prabodha — प्रबोध

Recognition-gated workspace steering for language models.

Prabodha ("awakening") implements the missing control theory for the Jacobian-lens actuator: steer a frozen LLM through its global workspace using a small recognition-driven world model. Writes are timed by sphurattā events (uncommitted moments), verified by āgama re-cognition, bounded by svātantrya (autonomy), and diagnosed by the three malas.

Built as a bridge between PWM (Pratyabhijñā World Model, Sharath S), the J-space (Anthropic's verbalizable global workspace), and GNW (Global Neuronal Workspace), read as engineering.

What it does

The readback verdict is weak (BA ≈ 0.59 at n=120 — honest negative, gates L14–L16); corpus-amplitude coupling is confirmed directionally but fails the strict margin criterion (gate L19 fail-on-margin). No new claims are made; all numbers are committed to gates.

60-second quickstart

pip install prabodha

Fit a lens and steer on a public model (Qwen3-4B, ~6 GB):

from prabodha.lens import fit, vis
from prabodha.steer import write

# 1. Fit a band-targeted lens (one-time; resumable)
fit(
    model_config_path="configs/models/qwen3.yaml",
    lens_config_path="configs/lens_mid.yaml",
    out_path="outputs/lens_qwen3_mid30.pt"
)

# 2. Steer with recognition-gated writes
write(
    model_config_path="configs/models/qwen3.yaml",
    lens_file_path="outputs/lens_qwen3_mid30.pt",
    exp_config_path="configs/experiments/e13full.yaml",
    out_path="gates/my_run.json",
    alpha=0.3,
    seed=42,
    emit_trace="outputs/traces/my_trace.json"  # optional: emit per-token trace
)

# 3. Visualize lens readout (interactive HTML)
vis(
    model_config_path="configs/models/qwen3.yaml",
    lens_file_path="outputs/lens_qwen3_mid30.pt",
    prompt="the fire remembers rivers",
    out_path="outputs/fire_vis.html"
)

See examples/quickstart_qwen3.md and examples/quickstart_nemotron.md for full command-line workflows with expected numbers (gate-cited).

Install & use

Library

pip install prabodha            # core library + CLI
pip install prabodha[hybrid]    # + flash-linear-attention support

Public API

# Lens operations
from prabodha.lens import fit, eval, vis

# Steering operations
from prabodha.steer import write, gate, verify

CLI

prabodha --help                           # all subcommands
prabodha lens-fit --model M.yaml --lens L.yaml --out lens.pt
prabodha lens-eval --model M.yaml --lens-file lens.pt --exp E.yaml --out gate.json
prabodha lens-vis --model M.yaml --lens-file lens.pt --prompt "..." --out page.html
prabodha steer --model M.yaml --mid-lens lens.pt --exp E.yaml --out gate.json [--emit-trace trace.json]
prabodha figures                          # regenerate paper figures from gates/

Plugin & MCP integration

Claude Code users: the plugin at integrations/claude-code-plugin/ ships skills (lens-map, steer-verify) with defaults from the measured findings.

MCP server at integrations/mcp-server/ exposes lens_map, steer_generate, readback_verify, list_gates for any MCP client.

Provenance & license

Vendored: anthropics/jacobian-lens (Apache-2.0) — companion code for Verbalizable Representations Form a Global Workspace (Anthropic, 2026).

Prabodha: Sharath S, Pratyabhijñā World Model (arXiv, 2026).

License: Apache-2.0.

Author: Sharath S qbz506@york.ac.uk · GitHub: SharathSPhD · Release: v1.0.0

Docs: jspace_pratyabhijna_scoping.md · Paper: docs/paper/paper.pdf · Live app: prabodha.vercel.app · Pages: sharathsphd.github.io/prabodha · HuggingFace: qbz506/prabodha-lenses