feat(poc): stronger RNG via full SHA256 seed (concat_murmur)

[IgnatovFedor]

Purpose

Fixes gonka-ai/gonka#926.

The previous _seed_from_string used only the first 32 bits of a SHA256 digest, leaving a small enough seed space that an attacker could forge proofs by finding collisions in ~2^32 SHA256 evaluations - allowing a single model run to be submitted as multiple nodes' outputs with different nonces.

Changes:

• Add generate_inputs_concat_murmur: splits the full 256-bit SHA256 digest into 8 × 32-bit sub-seeds, runs each through the existing murmur3 -> Box-Muller pipeline, and concatenates segments. Forging a proof now requires a full SHA256 collision (~2^128 work).
• Add poc_stronger_rng boolean flag (default False) threaded from /init/generate and /generate request bodies all the way down to input generation function selection, enabling governance-controlled activation.
• Include poc_stronger_rng in request log lines for auditability.

Test Plan

# New function correctness
pytest tests/poc/test_gpu_random.py -k "concat_murmur" -v

# Flag propagation through API layer (no GPU needed)
pytest tests/poc/test_routes.py -k "poc_stronger_rng" -v
# Full routes suite
pytest tests/poc/test_routes.py -v

Test Result

All existing tests/poc/test_routes.py tests pass. New tests added:

• 8 tests for generate_inputs_concat_murmur (determinism, sensitivity, shape/dtype, difference from old function, Gaussian distribution, CPU/GPU match)
• 2 tests verifying poc_stronger_rng propagation through /init/generate and /generate

Cross-GPU validation: generated artifacts on one GPU architecture and validated on another (64 nonces each direction). Model - Qwen/Qwen3-235B-A22B-Instruct-2507-FP8, seq_len = 1024, k_dim=12. Both directions show fraud_detected: false with p_value=0.999, confirming the concat_murmur pipeline is deterministic across GPU architectures:

Generator	Validator	n_total	n_mismatch	p_value	fraud_detected
A100	H200	64	1	0.9988	false
H200	A100	64	1	0.9988	false

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