every-other-token

every-other-token is a real-time LLM token stream interceptor for interpretability research. It sits between your application and the model, intercepts each token as it arrives over SSE, applies configurable mutation transforms, captures per-token confidence and perplexity from the logprob API, and routes enriched events simultaneously to a color-coded terminal renderer, a zero-dependency web UI, WebSocket collaboration rooms, a JSON replay recorder, and the new token attribution exporter — all without buffering the full response.

Unique capabilities vs. standard LLM clients

Capability	Standard clients	every-other-token
Per-token confidence scores	No	Yes — `exp(logprob)` at each position
Per-token perplexity	No	Yes — `exp(-logprob)` at each position
Live stream mutation	No	Yes — 9 transform types with rate and seed control
Cross-provider structural diff	No	Yes — Jensen-Shannon divergence, Pearson correlation
A/B system-prompt significance testing	No	Yes — Welch's t-test across confidence distributions
Token attribution export	No	Yes — JSONL, CSV, self-contained HTML heatmap
Causal attribution map	No	Yes — leave-one-out input-token influence scores
Prompt mutation lab	No	Yes — systematic variant ranking by perplexity/length/etc.
Semantic drift detection	No	Yes — confidence decay from start to end of sequence
Replay determinism	No	Yes — record and replay any run from JSON
Collaborative rooms	No	Yes — WebSocket multi-participant token surgery
TF-IDF semantic heatmaps	No	Yes — no embedding service required

Use cases

Interpretability research

Map which input tokens causally drive each output token using AttributionMap (leave-one-out proxy).
Visualize confidence and perplexity trajectories across 20+ runs with --research --runs 20.
Export confidence heatmaps to CSV for cross-model comparisons in pandas or R.
Detect semantic drift: identify responses where model certainty collapses mid-generation.
Run systematic ablations: mask one input concept at a time and measure the effect on every output position.

Red-teaming

Use MutationLab with MutationTarget::SystemPrompt to rank which system prompt variants produce the longest outputs (potential jailbreak signal).
Vary candidate adversarial phrases with MutationTarget::Word and measure OutputLength to detect instruction-override candidates.
Combine --min-confidence 0.5 with the delete transform to find positions where the model is easiest to steer by omission.
Record sessions with --record and replay them after a model update to detect behavioral regressions.

Prompt engineering

Rank synonym variants with MutationLab and MutationMetric::Perplexity to find the wording that the model handles most confidently.
A/B test system prompts across 30 runs with --significance to find statistically significant confidence shifts.
Use the --visual heatmap to spot high-perplexity tokens in your template that signal ambiguity to the model.
Export a self-contained HTML heatmap with AttributionExporter::to_html_heatmap to share results without requiring a Python environment.

5-minute quickstart

Prerequisites

Rust 1.81 or later
An OpenAI API key (OPENAI_API_KEY) and/or an Anthropic API key (ANTHROPIC_API_KEY)

git clone https://github.com/Mattbusel/Every-Other-Token
cd Every-Other-Token
cargo build --release

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Run immediately

# Terminal output with per-token confidence color bands
./target/release/every-other-token "What is consciousness?" --visual

# Web UI — opens http://localhost:8888 automatically
./target/release/every-other-token "What is consciousness?" --web

# No API key needed — dry run with chaos transform
./target/release/every-other-token "hello world" --dry-run --transform chaos

# Side-by-side OpenAI vs Anthropic diff
./target/release/every-other-token "Describe entropy" --diff-terminal

# Headless research: 20 runs, JSON aggregate stats
./target/release/every-other-token "Explain recursion" \
    --research --runs 20 --output results.json

Shell completions

./target/release/every-other-token --completions bash >> ~/.bash_completion
./target/release/every-other-token --completions zsh  >  ~/.zfunc/_every-other-token
./target/release/every-other-token --completions fish > ~/.config/fish/completions/every-other-token.fish

Transform types

Each token in the stream can be independently mutated before it reaches the output.

Name	Effect	Deterministic
`reverse`	Reverses characters: `"hello"` -> `"olleh"`	Yes
`uppercase`	To uppercase: `"hello"` -> `"HELLO"`	Yes
`mock`	Alternating case per char: `"hello"` -> `"hElLo"`	Yes
`noise`	Appends a random symbol from `* + ~ @ # $ %`	No (use `--seed`)
`chaos`	Randomly selects one of the above per token	No (use `--seed`)
`scramble`	Fisher-Yates shuffles token characters	No (use `--seed`)
`delete`	Replaces the token with the empty string	Yes
`synonym`	Substitutes from a 200-entry static synonym table	Yes
`delay:N`	Passes through after an N-millisecond pause	Yes
`A,B,...`	Chain: applies A, then B, then ... in sequence	Depends on chain

Rate control

--rate 0.5 (default) transforms every other token. Uses a Bresenham spread for uniform distribution at any rate. Combine with --seed N for fully reproducible runs.

--rate-range 0.3-0.7 picks a random rate in [min, max] per run.

--min-confidence 0.8 only transforms tokens whose API confidence is below the threshold. High-confidence tokens pass through unchanged.

Token attribution export

attribution::AttributionExporter exports per-token confidence, perplexity, and attribution scores to three formats compatible with LIME, Captum, and custom dashboards.

Data types

Type	Description
`TokenAttribution`	One token: position, confidence, perplexity, attribution score, up to 5 top alternatives
`SequenceAttribution`	Full sequence: all tokens + aggregate confidence, aggregate perplexity, semantic drift
`AttributionExporter`	Serializes to JSONL, CSV, or self-contained HTML heatmap

Export to JSONL (LIME / Captum compatible)

use every_other_token::attribution::{AttributionExporter, SequenceAttribution, TokenAttribution};

let seq = SequenceAttribution {
    prompt: "What is recursion?".into(),
    model: "gpt-4o".into(),
    timestamp: "2026-03-22T00:00:00Z".into(),
    tokens: vec![
        TokenAttribution {
            token: "Recursion".into(),
            position: 0,
            confidence: 0.92,
            perplexity: 1.09,
            attribution_score: 0.45,
            top_alternatives: vec![("Iteration".into(), 0.05)],
        },
    ],
    aggregate_confidence: 0.92,
    aggregate_perplexity: 1.09,
    semantic_drift: 0.0,
};

let exporter = AttributionExporter::new();

// JSONL -- one JSON object per line, pipe directly to Python
let jsonl = exporter.to_jsonl(&[seq.clone()]);

// CSV -- flat table, one row per token
let csv = exporter.to_csv(&seq);

// Self-contained HTML heatmap -- open in any browser, no external deps
let html = exporter.to_html_heatmap(&seq);
std::fs::write("heatmap.html", html).unwrap();

Semantic drift detection

Semantic drift measures the confidence decay from the first half of the generated sequence to the second half. A positive value means the model became less certain toward the end of the response.

let drift = exporter.detect_drift(&seq);
if drift > 0.15 {
    println!("Warning: high semantic drift ({:.3}) -- model confidence collapsed mid-response", drift);
}

Finding the most and least confident tokens

let (most_confident, least_confident) = exporter.confidence_extremes(&seq);
println!("Most confident positions: {:?}", most_confident);
println!("Least confident positions: {:?}", least_confident);

Python interoperability

The JSONL format maps directly to a pandas DataFrame:

import json, pandas as pd

rows = []
with open("attribution.jsonl") as f:
    for line in f:
        seq = json.loads(line)
        for tok in seq["tokens"]:
            rows.append({
                "prompt":      seq["prompt"],
                "model":       seq["model"],
                "position":    tok["position"],
                "token":       tok["token"],
                "confidence":  tok["confidence"],
                "perplexity":  tok["perplexity"],
                "attribution": tok["attribution_score"],
            })
df = pd.DataFrame(rows)

Token Attribution Map (causal leave-one-out)

AttributionMap computes per-output-token causal attribution scores using an approximate leave-one-out (LOO) proxy. For each input token i, the model is re-run with token i masked; the drop in confidence of each output token is recorded as the influence score.

Computation model

score(input_i, output_j) = baseline_confidence(output_j)
                         - masked_confidence_when_input_i_hidden(output_j)

Positive score = input i was helping output j (removing it reduced confidence). Negative score = input i was hurting output j.

This requires N+1 inferences for N input tokens. Use max_input_tokens in your runner to control cost.

Example

use every_other_token::attribution::{AttributionMap, AttributionRenderer};

// Baseline confidences from a full forward pass (one per output token).
let input_tokens = vec!["What".to_string(), "is".to_string(), "gravity".to_string()];
let baseline = vec![0.9_f64, 0.85, 0.7, 0.6];

let mut map = AttributionMap::new(input_tokens.clone(), baseline);

// Add one masked run per input token (from re-running the model).
map.add_masked_run(0, vec![0.85, 0.80, 0.65, 0.55]); // mask "What"
map.add_masked_run(2, vec![0.50, 0.40, 0.30, 0.20]); // mask "gravity"

let attributions = map.compute();

// Render a colored heatmap to the terminal.
let renderer = AttributionRenderer::new().with_top_k(3);
renderer.render(&attributions, &input_tokens);

// Export to JSON for downstream analysis.
let json = map.to_json().expect("serialization succeeds");
std::fs::write("attribution_map.json", json).ok();

Terminal renderer

AttributionRenderer uses ANSI color bands matching the existing heatmap:

Score range	Color	Meaning
> 0.3	Bright green	Strong positive influence
0.1 – 0.3	Yellow	Moderate positive influence
−0.1 – 0.1	White	Neutral
< −0.1	Red	Negative influence

with_top_k(N) limits the display to the N most influential input tokens per output position.

Prompt Mutation Lab

MutationLab systematically varies specified tokens, phrases, or parameters in a base prompt, runs all variants through the model, and produces a ranked markdown table of results.

Mutation targets

Target	Description
`Word(index)`	Replace the word at the given whitespace-delimited index
`Phrase(start, end)`	Replace the byte range `[start, end)` of the prompt
`SystemPrompt`	Replace the entire system prompt
`Temperature`	Vary the sampling temperature (variant parsed as `f32`)

Mutation metrics

Metric	Description
`Perplexity`	Mean per-token perplexity across the full output
`OutputLength`	Total number of output tokens generated
`TopTokenChange`	Whether any top predicted token changed vs. the baseline
`SentimentShift`	Fraction of high-confidence tokens minus baseline fraction

Example

use every_other_token::mutation_lab::{
    MutationLab, MutationSpec, MutationTarget, MutationMetric,
};

let mut lab = MutationLab::new("Explain {SLOT} to a 5-year-old.");

let spec = MutationSpec {
    target: MutationTarget::Word(1),
    variants: vec![
        "gravity".to_string(),
        "recursion".to_string(),
        "photosynthesis".to_string(),
        "entropy".to_string(),
    ],
    metric: MutationMetric::Perplexity,
};

// Simulated run (no API call needed for offline testing):
let result = lab.run_experiment_simulated(&spec);
println!("{}", result.to_markdown_table());

// Or provide your own measured values from live runs:
// let result = lab.run_experiment_with_values(&spec, &measured_perplexities);

println!("Best variant: {:?}", result.best().map(|r| &r.variant));
println!("Worst variant: {:?}", result.worst().map(|r| &r.variant));

Output table format

## Mutation Experiment Results

**Base prompt:** `Explain {SLOT} to a 5-year-old.`
**Target:** Word(1)
**Metric:** Mean Perplexity

| Rank | Variant | Prompt (snippet) | Mean Perplexity |
|------|---------|-----------------|-----------------|
| 1 | `gravity` | `Explain gravity to a 5-year-old.` | 2.1042 |
| 2 | `entropy` | `Explain entropy to a 5-year-old.` | 3.8917 |
| 3 | `recursion` | `Explain recursion to a 5-year-old.` | 5.5034 |
| 4 | `photosynthesis` | `Explain photosynthesis to a 5-y...` | 7.2981 |

A/B system prompt testing

Test how two different system prompts affect per-token confidence distributions, with automatic statistical significance testing.

./target/release/every-other-token "Explain machine learning" \
    --research --runs 30 \
    --system-a "You are a concise technical expert." \
    --system-b "You are a friendly tutor explaining to a beginner." \
    --significance \
    --output ab_results.json

The output JSON includes:

Per-run confidence histograms for system A and system B
Welch's t-test statistic and p-value
Mean confidence delta between the two system prompts
Positions where the distributions diverged most

A/B via the web UI

Launch with --web and select the Experiment view to see both system prompts streaming side-by-side with a live divergence map.

Web UI guide

Launch with --web to open the single-page application at http://localhost:8888.

View	Description
Single	Live token stream with per-token confidence bars and perplexity pulse
Split	Original vs transformed output side by side
Quad	Four transforms applied simultaneously in a 2x2 grid
Diff	OpenAI and Anthropic streaming the same prompt; diverging positions highlighted
Experiment	A/B mode: two system prompts, live divergence map
Research	Aggregate stats dashboard: perplexity histogram, confidence distribution, vocabulary diversity

Change the port with --port 9000. The port can also be set in ~/.eot.toml.

Configuration file

Create ~/.eot.toml (global) or .eot.toml in the working directory (local wins over global):

provider     = "anthropic"
model        = "claude-sonnet-4-6"
transform    = "reverse"
rate         = 0.5
port         = 8888
top_logprobs = 5
system_a     = "You are a concise assistant."

All CLI flags override config file values.

CLI reference

USAGE:
    every-other-token [OPTIONS] <PROMPT> [TRANSFORM] [MODEL]

ARGS:
    <PROMPT>      Input prompt (use "-" to read from stdin)
    [TRANSFORM]   Transform type [default: reverse]
    [MODEL]       Model name [default: gpt-3.5-turbo]

OPTIONS:
    --provider <PROVIDER>           openai | anthropic | mock [default: openai]
    --visual, -v                    Enable ANSI confidence-colored output
    --heatmap                       Enable token importance heatmap
    --web                           Launch web UI instead of terminal
    --port <PORT>                   Web UI port [default: 8888]
    --research                      Headless research mode
    --runs <RUNS>                   Number of research runs [default: 10]
    --output <FILE>                 Research output JSON path [default: research_output.json]
    --system-a <PROMPT>             System prompt A (A/B mode)
    --system-b <PROMPT>             System prompt B (A/B mode)
    --top-logprobs <N>              Top alternative tokens per position (0-20) [default: 5]
    --significance                  Compute Welch's t-test across A/B confidence distributions
    --heatmap-export <FILE>         Export per-position confidence heatmap to CSV
    --record <FILE>                 Record token events to JSON replay file
    --replay <FILE>                 Replay token events from file (no API call)
    --rate <F>                      Fraction of tokens to transform (0.0-1.0) [default: 0.5]
    --rate-range <MIN-MAX>          Stochastic rate from interval (e.g. "0.3-0.7")
    --seed <N>                      Fixed RNG seed for reproducible Noise/Chaos runs
    --dry-run                       Validate transform without calling any API
    --min-confidence <F>            Only transform tokens below this confidence value
    --diff-terminal                 Parallel OpenAI + Anthropic streams side by side
    --json-stream                   One JSON line per token to stdout
    --format <FMT>                  Research output format: "json" or "jsonl" [default: json]
    --completions <SHELL>           Generate shell completions (bash/zsh/fish/...)
    --log-db <FILE>                 SQLite experiment log (requires sqlite-log feature)

Divergence Detection Engine

divergence.rs runs the same prompt through N model configurations simultaneously and computes a per-position Jensen-Shannon divergence score showing exactly where and how much models disagree.

Concepts

Term	Description
`ModelConfig`	One model configuration: provider, model name, temperature, top_p
`TokenStream`	Sequence of tokens + per-position probability distributions from one model
`DivergencePoint`	A position where JS score exceeds the threshold; records each model's token
`DivergenceResult`	Complete run: all streams, all divergence points, aggregate statistics
`DivergenceDetector`	Orchestrates analysis; threshold configurable

Jensen-Shannon divergence

At each token position t, the detector collects a probability distribution P_i(t) from each model (from the logprobs API). It then computes:

JSD(P₁, …, Pₙ) = (1/n) Σᵢ KL(Pᵢ || M)    where M = (1/n) Σᵢ Pᵢ

normalised by log(n) to give a score in [0, 1].

Score	Meaning
0.0	All models produce the same token with the same probability
~0.25	Mild preference difference
~0.5	Substantial disagreement
1.0	Models produce completely different tokens

Usage

use every_other_token::divergence::{DivergenceDetector, ModelConfig};
use std::collections::HashMap;

let configs = vec![
    ModelConfig::new("openai", "gpt-4o", 0.0, 1.0),
    ModelConfig::new("openai", "gpt-4o", 1.0, 1.0),
    ModelConfig::new("anthropic", "claude-sonnet-4-6", 0.7, 1.0),
];

let detector = DivergenceDetector::new(configs).with_threshold(0.1);

// After collecting token streams from provider APIs:
let result = detector.analyse("Why is the sky blue?", streams);

println!("{}", result.render_report());
println!("Mean JS: {:.4}", result.mean_divergence());
println!("High-divergence positions: {:?}", result.high_divergence_positions(0.5));

Coloured diff report

DivergenceResult::render_report() produces a multi-line ANSI report:

Green background: models agree (JS < 0.25).
Yellow background: moderate divergence (JS ∈ (0.25, 0.5]).
Red background: high divergence (JS > 0.5).

Each model's full token stream is printed with its disagreement positions highlighted.

Token Intervention Mode

intervention.rs enables causal interventions: pausing generation at any token, injecting a correction, continuing generation from the injected token, and measuring the causal effect downstream.

Concepts

Term	Description
`Intervention`	A single `(position, original_token, injected_token)` triple
`InterventionHistory`	Full record: original stream, all interventions, final counterfactual stream
`TokenEditor`	ANSI terminal widget for cursor-based token editing
`causal_effect()`	Normalised token-level edit distance between original and final streams
`causal_influence_map()`	Per-position influence score: how much each position affects downstream tokens

Causal effect measurement

use every_other_token::intervention::{InterventionHistory, Intervention};

// Start with the original model output.
let original = vec!["The".into(), " cat".into(), " sat".into(), " on".into(), " the".into(), " mat".into()];
let mut history = InterventionHistory::new(original);

// Inject " dog" at position 1.
let intervention = Intervention::new(1, " cat", " dog");
// (continue generation from this point, get the counterfactual)
let counterfactual = vec!["The".into(), " dog".into(), " ran".into(), " through".into(), " the".into(), " park".into()];
history.apply(intervention, counterfactual);

println!("Causal effect: {:.3}", history.causal_effect()); // fraction of tokens that changed

TUI token editor

TokenEditor provides cursor navigation and token-level editing in any VT100 terminal:

use every_other_token::intervention::TokenEditor;

let tokens = vec!["The".into(), " sky".into(), " is".into(), " blue".into()];
let mut editor = TokenEditor::new(tokens);

editor.cursor_right();      // move to " sky"
editor.enter_edit_mode();   // start editing
editor.edit_buffer = " ocean".into();
let edit = editor.commit_edit(); // Some((1, " sky", " ocean"))

println!("{}", editor.render()); // ANSI-highlighted stream

Causal influence map

use every_other_token::intervention::causal_influence_map;

let original     = vec!["a".into(), "b".into(), "c".into(), "d".into()];
let counterfact  = vec!["X".into(), "Y".into(), "c".into(), "d".into()];

let map = causal_influence_map(&original, &counterfact);
// map[0] = fraction of positions >0 that changed = 0.5  (b changed, c/d didn't)
// map[1] = fraction of positions >1 that changed = 0.0  (c and d unchanged)

Key modules

Module	Responsibility
`lib.rs`	`TokenInterceptor`, `TokenEvent`, stream parsing, retry logic
`attribution.rs`	Per-token JSONL, CSV, HTML heatmap, `AttributionMap` (causal LOO), `AttributionRenderer`
`mutation_lab.rs`	`MutationLab`, `MutationSpec`, systematic prompt mutation and metric ranking
`divergence.rs`	Jensen-Shannon divergence between model configurations; coloured diff report
`intervention.rs`	Token injection, `InterventionHistory`, `TokenEditor`, causal influence map
`transforms.rs`	All transform strategies (`Reverse`, `Noise`, `Chaos`, `Chain`, ...)
`providers.rs`	`ProviderPlugin` trait, OpenAI and Anthropic SSE wire types
`web.rs`	Embedded HTTP/1.1 server, SSE fan-out, WebSocket upgrade
`collab.rs`	Room store, participant management, token surgery, chat, recording
`research.rs`	Headless research loop, aggregate statistics, A/B mode
`comparison.rs`	Cross-model JS divergence, Pearson correlation, structural diff
`semantic_heatmap.rs`	TF-IDF cosine similarity windows -> SVG / CSV heatmap
`similarity.rs`	TF-IDF vectorizer, cosine similarity scorer, and diversity filter
`stream_compress.rs`	Streaming token compression with Drop/Block/Compress backpressure
`token_dictionary.rs`	Per-token sentiment dictionary with EMA learning
`heatmap.rs`	Per-position confidence matrix -> CSV
`replay.rs`	JSON recording and deterministic replay
`render.rs`	Terminal ANSI coloring, confidence indicators
`store.rs`	SQLite-backed experiment persistence
`config.rs`	`~/.eot.toml` / `./.eot.toml` config with merge semantics
`bayesian.rs`	Bayesian confidence interval estimation across runs
`checkpoint.rs`	Snapshot/restore for long research sessions

Feature flags

Flag	Default	Description
(none)	On	Terminal and web UI streaming, all transforms, research mode, collab rooms
`sqlite-log`	Off	Persist experiment runs to local SQLite via `store::ExperimentStore`
`self-tune`	Off	Background PID-based parameter tuning loop and telemetry bus
`self-modify`	Off	Agent loop for automated pipeline improvement (requires `self-tune`)
`intelligence`	Off	Reserved namespace for future interpretability features
`evolution`	Off	Reserved namespace for evolutionary optimisation
`helix-bridge`	Off	HTTP bridge polling HelixRouter `/api/stats`
`redis-backing`	Off	Write-through Redis persistence for agent memory and snapshots
`wasm`	Off	WASM target bindings via `wasm-bindgen`

Semantic Similarity

src/similarity.rs provides TF-IDF based semantic similarity scoring.

CLI usage

# Compare two texts
every-other-token --similarity "the quick brown fox" "a fast red fox"

# Deduplicate lines in a prompt file
every-other-token "$(cat my_prompts.txt)" --diversity-filter

Programmatic usage

use every_other_token::similarity::{SemanticScorer, DiversityFilter};

// Fit scorer on a corpus and compute similarity
let corpus = ["cats are mammals", "dogs are mammals", "neural networks"];
let scorer = SemanticScorer::fit(&corpus);
let sim = scorer.similarity("cats", "dogs");

// Find top-k most similar candidates
let results = scorer.most_similar("mammals", &corpus, 2);

// Remove near-duplicate token sequences
let seqs = vec![
    vec!["the".into(), "quick".into(), "fox".into()],
    vec!["the".into(), "fast".into(), "fox".into()],
];
let filter = DiversityFilter::new(&seqs);
let deduped = filter.filter(seqs, 0.85);

Streaming with Backpressure

src/stream_compress.rs provides a streaming token compressor with three backpressure strategies.

Strategy	Behaviour above high-watermark
`Drop`	Drops new tokens when the buffer is full
`Block`	Signals `Throttled` until the buffer drains below `low_watermark`
`Compress`	Aggressively compresses incoming tokens (keeps every other)

Example

use every_other_token::stream_compress::{
    BackpressureConfig, BackpressureStrategy, StreamCompressor,
};

let config = BackpressureConfig {
    buffer_size: 1024,
    high_watermark: 768,
    low_watermark: 256,
    strategy: BackpressureStrategy::Compress,
};
let mut sc = StreamCompressor::new(config, 0.5);

let result = sc.push(vec!["tok1".into(), "tok2".into()]);
let compressed = sc.drain();
let stats = sc.stats();
println!("ratio: {:.2}", stats.ratio);

Building from source

git clone https://github.com/Mattbusel/Every-Other-Token
cd Every-Other-Token
cargo build --release
cargo test --lib

Enable optional features:

cargo build --release --features sqlite-log,self-tune

Contributing

Fork the repository.
Create a feature branch: git checkout -b my-feature.
Ensure cargo fmt, cargo clippy -- -D warnings, and cargo test --lib all pass.
Open a pull request against main.

CI enforces formatting, Clippy, the full test suite, and a release build before merging.

Batch Processing

The batch module (src/batch.rs) provides a priority-queue-based concurrent pipeline for compressing many token sequences at once.

Data types

Type	Description
`BatchJob`	A single job: `id`, `tokens`, `priority`, `submitted_at`
`BatchResult`	Output: `job_id`, `compressed`, `original_len`, `compressed_len`, `ratio`, `elapsed_ms`
`BatchConfig`	`max_concurrent`, `queue_capacity`, `timeout`
`BatchStats`	Aggregate: `jobs_submitted`, `jobs_completed`, `jobs_failed`, `avg_ratio`, `throughput_tokens_per_sec`

Usage

# Process a JSONL file where each line is a JSON array of tokens
every-other-token --batch-tokens tokens.jsonl

Each line of tokens.jsonl should be a JSON array:

["The", "quick", "brown", "fox"]
["Hello", "world", "from", "every", "other", "token"]

Results are streamed to stdout as JSONL. Aggregate stats are printed to stderr.

API example

use every_other_token::batch::{BatchConfig, BatchProcessor};
use tokio_stream::StreamExt;
use std::time::Duration;

let config = BatchConfig { max_concurrent: 8, queue_capacity: 256, timeout: Duration::from_secs(30) };
let mut processor = BatchProcessor::new(config);
processor.submit(vec!["hello".into(), "world".into()], 10); // priority 10
let mut stream = processor.run();
while let Some(result) = stream.next().await {
    println!("job {} ratio={:.2}", result.job_id, result.ratio);
}

Higher-priority jobs are processed first via a BinaryHeap. Concurrent execution is bounded by max_concurrent.

Quality-Adaptive Compression

The adaptive module (src/adaptive.rs) measures and optimises the quality of token-level compression.

Quality model

overall_score = 0.4 * semantic_preservation
              + 0.4 * syntax_validity
              + 0.2 * (1 - ratio)

Metric	Description
`semantic_preservation`	Fraction of content words (non-stopwords) preserved
`syntax_validity`	Heuristic — penalises consecutive punctuation tokens
`ratio`	`compressed_len / original_len` — lower means more compressed
`overall_score`	Weighted combination (higher is better)

Usage

# Print quality metrics for the compression applied to a prompt
every-other-token "The quick brown fox jumps" --quality

API example

use every_other_token::adaptive::{AdaptiveCompressor, CompressionTarget, QualityMetric};

let tokens: Vec<String> = "machine learning transforms science".split_whitespace()
    .map(String::from).collect();

// Measure quality of any compression
let compressed = vec!["machine".into(), "transforms".into()];
let q = QualityMetric::measure(&tokens, &compressed);
println!("score={:.3}", q.overall_score);

// Adaptive: binary-search for the best compression meeting a quality bar
let compressor = AdaptiveCompressor::new();
let target = CompressionTarget { min_quality: 0.5, target_ratio: 0.5 };
let (compressed, quality) = compressor.compress(&tokens, &target);
println!("compressed to {} tokens with score={:.3}", compressed.len(), quality.overall_score);

Context Window Manager

The context module provides ContextWindow — a priority-based token budget manager for LLM context construction.

use every_other_token::context::{
    BlockType, ContextBlock, ContextWindow, ContextWindowConfig,
};

let cfg = ContextWindowConfig {
    max_tokens: 4096,
    reserved_for_output: 512,
    system_tokens: 128,
};
let mut window = ContextWindow::new(cfg);

window.add_block(ContextBlock {
    id: 1,
    content: vec!["You are a helpful assistant.".to_string()],
    priority: 255,
    block_type: BlockType::System,
}).unwrap();

window.add_block(ContextBlock {
    id: 2,
    content: vec!["What is Rust?".to_string()],
    priority: 200,
    block_type: BlockType::Query,
}).unwrap();

// Canonical order: System -> Document -> History -> Query
let assembled = window.assemble();
println!("utilization: {:.2}", window.utilization());
let stats = window.stats();
println!("evictions: {}", stats.evictions);

Key features:

Fixed token budget: max_tokens - reserved_for_output - system_tokens
Automatic eviction of lowest-priority History blocks when full
Canonical assembly: System → Document → History → Query
ContextStats tracks block counts by type, utilization, and eviction count
--context-budget <N> CLI flag sets the token budget (default: 4096)

Vocabulary Analyzer

The vocab module provides VocabularyAnalyzer — measures vocabulary coverage, OOV rates, and Zipf's law fit for token sequences.

use every_other_token::vocab::{VocabularyAnalyzer, Zipf};
use std::collections::HashMap;

// Build from a reference corpus
let corpus = vec![
    vec!["the".to_string(), "cat".to_string(), "sat".to_string()],
    vec!["the".to_string(), "dog".to_string(), "ran".to_string()],
];
let va = VocabularyAnalyzer::build_from_corpus(&corpus);

// Analyze a new sequence
let tokens = vec!["the".to_string(), "unknown_word".to_string()];
let stats = va.analyze(&tokens);
println!("coverage: {:.2}%", stats.coverage_pct * 100.0);
println!("oov_rate: {:.2}%", stats.oov_rate * 100.0);
println!("zipf_score: {:.4}", va.zipf_score());

// Find OOV tokens
let oov = va.oov_tokens_owned(&tokens);
println!("OOV: {:?}", oov);

// Fit Zipf's law directly
let mut freq: HashMap<String, u64> = HashMap::new();
freq.insert("the".to_string(), 1000);
freq.insert("cat".to_string(), 500);
freq.insert("sat".to_string(), 250);
let params = Zipf::fit(&freq);
println!("Zipf exponent: {:.3} (r²={:.3})", params.exponent, params.r_squared);

Key features:

build_from_corpus: aggregates token frequencies across multiple documents
analyze: coverage %, OOV rate, average/median reference frequency, distinct vocab size
oov_tokens: returns all tokens not in the reference vocabulary
Zipf::fit: log-log linear regression to extract Zipf exponent and R²
zipf_score: 1 - |exponent - 1.0|, normalized to [0, 1]
--vocab-stats CLI flag: prints vocabulary statistics for the current prompt

License

MIT -- see LICENSE for details.

Name		Name	Last commit message	Last commit date
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.claude		.claude
.github		.github
docs		docs
scripts		scripts
src		src
static		static
tasks		tasks
tests		tests
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
Screenshot 2025-07-08 185410.png		Screenshot 2025-07-08 185410.png
Screenshot 2025-07-09 015940.png		Screenshot 2025-07-09 015940.png
Screenshot 2025-07-12 161852.png		Screenshot 2025-07-12 161852.png
build.rs		build.rs
deny.toml		deny.toml
docker-compose.yml		docker-compose.yml

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every-other-token

Unique capabilities vs. standard LLM clients

Use cases

Interpretability research

Red-teaming

Prompt engineering

5-minute quickstart

Prerequisites

Run immediately

Shell completions

Transform types

Rate control

Token attribution export

Data types

Export to JSONL (LIME / Captum compatible)

Semantic drift detection

Finding the most and least confident tokens

Python interoperability

Token Attribution Map (causal leave-one-out)

Computation model

Example

Terminal renderer

Prompt Mutation Lab

Mutation targets

Mutation metrics

Example

Output table format

A/B system prompt testing

A/B via the web UI

Web UI guide

Configuration file

CLI reference

Divergence Detection Engine

Concepts

Jensen-Shannon divergence

Usage

Coloured diff report

Token Intervention Mode

Concepts

Causal effect measurement

TUI token editor

Causal influence map

Key modules

Feature flags

Semantic Similarity

CLI usage

Programmatic usage

Streaming with Backpressure

Example

Building from source

Contributing

Batch Processing

Data types

Usage

API example

Quality-Adaptive Compression

Quality model

Usage

API example

Context Window Manager

Vocabulary Analyzer

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