Meta-Vector++ Autoresearch

Parallel exploration of ICV composition methods for zero-shot reasoning improvement, powered by claudini's autoresearch loop.

What This Is

An adaptation of claudini's autoresearch pipeline to explore 4 parallel research directions for improving in-context vector (ICV) composition:

Direction	Run Code	Core Idea
Phase Decomposition	`phase`	Decompose ICVs into execution/reflection/transition components
Layer-Aware Composition	`layer`	Different composition strategies for early/mid/late transformer layers
Uncertainty Gating	`adaptive`	Entropy-adaptive steering strength with norm preservation
Test-Time Compute	`ttc`	Diverse steering ensemble + majority vote / LatentSeek warm-start

Setup

git clone https://huggingface.co/kweCobi/metavector-autoresearch
cd metavector-autoresearch
chmod +x setup.sh launch_all.sh monitor.sh
./setup.sh

Prerequisites: Python 3.12+, uv, Claude Code CLI, tmux, GPU (≥16GB for dev, ≥24GB for full eval).

Launch All Directions

cd claudini
./launch_all.sh # launches 4 tmux sessions
./monitor.sh # cross-direction leaderboard

Launch One Direction

cd claudini
claude
> /loop /metavector phase improve zero-shot reasoning on MATH-500 via phase-specific ICV decomposition

Research Context

Based on the Meta-Vector paper (ICLR 2026 submission) and 6 key concurrent papers:

SEAL (2504.07986): Reasoning phases form disjoint latent subspaces
Fractional Reasoning (2506.15882): Contrastive steering with norm preservation
LatentSeek (2505.13308): Test-time policy gradient in latent space
Steering Vector RL (2509.06608): Three-cluster layer structure for steering
DeepSeek-R1 (2501.12948): GRPO + rule-based rewards for reasoning

File Structure

metavector_claudini/
├── setup.sh # One-time setup
├── launch_all.sh # Launch 4 parallel tmux sessions
├── monitor.sh # Cross-direction leaderboard
├── CLAUDE.md # Developer guide for the agent
├── skills/metavector/SKILL.md # Claude Code autoresearch skill
├── configs/
│ ├── mv_math500_dev.yaml # Fast dev (1.5B, 50 problems)
│ ├── mv_math500.yaml # Full eval (7B, 500 problems)
│ ├── mv_gsm8k_dev.yaml # GSM8K sanity check
│ └── mv_aime.yaml # AIME 2024 hard eval
├── metavector_base/ # Shared evaluation code
│ ├── steering_optimizer.py # SteeringOptimizer ABC
│ ├── source_bank.py # ICV bank management
│ ├── evaluator.py # Benchmark runner
│ ├── baselines.py # Zero-shot, static ICV, holistic composition
│ └── utils.py # Hidden states, phase classification, answer extraction
└── starter_methods/ # v1 for each direction
 ├── mv_phase_v1/
 ├── mv_layer_v1/
 ├── mv_adaptive_v1/
 └── mv_ttc_v1/

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

URL: https://huggingface.co/kweCobi/metavector-autoresearch