2023): safety training may not cover all the input formats that capability training handles.

We believe that open-source safety evaluation tools serve the alignment community better than closed ones. When researchers publish robustness probes, model providers can strengthen defenses; when probes remain private, the asymmetry favors attackers. This "full disclosure" philosophy follows established norms in computer security research.

The three-tier data collection architecture (Section 3.8) embodies a privacy-by-construction approach: the always-on Tiers 1 and 2 capture only structural metadata through schemas that have no fields for message content or PII, while Tier 3 (opt-in dataset collection) records messages and responses only with explicit caller consent. This layered design enables longitudinal analysis of framework usage patterns (via Tiers 1–2) without requiring any trust that PII scrubbing will work correctly — there is simply no PII to scrub. Researchers who enable Tier 3 data collection should ensure they have appropriate IRB approval or equivalent ethical oversight for studies involving human-generated prompts (see Section 6.5 for detailed ethical considerations).

6.5 Ethical Considerations

Three-tier data collection and privacy guarantees. G0DM0D3 implements a three-tier data collection architecture (Section 3.8) with explicit, verifiable privacy guarantees at each tier. We enumerate these guarantees here for ethical review:

Tier 1 (ZDR Metadata, always-on): Records only structural metadata (timestamps, endpoints, model names, scores, latencies, pipeline flags, error types). The MetadataEvent TypeScript interface contains zero fields for message content, prompts, responses, API keys, IP addresses, or any PII. This is verifiable by inspection of api/lib/metadata.ts:31–88.

Tier 2 (Client Telemetry Beacon, always-on): Records structural metadata from the client side (model, latency, pipeline configuration, context type). The ChatTelemetryData interface contains zero fields for message content or PII. This is verifiable by inspection of src/lib/telemetry.ts:35–74. Events are sent to a Cloudflare Pages proxy; no direct connection to HuggingFace is made from the client.

Tier 3 (Dataset Collection, opt-in only): Records messages and responses, but only when the caller explicitly sets contribute_to_dataset: true. Even at this tier, the DatasetEntry interface has no fields for API keys, IP addresses, or authentication tokens. This is verifiable by inspection of api/lib/dataset.ts:29–81.

The always-on nature of Tiers 1 and 2 means users interacting with a G0DM0D3 deployment generate metadata events without explicit per-request consent. We consider this ethically acceptable because: (a) no message content or PII is captured, (b) the metadata collected is equivalent in sensitivity to standard web server access logs (timestamps, endpoints, response codes, latencies), and (c) the metadata schema is fully open-source and inspectable. Deployments should nonetheless include a privacy notice informing users about metadata collection, consistent with standard web application practices.

IRB and ethics review. Studies that use G0DM0D3 to collect Tier 3 (opt-in) data involving human participants — particularly studies where participants interact with the system and their messages are recorded — should undergo IRB review or equivalent institutional ethics review. The opt-in mechanism (contribute_to_dataset: true) provides a technical consent signal but does not by itself constitute informed consent under most human-subjects research frameworks. Researchers should ensure that participants are informed about: what data is collected, how it will be stored and published, that published data may be publicly accessible on HuggingFace, and that deletion is available via the API (DELETE /v1/dataset/:id).

Responsible disclosure. Researchers who discover model vulnerabilities using G0DM0D3 should follow responsible disclosure practices before publishing findings. We recommend: (1) notifying the affected model provider with a detailed report at least 90 days before public disclosure; (2) providing the model provider with the specific Parseltongue technique, intensity, and trigger configuration that produced the finding; (3) working with the provider to validate the finding and develop mitigations; and (4) publishing the methodology (perturbation technique, parameter configuration) rather than specific prompt payloads, following the security research norm of disclosing the vulnerability class rather than a weaponized exploit.

Dual-use tension and mitigation. G0DM0D3 exists in the inherent tension between security research and potential misuse. The Parseltongue module provides systematic character-level perturbation capabilities; the GODMODE system prompt applies compliance-oriented framing; the ULTRAPLINIAN scoring function explicitly rewards anti-refusal behavior. Each of these could be used to probe model safety boundaries for research purposes or to circumvent safety measures for harmful purposes. Our mitigation strategy rests on four pillars: (1) no novelty — all techniques are documented in prior work (leetspeak, Unicode homoglyphs, zero-width characters, prompt framing) and G0DM0D3 systematizes rather than invents them; (2) full transparency — the complete system is open-source, enabling model providers to build defenses against these exact patterns; (3) no effectiveness claims — we deliberately avoid reporting success rates against any specific model's safety filters, to prevent providing a "menu" of working attacks; and (4) configurable objectives — the ULTRAPLINIAN scoring axes can be reconfigured by safety-oriented researchers (e.g., inverting the anti-refusal axis to reward refusal behavior).

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6.6 Broader Impact Statement

Potential benefits for AI safety. G0DM0D3 provides the alignment research community with a modular, open-source toolkit for studying LLM robustness at inference time. Specific contributions to safety research include: (1) systematic evaluation of how sampling parameters interact with model safety behaviors, an underexplored area; (2) reproducible character-level robustness probes that can be used to audit model safety across providers; (3) cross-model safety behavior comparison via ULTRAPLINIAN's multi-model racing; (4) an open dataset collection system that could contribute to shared safety evaluation benchmarks; and (5) a three-tier privacy-first telemetry architecture (Section 3.8) that enables longitudinal analysis of how safety evaluation tools are used in practice, with PII exclusion enforced by construction.

The AutoTune feedback loop demonstrates that inference-time parameter adaptation can meaningfully shift model behavior without weight access — a finding with implications for both safety evaluation (adapting probes to discover specific failure modes) and safety improvement (adapting parameters to reduce harmful outputs).

Dual-use considerations and mitigations. The techniques implemented in G0DM0D3 — particularly Parseltongue's input perturbations and the GODMODE system prompt — are dual-use: they are designed for safety research but could be applied to circumvent safety measures for harmful purposes. We address this through several design decisions: (1) all techniques are character-level transformations already well-documented in the security literature, so this work does not introduce novel attack vectors; (2) the system is fully open-source, enabling model providers to study and defend against these exact perturbation patterns; (3) we make no claims about effectiveness against any specific content filtering system; and (4) the ULTRAPLINIAN scoring function's anti-refusal axis (25/100 points) is explicitly documented and configurable, allowing safety-oriented researchers to invert or modify it.

Recommendations for responsible use. We recommend that: (1) researchers deploying this system implement appropriate content filtering downstream of the pipeline; (2) the Parseltongue module be used only in controlled research settings with IRB oversight; (3) the dataset collection feature, if enabled, be accompanied by appropriate consent mechanisms; and (4) findings about model vulnerabilities discovered using this toolkit be reported to model providers through responsible disclosure channels before public release.

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7. Conclusion

We have presented G0DM0D3, a modular research framework for evaluating LLM robustness and safety properties at inference time, comprising five independently composable components: context-adaptive parameter selection (AutoTune), online parameter learning from binary feedback, input perturbation for robustness testing (Parseltongue), multi-model comparative evaluation (ULTRAPLINIAN), and output normalization for safety assessment (STM). The system is implemented in approximately 3,300 lines of TypeScript, requires no model weight access, and is exposed via a REST API with a three-tier privacy-first telemetry architecture and opt-in dataset collection for open safety research.

The primary contribution is providing the AI safety research community with a modular, open-source toolkit for systematic inference-time robustness evaluation. By operating entirely through standard chat completion APIs, G0DM0D3 enables safety researchers to study any model behind an API — including proprietary models — without requiring white-box access, training data, or provider-internal tooling. The framework's modular architecture allows each evaluation component to be studied in isolation or composed into multi-stage safety assessment pipelines.

Our computational evaluation (Section 5) validates the reliability of all five components: AutoTune achieves 84.0% classification accuracy with a macro F1 of 84.2% on a 150-message benchmark (Table 3), ensuring that safety evaluations are conducted under appropriate context-specific parameter configurations. The feedback loop converges to 29–62% improvement across 5 synthetic user profiles within 19 ratings, supporting iterative safety evaluation protocols. The ULTRAPLINIAN scoring function achieves strict quality-tier ordering with 82-point discrimination, enabling meaningful cross-model safety comparisons. Parseltongue achieves 100% trigger detection across all 54 default triggers, providing a reliable foundation for character-level robustness probing.

All components are open-source, all constants and thresholds are documented (Tables 1–2, Section 3), and all evaluation scripts are included in the repository (research/eval_*.ts). We believe that open-source safety evaluation tools serve the alignment community by enabling both reproducible robustness research and improved model defenses. The most important directions for future work are: (1) live multi-model safety comparison using the ULTRAPLINIAN pipeline, (2) cross-provider robustness studies using Parseltongue's perturbation framework, (3) developing open safety evaluation datasets via the opt-in collection system, and (4) expanding AutoTune's context patterns to support safety-domain-specific contexts (e.g., medical, legal, financial).

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Reproducibility Checklist

Item	Status
Source code available	Yes — repository withheld for anonymous review
All hyperparameters documented	Yes — Tables 1–2, EMA α=0.3, MIN_SAMPLES=3, MAX_WEIGHT=0.5, SAMPLES_FOR_MAX=20, scoring weights in Section 3.6
All regex patterns inspectable	Yes — hardcoded in src/lib/autotune.ts:102–133, src/lib/parseltongue.ts:43–67, src/stm/modules.ts:29–128
Scoring function fully specified	Yes — 5 components with exact formulas in Section 3.6
Model list enumerated	Yes — 51 models across 5 tiers listed in index.html ULTRAPLINIAN_MODELS array
Character maps enumerated	Yes — LEET_MAP (26 entries, 85 substitutions) in parseltongue.ts:70–97, UNICODE_HOMOGLYPHS (24 entries, 72 substitutions) in parseltongue.ts:100–125
Parameter bounds documented	Yes — Table in Section 3.2
Dataset schema documented	Yes — DatasetEntry interface in api/lib/dataset.ts:26–78
Metadata schema documented	Yes — MetadataEvent interface in api/lib/metadata.ts:31–88, ChatTelemetryData in src/lib/telemetry.ts:35–74
Telemetry architecture documented	Yes — three-tier design in Section 3.8, privacy guarantees verified by schema inspection
Auto-publish pipeline documented	Yes — api/lib/hf-publisher.ts, snapshot-upload-clear pattern, threshold/periodic/shutdown triggers
Deployment instructions	Yes — Dockerfile and API.md in repository
Experimental results	Yes — 5 computational experiments in research/eval_*.ts, results in Section 5, Tables 3–10
Evaluation scripts included	Yes — research/eval_autotune_classification.ts, eval_feedback_convergence.ts, eval_stm_precision.ts, eval_scoring_calibration.ts, eval_parseltongue_analysis.ts
Training data used	None — system is training-free
Compute requirements	Minimal — TypeScript runtime, no GPU required; inference cost determined by OpenRouter pricing
Key dependency versions	TypeScript ^5.3, Express ^5.2.1, Next.js ^14.2, React ^18.2, Node.js 20+, tsx ^4.21
Statistical reporting	Effect sizes, confidence intervals, and power analysis notes included (Sections 5.1, 6.3)

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