llmintent 1.1.0

LLMIntent suite: semantic extraction, model registry, isolates, motifs, trajectories, and IV layer-causal

LLMIntent

Semantic extraction & intent analysis for transformer LLMs
Morphemes · trajectories · reasoning subspaces · J-space layer thoughts · cognitive kernels

GitHub · PyPI · Install · Features · Visualization · Examples

---

Python library derived from the SemanticExtractionLLms research notebook. LLMIntent extracts semantic structure from transformer weights and runtime hidden states: morpheme wells, semantic poles, layer pivots, chain-of-thought intensity, compaction metrics (SSO), J-space layer thoughts, cognitive module kernels, unified activation trajectories, and a full visualization suite (maps, correlation matrices, animations).

Table of contents

• Source
• Install
• Quick start
• Model suite
• Full product suite
• Research pipeline
• CLI
• Modules
• Advanced features
  • 1. Activation layer identification
  • 2. Layer correspondence map
  • 3. J-space layer thoughts
  • 4. Cognitive module kernels
  • 5. Steering, compaction & weight semantics
  • 6. Full analysis report
  • 7. Semantic concept query
  • 8. Unified trajectory mapping
  • 9. Visualization suite
  • 10. Low-level API
  • 11. Heightened Reasoning Framework
  • 12. HellaSwag benchmark & SLM ablation
  • 13. Retracement Transformer
  • 14. Live suite — real-time app
• Visualization suite
• Examples
• Research lineage & citations
• License

Source

The reference notebook lives at reference/SemanticExtractionLLms.ipynb. Code cells are extracted to reference/extracted_cells.py.

Install

From PyPI

pip install llmintent
pip install "llmintent[viz]"      # maps & animations
pip install "llmintent[live]"       # Streamlit UI + FastAPI
pip install "llmintent[models]"     # accelerate stack for Qwen/Mistral/MiniMax/GLM suite
pip install "llmintent[suite]"      # prefer external intentisolates (vendored isolates always included)
pip install "llmintent[all]"        # full research stack

From source (development)

git clone https://github.com/ehallford11714/llmintent.git
cd llmintent
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -e ".[all]"
python -m spacy download en_core_web_sm
python -c "import stanza; stanza.download('en')"

Publishing to PyPI: see docs/PUBLISHING.md.

Optional extras:

Extra	Packages	Use when
[viz]	matplotlib, seaborn, pillow	Maps, correlation heatmaps, animations
[benchmark]	datasets	HellaSwag loading
[live]	fastapi, uvicorn, streamlit	Real-time Live app + API
[models] / [hf] / [slm]	transformers, torch, accelerate	Curated Qwen/Mistral/MiniMax/GLM suite
[suite] / [isolates]	intentisolates (optional)	Prefer extractable isolates; core already vendors offline path
[nlp]	stanza, spacy	Morpheme / lemma extraction
[embeddings]	gensim	GloVe projection & weight semantics
[all]	everything above	Full research pipeline

Quick start

from llmintent import LLMIntentAnalyzer

analyzer = LLMIntentAnalyzer("gpt2", load_glove=False)
report = analyzer.analyze_prompt("The quick brown fox jumps over the lazy")
print(report.activation_layers)
print(report.intensity_sweep.head())
analyzer.cleanup()

Model suite (Qwen / Mistral / MiniMax / GLM)

Beyond GPT-2 defaults, LLMIntent ships a curated model suite for larger instruct models. Full tables, VRAM guidance, and API fallbacks: docs/MODEL_SUITE.md.

Family	Example HF ids (tiny → medium)
Qwen	Qwen/Qwen2.5-0.5B-Instruct, …-3B-Instruct, …-7B-Instruct
Mistral	mistralai/Ministral-3-3B-Instruct-2512, …-8B-…, …-14B-…
MiniMax	MiniMaxAI/MiniMax-M2 (MoE; API preferred on small GPUs)
GLM	THUDM/chatglm3-6b, zai-org/GLM-4-9B-0414, zai-org/GLM-4.7-Flash
legacy	distilgpt2, gpt2 (offline CI)

from llmintent import LLMIntentAnalyzer, list_models, resolve_model_id
from llmintent.suite import get_model_spec, load_suite_model

list_models(family="qwen")
hf_id = resolve_model_id(family="qwen", size="tiny")
analyzer = LLMIntentAnalyzer.from_suite("qwen", "tiny", load_glove=False)
# Env: LLMINTENT_FAMILY=qwen LLMINTENT_SIZE=medium LLMINTENT_DEVICE=cuda

python -m llmintent models list
python -m llmintent models info qwen medium
python -m llmintent run --family legacy --size tiny --text "Hello"
python -m llmintent analyze --family qwen --size tiny --prompt "Two plus two equals"

Size tiers: tiny | small | medium | large | xl. Weights load lazily — never at import time. Multi-GB download tests stay behind LLMINTENT_LOAD_TEST=1.

Env var	Purpose
LLMINTENT_MODEL	HF id or suite key (qwen:medium)
LLMINTENT_FAMILY	qwen | mistral | minimax | glm | legacy
LLMINTENT_SIZE	tiny | small | medium | large | xl
LLMINTENT_DEVICE	cpu, cuda, cuda:0, …
LLMINTENT_LOAD_TEST	Set 1 to enable optional weight-download tests

Full product suite

LLMIntent is the suite home for models and intent-structure tooling. Architecture: docs/SUITE.md.

flowchart LR
  LI[LLMIntent]
  LI --> Models[Models<br/>Qwen/Mistral/MiniMax/GLM]
  LI --> Iso[Isolates + typology]
  LI --> Mot[Motifs + trajectories]
  LI --> Lat[Latent inspect hooks]
  LI --> IV[IV layer causal<br/>indication vs causation]

Module	Import	Offline?
Isolates + typology	llmintent.isolates	Yes (vendored; prefers intentisolates if installed)
Motifs / trajectories	llmintent.motifs	Yes
IV / layer causal	llmintent.iv_motifs	Yes (stdlib Wald; soft causaliv/autocausal)
Latent inspect	llmintent.latent	Soft stub until LatentIntentInspect publishes
Model suite	llmintent.suite	Registry offline; weights lazy

from llmintent.isolates import identify_isolates, form_motifs, trajectory_from_motifs
from llmintent.iv_motifs import LayerCausalSuite

text = "I want to finish. I cannot miss the deadline. I will submit it so that it is on time."
isos = identify_isolates(text=text)
motifs = form_motifs(isos)
traj = trajectory_from_motifs(motifs, isos)
print(traj.ascii_diagram)

result = LayerCausalSuite.from_text(text).run(outcome_hint="on time")
print(result.to_markdown())

python -m llmintent isolates --text "I want X but cannot Y"
python -m llmintent motifs --text "..."
python -m llmintent reasoning-trajectory --text "..."
python -m llmintent trajectory --text "..."          # isolates reasoning path
python -m llmintent iv-motifs --text "..." --mock-iv
python -m llmintent models list

Standalone extractable libs (intent-isolates, LatentIntentInspect) may still be installed separately; the suite re-exports them when present.

Changelog (1.1.0)

• Suite unity — isolates, motifs, reasoning trajectories, IV layer-causal, and latent hooks under llmintent
• Vendored isolates — offline identify_isolates / form_motifs / LayerCausalSuite without a second package
• CLI umbrella — isolates, motifs, reasoning-trajectory, iv-motifs, models
• Docs — docs/SUITE.md

1.0.0

• Model suite — curated Qwen / Mistral / MiniMax / GLM / legacy registries with size tiers
• CLI — llmintent models list|info|env, llmintent run --family … --size …
• API — list_models, resolve_model_id, get_model_spec, load_suite_model, LLMIntentAnalyzer.from_suite
• Extras — [models] / [hf] / [slm] add accelerate for larger local loads
• Docs — docs/MODEL_SUITE.md (HF ids, VRAM, API fallbacks)

Research pipeline

LLMIntent combines three research lines into one pipeline:

Lineage	What it contributes
SemanticExtractionLLms (Kineteq)	Weight semantics, morpheme wells, steering poles, SSO compaction
Anthropic J-space / Global Workspace	Logit & J-lens decode, transport maps, regime bands, intent traces
Cognitive kernels (novel)	KL + twin Barlow minimization → identity / reasoning / meta / ideation

flowchart LR
  subgraph inputs [Inputs]
    P[Prompt]
    T[Twin prompt CoT]
    C[Concept queries]
  end

  subgraph extract [Extraction]
    W[Weight semantics / morphemes]
    H[Hidden-state forward pass]
    J[J-space decode + transport]
  end

  subgraph analyze [Analysis]
    A[Activation pivots]
    K[Cognitive kernels]
    Q[Concept query KNN]
    TR[Trajectory mapping]
  end

  subgraph viz [Visualization]
    M[Maps]
    CR[Correlation matrices]
    AN[Animations]
  end

  P --> H
  T --> H
  P --> W
  H --> J
  H --> A
  H --> K
  C --> Q
  A --> TR
  K --> TR
  Q --> TR
  J --> TR
  TR --> M
  TR --> CR
  TR --> AN
  W --> M

Typical workflow:

1. Run a prompt (and optional twin for CoT comparison).
2. Extract per-layer signals: entropy, KL, intensity, J-space intents, cognitive modules.
3. Query concepts ("subtraction", "eight") against the activation trajectory.
4. Merge everything into a single trajectory table.
5. Visualize as maps, correlation matrices, and layer-by-layer animations.

CLI

llmintent analyze --model gpt2 --prompt "Two plus two equals"
llmintent analyze --family qwen --size tiny --prompt "Two plus two equals"
llmintent trace --model gpt2 --prompt "The spider has 8 legs" --transport --track 8 6
llmintent layers --model gpt2 --prompt "Let's think step by step"
llmintent cognitive --model gpt2 --twin-a "simple prompt" --twin-b "CoT prompt"
llmintent query --model gpt2 --concept "subtraction" --prompt "Eight minus two equals" --twin-b "Let's think step by step..."
llmintent trajectory --model gpt2 --prompt "Eight minus two equals" --twin-b "Let's think step by step..." --concepts subtraction eight
llmintent compare-cot --model gpt2 --direct "I have ten apples..." --cot "Let's think step by step..."

# Model suite
llmintent models list --family mistral
llmintent models info glm medium
llmintent run --family legacy --size small --text "The capital of France is"

# Visualization — full report or single artifact
llmintent viz --model gpt2 --prompt "Eight minus two equals" --twin-b "Let's think..." --concepts subtraction eight --blocks

# Heightened reasoning — diagnose focus and force retrace
llmintent heighten --model gpt2 --prompt "Eight minus two equals ? Answer:" --anchor "Let's think step by step..." --concepts subtraction eight
llmintent heighten --model gpt2 --prompt "..." --anchor "..." --mode concept_anchor --steer

# Retracement Transformer — perplexity ablation
llmintent retracement perplexity --model gpt2 --mode focus_gate --limit 24
llmintent retracement ablation --models gpt2 distilgpt2 --limit 16

# Live — real-time app (Phi-3, Qwen 0.5B)
llmintent live models
llmintent live run --model gpt2 --prompt "Eight minus two equals ?" --action analyze
llmintent live serve --model qwen-0.5b --port 8765
llmintent live ui

llmintent viz --type trajectory-map --model gpt2 --prompt "Eight minus two equals" --output-dir out/
llmintent viz --type subspace-anim --model gpt2 --prompt "Eight minus two equals"

viz --type options: full, trajectory-map, morpheme-map, subspace, concept-corr, reasoning-corr, trajectory-anim, subspace-anim, intent-anim

Modules

Module	Purpose
suite	Curated Qwen / Mistral / MiniMax / GLM / legacy registry + lazy load
metrics	SSO score, Shannon entropy, KL divergence
activation	Inference pivot, workspace peak, motor onset, intensity peak
layers	Layer → regime, role, top intent, cognitive module
jspace	Logit/J-lens decode, transport maps, intent traces
cognitive	Identity, reasoning, meta-reasoning, ideation kernels
trajectory	Unified activation trajectory mapping across layers
query	Semantic concept → layer activation via KL-Barlow-KNN
viz	Maps, correlation matrices, and animations
heighten	Focused / extreme retrace + activation steering
benchmark	HellaSwag SLM eval, retrace store, ablation compare
retracement	Retracement Transformer perplexity & architecture ablation
live	Real-time Live suite — Phi-3, Qwen 0.5B, API + Streamlit UI
morphemes	Lemma/morpheme extraction (Stanza, spaCy, polyglot)
projection	GloVe ↔ model embedding projection matrix
poles	Semantic, grammatical, numerical reference poles
weight_semantics	Weight-slice → vocabulary KNN → semantic units
steering	Layer-wise pole intensity and CoT comparison
compaction	SVD-based semantic isolate detection
analyzer	High-level LLMIntentAnalyzer facade

---

Advanced features

1. Activation layer identification

Pinpoints where computation "turns on" inside a transformer for a given prompt.

from llmintent import LLMIntentAnalyzer

analyzer = LLMIntentAnalyzer("gpt2")
layers = analyzer.identify_activation("Two plus two equals")
# {
#   "inference_pivot": 4,    # largest entropy drop (maturation)
#   "workspace_peak": 5,       # max J-space occupancy in middle layers
#   "motor_onset": 11,         # decode aligns with final output
#   "intensity_peak": 3,       # max numerical-pole similarity
# }

Use cases: locate the inference pivot for CoT vs direct prompts, find where numerical reasoning concentrates, compare activation profiles across models.

---

2. Transformer layer correspondence map

Every layer gets a functional label — not just depth, but what it is doing.

layer_map = analyzer.layer_correspondence(
    "Question: 12 * 2 - 5 = ? Answer:",
    twin_b="Question: 12 * 2 - 5 = ? Answer: Let's think step by step...",
)
print(layer_map[[
    "layer", "regime", "role", "top_intent",
    "dominant_module", "kl_divergence", "is_activation_pivot"
]])

Column	Meaning
regime	sensory → workspace → motor (Anthropic bands)
role	Human-readable function (e.g. "Abstract reasoning & silent verbal thoughts")
top_intent	Dominant decoded token at that layer
dominant_module	identity / reasoning / meta_reasoning / ideation
kl_divergence	Twin-prompt structural tension at this layer

---

3. J-space layer thoughts (Anthropic Jacobian lens)

Surfaces "words on the model's mind" at each layer — including silent intermediates before the final token.

Based on Verbalizable Representations Form a Global Workspace in Language Models (Gurnee et al., 2026).

analyzer = LLMIntentAnalyzer("gpt2", fit_jspace_transport=True)

trace = analyzer.intent_trace(
    "Question: A spider has 8 legs. Remove 2. Answer:",
    track_tokens=["8", "6", "spider"],
)

# Top thought at each depth
for layer in [0, 3, 6, 11]:
    print(f"L{layer}: {trace.top_thought_at(layer)!r}")

# Token rank evolution across layers
print(trace.rank_curves)  # {"8": [None, 45, 12, 3, ...], ...}
print(trace.regime_bands) # {"sensory": (0,3), "workspace": (4,8), "motor": (9,12)}

Transport lens: fit_jspace_transport=True fits linear maps J_l so h_final ≈ J_l @ h_l, correcting for representational rotation that breaks the standard logit lens in early/mid layers.

Sparse decomposition: active verbal intents via greedy matching pursuit over the unembedding dictionary (jspace.decompose).

---

4. Cognitive module kernels (KL + Twin Barlow)

Four cognitive functions identified per layer by comparing twin prompts (e.g. direct vs chain-of-thought):

Module	Detection signal	Cognitive role
identity	Low KL + high Barlow diagonal	Stable self-representation; twin-invariant binding
reasoning	Mid-high KL + high J-space occupancy	Primary computation in workspace band
meta_reasoning	KL spikes + Barlow off-diagonal coupling	Monitoring/restructuring ("thinking about thinking")
ideation	High entropy + low motor alignment	Divergent generation before readout commit

profile = analyzer.cognitive_modules(
    twin_a="I have five apples and eat two. I now have exactly",
    twin_b=(
        "Question: I have five apples and eat two. How many remain? "
        "Answer: Let's think step by step. Five minus two equals"
    ),
)

for kernel in profile.kernels:
    print(f"{kernel.module:15} L{kernel.layer:2d}  score={kernel.score:.3f}  intent={kernel.top_intent!r}")

print(profile.layer_assignments[["layer", "dominant_module", "reasoning", "meta_reasoning"]])

Algorithm:

1. Compute per-layer KL(P_twin_b ‖ P_twin_a) on next-token distributions
2. Collect twin hidden-state trajectories; minimize Barlow Twins loss (diagonal → 1, off-diagonal → 0) weighted by KL
3. Extract combined kernel basis via KL-weighted SVD + Barlow projector
4. Score each layer for four modules; assign dominant module + peak kernel per module

---

5. Steering, compaction, and weight semantics (notebook lineage)

From the original SemanticExtractionLLms research:

# CoT vs direct intensity sweep
sweep = analyzer.compare_prompts({
    "Direct": "If I have ten apples and lose three, I have",
    "CoT": "Question: ... Answer: Let's think step by step...",
})

# KL stress test (simple vs complex prompt)
stress = analyzer.stress_test(
    "I have five apples and I eat two. I now have exactly",
    "If I start with the square root of twenty-five and subtract the smallest prime...",
)

# Full analysis with compaction + block semantics
report = analyzer.analyze_prompt(
    prompt,
    cot_prompt=cot_prompt,
    twin_b=cot_prompt,
    include_compaction=True,
    include_block_semantics=True,
    track_tokens=["8", "6"],
)
print(report.compaction)        # SSO isolate density per layer
print(report.inference_pivot)   # compaction-derived pivot

SSO (Semantic-Structural Orthogonality): (|SemSim| - |StrSim|) / (|SemSim| + |StrSim|) — measures semantic purity of FFN weight components after GloVe projection.

Morpheme wells: extract_block_semantics() maps each layer's weight slices to top semantic units via GloVe KNN — the raw material for morpheme heatmaps in the viz suite.

---

6. Full analysis report

analyze_prompt() returns an AnalysisReport combining all subsystems:

report = analyzer.analyze_prompt(
    "Question: 12 * 2 - 5 = ? Answer:",
    cot_prompt="... Let's think step by step ...",
    twin_b="... Let's think step by step ...",
    include_jspace=True,
    include_compaction=False,
    track_tokens=["24", "19"],
)

report.activation_layers   # pivot layers
report.intent_trace        # J-space IntentTrace
report.layer_map           # full correspondence + cognitive modules
report.cognitive_profile   # CognitiveModuleProfile
report.intensity_sweep     # numerical pole intensity per layer
report.entropy_trajectory  # maturation curve
report.cot_comparison      # direct vs CoT at pivot
report.pivot_entropy       # entropy validation at pivot

---

7. Semantic concept query (KL + Barlow + KNN)

Directly query a semantic concept (plain text) and get back which layers in the activation trajectory it activates.

result = analyzer.query_concept(
    concept="subtraction",
    prompt="Question: Eight minus two equals ? Answer:",
    twin_b="Question: ... Answer: Let's think step by step. Eight minus two is",
)

print(result.peak_layer)       # e.g. 5
print(result.matched_layers)   # [5, 4, 6, 3, 7]
print(result.knn_ranking)      # KNN + fused scores per layer
print(result.trajectory)       # full trajectory with concept_activation column

Strategy:

1. Build per-layer KL + twin Barlow feature vectors from twin prompts
2. Embed concept text into the same space (token embeddings + contextual hidden state)
3. KNN (cosine) retrieves nearest layers in Barlow-projected space
4. Re-rank by KNN sim × KL weight × Barlow invariance × semantic probe
5. Annotate full activation trajectory with concept_similarity and concept_activation

# Batch query
results = analyzer.query_concepts(
    ["identity", "reasoning", "ideation", "numerical"],
    prompt,
    twin_b=cot_prompt,
)

Concept query results feed directly into trajectory mapping (concept_*_activation columns) and the visualization correlation matrices.

---

8. Unified trajectory mapping

Single API that merges all per-layer signals into one trajectory table:

mapping = analyzer.trajectory_map(
    prompt="Question: Eight minus two equals ? Answer:",
    twin_b="... Let's think step by step ...",
    concepts=["subtraction", "eight", "step by step"],
)

print(mapping.pivots)                    # inference_pivot, workspace_peak, ...
print(mapping.layers)                    # full per-layer DataFrame
print(mapping.layers_for_concept("subtraction"))

Each row = one layer. Columns include:

Column group	Fields
Maturation	entropy, entropy_drop, occupancy
Steering	intensity, kl_divergence, kl_weight
J-space	top_intent, top_intent_prob, motor_alignment, regime
Cognitive	dominant_module, reasoning, meta_reasoning, ideation, barlow_invariance
Pivots	is_activation_pivot, pivot_tags
Concepts	concept_{name}_activation, concept_{name}_similarity

The trajectory table is the single source of truth for all visualization outputs.

---

9. Visualization suite

Maps, correlation matrices, and animations for morphemes, trajectories, and reasoning subspaces.

paths = analyzer.visualize_report(
    prompt="Question: Eight minus two equals ? Answer:",
    twin_b=cot_prompt,
    concepts=["subtraction", "eight"],
    output_dir="llmintent_viz",
    include_morphemes=True,
)

for name, path in paths.items():
    print(f"{name}: {path}")

Or use VisualizationSuite directly:

from llmintent import VisualizationSuite

viz = analyzer.visualizer("llmintent_viz")
mapping = viz.trajectory_mapping(prompt, twin_b=cot_prompt, concepts=["subtraction"])
trace = viz.intent_trace(prompt)

viz.save_trajectory_map(mapping)
viz.save_reasoning_subspace(prompt, mapping=mapping)
viz.save_concept_correlation(mapping)
viz.save_trajectory_animation(mapping)
viz.save_subspace_animation(prompt, trace=trace)
viz.save_intent_animation(trace)

Install viz extras: pip install llmintent[viz]

See Visualization suite below for artifact descriptions and design rationale.

---

10. Low-level API

For custom pipelines without the facade:

from llmintent.kernels import minimize_twin_barlow, per_layer_kl_profile, collect_twin_hidden_matrix
from llmintent.jspace import decode_intents, fit_transport_maps, sparse_intent_decomposition
from llmintent.cognitive import build_cognitive_module_profile
from llmintent.metrics import calculate_sso_score, kl_divergence, shannon_entropy
from llmintent.viz import plot_trajectory_map, plot_concept_correlation, animate_trajectory_maturation

# Direct kernel fitting
kl, _ = per_layer_kl_profile(bundle, twin_a, twin_b)
h_a, h_b = collect_twin_hidden_matrix(bundle, twin_a, twin_b)
projector, metrics = minimize_twin_barlow(h_a, h_b, kl, proj_dim=32)

# Single-layer intent decode
intents = decode_intents(bundle, hidden_state, layer=6, transport=projector, top_k=10)
sparse = sparse_intent_decomposition(bundle, hidden_state, k=16)

# Standalone plots (no analyzer)
plot_trajectory_map(mapping)
plot_concept_correlation(mapping)
animate_trajectory_maturation(mapping, save_path="out.gif")

---

11. Heightened Reasoning Framework (heighten/)

Heighten reasoning by forcing the model to retrace itself — then measure whether computation becomes more focused (layer-concentrated, concept-peaked, less ideation/meta dispersion).

Research basis: meta-reasoning detects when CoT restructuring occurs (cot_delta); look-ahead planning shows middle layers encode future decisions; focused reasoning requires suppressing diffuse ideation before motor commit.

What “focused reasoning” means in LLMIntent

Metric	High = focused	Low = diffuse
reasoning_concentration	Reasoning peaks in few layers	Spread across depth
concept_peakiness	Concepts activate sharply	Flat concept profile
reasoning_ideation_ratio	Reasoning dominates ideation	Speculation without commit
meta_load	Low monitoring overhead	“Thinking about thinking” loops
motor_prematurity	Motor rises after reasoning	Early output lock-in
focus_score	Composite 0–1	Triggers needs_retrace if < 0.45

Retrace modes

Mode	Scaffold
explicit_retrace	“Wait — let me retrace my reasoning step by step…”
concept_anchor	“Focusing strictly on {concepts}, let me work through this again…”
pivot_replay	Replay from inference pivot with concept focus
correction	“I need to reconsider. My prior path may have been diffuse…”
focused_cot	Minimal CoT chain constrained to essential steps

API

from llmintent import LLMIntentAnalyzer

analyzer = LLMIntentAnalyzer("gpt2", load_glove=False)

# Diagnose focus
focus, mapping = analyzer.diagnose_focus(
    prompt="Question: Eight minus two equals ? Answer:",
    anchor_prompt=cot_prompt,
    concepts=["subtraction", "eight"],
)
print(focus.focus_score, focus.needs_retrace)

# Heighten via forced retrace
result = analyzer.heighten_reasoning(
    prompt="Question: Eight minus two equals ? Answer:",
    anchor_prompt=cot_prompt,
    concepts=["subtraction", "eight"],
    mode="explicit_retrace",
    apply_steering=True,  # activation injection at reasoning layers
)

print(result.plan.retrace_prompt)
print(result.focus_gain)           # focus_score_delta, meta_load_delta, ...
print(result.heightening_successful)

Pipeline

flowchart LR
  A[Baseline prompt + anchor] --> B[FocusMetrics]
  B -->|needs_retrace| C[RetracePlan]
  C --> D[Retrace prompt twin]
  D --> E[Re-measure focus]
  E --> F{apply_steering?}
  F -->|yes| G[Inject focus vector at pivot layers]
  F -->|no| H[HeightenedReasoningResult]
  G --> H

Activation steering: extract_reasoning_focus_vector() builds a direction from anchor→retrace hidden delta, blended with the reasoning cognitive kernel. Forward hooks inject this vector at retrace_layers (pivots + reasoning peaks).

cot_delta wired: per-layer twin shift magnitude now feeds meta_reasoning_layer_scores in cognitive_modules().

---

12. HellaSwag benchmark & SLM ablation (benchmark/)

Validate focused and extreme retrace interventions on small language models against HellaSwag commonsense completion, with all forced retracements stored for comparison.

Prepared SLMs

Key	Model	Params
gpt2	gpt2	124M
distilgpt2	distilgpt2	82M
gpt2-medium	gpt2-medium	355M
opt-125m	facebook/opt-125m	125M

Ablation conditions

Condition	Description
baseline	Raw context only
focused	Focused reasoning scaffold
retrace	Single forced retrace
extreme_retrace	Chained triple retrace + concept lock
retrace_steer	Retrace + activation steering
extreme_steer	Extreme retrace + steering
iterative_heighten	Loop until focus threshold
extreme_iterative	Extreme chain + iterative heighten

Retrace storage

All retracements saved to JSONL (RetraceStore):

llmintent_retraces/hellaswag.jsonl   # per-example records
llmintent_retraces/hellaswag_results.csv

Each record: context, retrace_prompt, retrace_chain, focus_baseline/after, predicted label, accuracy, ablation condition.

CLI

pip install llmintent[benchmark]

llmintent benchmark slms
llmintent benchmark hellaswag --models gpt2 distilgpt2 --limit 50 --conditions fast
llmintent benchmark hellaswag --models gpt2 --fallback --limit 8
llmintent benchmark compare --store llmintent_retraces/hellaswag.jsonl --export-csv results.csv

Python API

from llmintent import prepare_slm_comparison, RetraceStore, BenchmarkRunConfig, HellaSwagBenchmarkRunner

# Quick comparison (fast ablation suite)
results = prepare_slm_comparison(models=["gpt2", "distilgpt2"], limit=20)

# Full control
runner = HellaSwagBenchmarkRunner(BenchmarkRunConfig(
    models=["gpt2", "distilgpt2"],
    limit=100,
    store_path="llmintent_retraces/hellaswag.jsonl",
))
df = runner.run_all()
print(runner.compare_from_store())

Extreme retrace chain

from llmintent.heighten import build_extreme_retrace_chain, ExtremeRetraceMode

chain = build_extreme_retrace_chain(
    anchor_prompt=context,
    concepts=["commonsense", "continuation"],
    mode=ExtremeRetraceMode.CONCEPT_LOCK.value,
)
# chain.passes → list of retrace scaffolds
# chain.combined_prompt → full prefix for scoring

---

13. Retracement Transformer (retracement/)

Inference-time focused-reasoning architecture built from LLMIntent insights: sensory → retrace pivot → workspace → motor bands, with hook-based gates instead of weight fine-tuning.

Proposed structure

Input → [Sensory layers 0–33%] → RETRACE PIVOT (FocusGate)
      → [Workspace layers 33–78%] → optional dual-pass merge
      → [Motor layers 78–100%] → LM head

Modes (ablation)

Mode	Mechanism
baseline	Standard forward (control)
focus_gate	Sigmoid-gated self-focus vector at pivot
retrace_steer	Anchor→retrace delta injection at workspace layers
dual_pass	Snapshot at pivot, blend in workspace band
workspace_loop	Focus gate at every workspace layer
extreme	Amplified workspace loop + multi-pass blend

Perplexity ablation

Compare modes on WikiText-2 (or built-in fallback corpus). Lower perplexity vs baseline suggests the retracement path improves next-token prediction under focused reasoning constraints.

llmintent retracement perplexity --model gpt2 --mode focus_gate --limit 24
llmintent retracement ablation --models gpt2 distilgpt2 --limit 16
llmintent retracement ablation --models gpt2 --full   # all six modes

Python API

from llmintent import RetracementConfig, RetracementMode, run_retracement_ablation

df = run_retracement_ablation(models=["gpt2", "distilgpt2"], fast=True, text_limit=24)
print(df[["model_name", "mode", "perplexity", "delta_ppl_vs_baseline"]])

from llmintent.retracement import RetracementTransformer, evaluate_perplexity, load_eval_texts

cfg = RetracementConfig(mode=RetracementMode.DUAL_PASS)
result = evaluate_perplexity("gpt2", cfg, load_eval_texts(limit=32))
print(result.perplexity, result.avg_nll)

---

14. Live suite — real-time app (live/)

LLMIntent Live applies focused reasoning in interactive latency on loaded SLMs — Phi-3 Mini, Qwen2.5 0.5B Instruct, TinyLlama, GPT-2, etc.

Architecture: docs/LIVE_SUITE.md

Streamlit UI / FastAPI / CLI
         ↓
LiveIntentPipeline (analyze · heighten · generate · probe)
         ↓
LiveSession (hot model + RetracementTransformer)
         ↓
heighten · retracement · activation (research modules)

Registered models

Key	Model	Chat
qwen-0.5b	Qwen/Qwen2.5-0.5B-Instruct	yes
phi3-mini	microsoft/Phi-3-mini-4k-instruct	yes
phi2	microsoft/phi-2	no
tinyllama	TinyLlama/TinyLlama-1.1B-Chat-v1.0	yes
gpt2	gpt2	no
distilgpt2	distilgpt2	no

Install & run

pip install -e ".[live]"

llmintent live models
llmintent live run --model qwen-0.5b --prompt "Explain photosynthesis briefly." --action generate
llmintent live serve --model qwen-0.5b --port 8765
llmintent live ui

Python API

from llmintent import LiveIntentPipeline, LiveSessionConfig

pipe = LiveIntentPipeline(LiveSessionConfig(model_key="qwen-0.5b", retracement_mode="focus_gate"))
pipe.load()

analysis = pipe.analyze("What is 12 × 2?")
heighten = pipe.heighten("What is 12 × 2?", steer=True)
completion = pipe.generate("What is 12 × 2?", retracement_mode="dual_pass")
tokens = pipe.probe_next_tokens("The capital of France is")

pipe.unload()

FastAPI endpoints

Method	Path	Purpose
GET	/models	List registry + loaded model
POST	/load	Switch model
POST	/analyze	Pivots + focus score
POST	/heighten	Retrace scaffold + focus delta
POST	/generate	Completion with retracement / steer
POST	/probe	Top-k next tokens

Streamlit UI tabs

Tab	Purpose
Analyze	Activation pivots + focus score
Heighten	Forced retrace + focus gain
Generate	Completion with Retracement Transformer
Compare	Baseline vs retracement next-token probe
Probe	Top-k next tokens
Visualize	Real-time layer activation stream + maps, correlations, animations

Requires pip install -e ".[live]" (includes matplotlib/seaborn for viz).

---

Visualization suite

The viz module (src/llmintent/viz/) turns analysis outputs into inspectable artifacts. Three families:

Maps

Static spatial views of where semantics and computation live.

Artifact	Function	What you see
Morpheme map	plot_morpheme_map	Layer × semantic unit heatmap from weight-slice KNN (which morphemes each block "knows")
Trajectory map	plot_trajectory_map	Normalized metric heatmap across layers; white dashed lines mark activation pivots
Reasoning subspace	plot_reasoning_subspace	2D PCA of per-layer hidden states, colored by regime or cognitive module; path shows depth progression

When to use maps: compare models, identify which layers encode a concept, see whether reasoning concentrates in the workspace band.

Correlation matrices

Quantify how signals co-vary across the depth dimension.

Artifact	Function	What you see
Concept correlation	plot_concept_correlation	Pearson r between concept activation traces (e.g. does "subtraction" peak where "eight" peaks?)
Reasoning trace correlation	plot_reasoning_trace_correlation	r between entropy, KL, intensity, occupancy, and cognitive module scores

When to use correlations: detect redundant vs complementary signals, validate that CoT twin divergence aligns with reasoning module scores, find concept clusters.

Animations

Temporal views of how the model's internal state matures layer by layer.

Artifact	Function	What you see
Trajectory maturation	animate_trajectory_maturation	Line chart builds up metric curves; pivot labels appear as layers are revealed
Subspace animation	animate_reasoning_subspace	Point travels through PCA space; trail shows prior layers
Intent filmstrip	animate_intent_grid	Top decoded intent at each layer for a fixed token position

When to use animations: presentations, debugging pivot timing, showing silent verbal thoughts emerging in workspace layers before motor commit.

Output directory layout

A full visualize_report() run produces:

llmintent_viz/
├── morpheme_map.png              # optional (--blocks / include_morphemes)
├── trajectory_map.png
├── reasoning_subspace.png
├── concept_correlation.png
├── reasoning_trace_correlation.png
├── trajectory_maturation.gif
├── reasoning_subspace.gif
└── intent_layers.gif

Color conventions

Viz outputs use consistent colors aligned with regime and module semantics:

Label	Color	Used in
Sensory regime	#4C72B0	Subspace maps, regime bands
Workspace regime	#55A868	Subspace maps, regime bands
Motor regime	#C44E52	Subspace maps, regime bands
Identity module	#8172B3	Subspace point colors
Reasoning module	#CCB974	Subspace point colors
Meta-reasoning	#64B5CD	Subspace point colors
Ideation	#E377C2	Subspace point colors

---

Examples

Script	What it demonstrates
examples/basic_steering.py	Intensity sweep + entropy trajectory
examples/cot_intensity.py	Direct vs CoT comparison
examples/jspace_layer_thoughts.py	J-space trace + activation layers
examples/cognitive_kernels.py	Identity/reasoning/meta/ideation kernels
examples/trajectory_mapping.py	Unified activation trajectory map
examples/query_concept.py	Semantic concept → layer activation query
examples/viz_suite.py	Full visualization report (maps, correlations, animations)
examples/heighten_reasoning.py	Focus diagnosis, forced retrace, activation steering
examples/hellaswag_benchmark.py	HellaSwag SLM ablation + retrace store
examples/retracement_ablation.py	Retracement Transformer perplexity ablation
examples/live_demo.py	Live suite — analyze, heighten, generate on SLM

License

MIT — see LICENSE.

PyPI: llmintent · GitHub: ehallford11714/llmintent