decision-point-attention

Decision Point Attention (DPA)

Strategic Full Attention Routing for Efficient Agent Reasoning with Linear Attention

Target: NeurIPS 2026 (DDL: May 6, 2026)

Core Idea

Linear attention models (Mamba, GLA, DeltaNet) fail on multi-turn agent reasoning (MiniMax M2 case). Current hybrids (Jamba 1:7, Kimi Linear 1:3) add full attention uniformly. DPA routes only "decision point" tokens (~10-15%) through full softmax attention, keeping the rest on linear attention.

Project Structure

src/
  models/
    dpa_model.py          # DPA architecture (GLA + softmax router)
    baselines.py          # Transformer, pure linear, uniform hybrid baselines
    router.py             # Decision point router (learned binary classifier)
  data/
    agent_trajectory.py   # Synthetic agent trajectory generator
    decision_points.py    # Decision point labeling & analysis
    datasets.py           # HotpotQA, GSM8K, ToolBench loaders
  eval/
    benchmark.py          # Unified evaluation pipeline
    metrics.py            # Accuracy, FLOPs, latency, KV cache metrics
    visualize.py          # Publication figures
configs/
  dpa_7b.yaml             # 7B model config
  dpa_72b.yaml            # 72B model config (Merlin 8xH100)
scripts/
  run_baseline.sh         # Run all baselines
  run_dpa.sh              # Run DPA experiments
  run_ablation.sh         # Ablation studies
paper/
  main.tex                # NeurIPS 2026 LaTeX
results/                  # Experiment outputs
figures/                  # Generated plots

Quick Start

# 1. Install deps
pip install -r requirements.txt

# 2. Run trajectory analysis (no GPU needed)
python src/data/decision_points.py

# 3. Run attention simulation (CPU/MPS OK)
python src/eval/benchmark.py --mode simulate

# 4. Run full training (8xH100 on Merlin)
bash scripts/run_dpa.sh