What it is Talos-XII is a CLI simulator for the gacha system in Arknights: Endfield. Rather than sampling from a static probability table, it trains a small set of neural nets to model environment uncertainty and pull-decision policy, then uses them to answer questions a static table can’t easily express — e.g. “as a F2P player, what’s my probability of getting the rate-up unit on free currency alone?” or “given my current pity count, should I keep pulling or save for the next banner?” Models (trained on first run, ~30-45s, then cached to disk) • EnvNet — small MLP fitting an environment noise/bias distribution, sampled per simulation • Luck Optimizer — neural optimizer over a 32-dim engineered feature vector (pity progress, streaks, interaction terms) • Dueling DQN — discrete pull/wait decision • PPO actor-critic — with an MLA (latent-attention) transformer for continuous strategy Everything underneath is hand-written, no external ML framework: • Custom autograd engine (matmul, conv2d, pooling, norms, gradient-checked backward passes) • Runtime SIMD dispatch: scalar → AVX2 → AVX2+FMA → AVX-512, NEON on ARM64 • Rayon-parallelized sims (~10k+/sec on my laptop) • BF16 inference caches • Optional PyO3 bridge (import talos_xii as tx) for writing training scripts without NumPy/PyTorch • 142 tests, CI on Linux/Windows/macOS with ARM64 cross-compile, single static binary, MIT The part I’m not confident about There’s a component I call ACHF (Adaptive Cache-aware Hyper-Connections): it blends a dense path with a pruned sparse path via a gradient-sensitive gate, adds a manifold (Sinkhorn) weight projection, and switches between cached/sparse/dense execution paths based on measured latency. Loosely inspired by manifold-constrained hyper-connections, but aimed at a different regime — compact RL policies running on CPU inside a single binary, not large-scale training. I don’t yet know if the speed/accuracy tradeoff holds up outside my own machine. I’m treating it as an open experiment, not a result. Where I’d like help I only have access to my own hardware, so my benchmark coverage is thin. There’s an automated benchmark suite in the repo that reports mean ± std with 95% CIs, per-path latency distributions (p50/p90/p99), training curves, and raw CSVs — instructions are in the README. If anyone’s willing to run it on a different CPU (AVX-512, ARM NEON) or GPU setup, I’d genuinely appreciate the data — negative results (ACHF not helping on your hardware) are just as useful to me as positive ones. Repo: github.com/zayokami/Talos-XII Solo project, built to learn Rust + ML fundamentals from scratch. Happy to answer questions about any of the implementation details. submitted by /u/zay0kami [link] [Kommentare]
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I've been thinking about BTC’s opportunity cost. People can still be long-term bullish, but when AI stocks, gold, oil, or other trades are moving faster, it becomes harder to decide the portion to allocate to BTC. Holding can still make sense, especially as a core position, but there is a trade-off when other assets have clearer momentum. That is what I keep going back and forth on. I look at BTC on bydfi these days and realized I was comparing it against others with the same capital. How are you guys here reading BTC now? Still holding as a core position, selling a bit, or rotating into stronger momentum? submitted by /u/Choice_Employee_7739 [link] [Kommentare]
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