Phase 1 & 2: Baseline experiments + AGoT implementation + reasoning graph analysis

## Added

### Core framework
- src/llm_client.py — Ollama API wrapper for local Docker inference
- src/samples.py — Embedded benchmark datasets (GSM8K/MATH/AIME + Chinese)
- src/run_all.py — Unified experiment runner

### Phase 1: Baselines
- src/baseline/benchmark.py — IO, CoT, CoT-SC, ToT evaluation with Chinese support

### Phase 2: AGoT
- src/agot/agot.py — AGoT core algorithm (6 agent types, recursive decomposition)
- src/agot/graph_utils.py — DAG graph data structure with cycle detection
- src/agot/prompts.py — 6 agent prompt templates (EN + ZH)
- src/agot/run.py — AGoT experiment runner

### Graph Analysis
- src/graph_analysis/reasoning_graph.py — Graph property computation (cyclicity, diameter, small-world)
- src/graph_analysis/visualize.py — Visualization charts

### Documentation
- docs/graph_reasoning_principles.md — Comprehensive principles document
- docs/next_steps.md — Roadmap for reliable reasoning tool

### Experiment Results
- GSM8K/MATH baselines on qwen2.5:32b and qwq:latest
- AGoT validation (100% on small sample)
- Chinese dataset benchmarks (C-GSM8K, CMATH)
- Graph property analysis confirming Topology of Reasoning findings

requirements.txt

@@ -0,0 +1,18 @@
+# s-LLM-project: Graph-Enhanced Reasoning for 32B LLMs
+# Core dependencies
+ollama>=0.6.0
+tqdm>=4.60.0
+numpy>=1.24.0
+
+# Graph analysis
+networkx>=3.0
+matplotlib>=3.5
+
+# For dataset loading (optional, can use embedded samples)
+# datasets>=2.14.0
+
+# For SFT training (Phase 3)
+# torch>=2.0.0
+# transformers>=4.36.0
+# peft>=0.6.0
+# accelerate>=0.24.0