Cheng Chang

Weights constrained to {-1, 0, +1}; proved 1.58-bit quantization converges at small scale.

Delta-rule memory M += β(v − M·k)⊗k performs targeted correction rather than blind accumulation — 3.54× better PPL than the transformer baseline.

An O(n) linear-attention cumsum replacing O(n²) attention — 15× cheaper per layer (136µs vs 2,062µs).

Product-Softmax addressing expands to 512 virtual slots with Top-8 sparse read/write.

Predict a bag-of-words 16 tokens ahead via a 65K-param head — the project’s largest single gain (2.5×).

Explicit memory registers that bind to variable identities and store current values. Execution order is preserved via sequential per-chunk writes — something parallel attention fundamentally cannot do.

Learnable eigenvalues constrained to [−1, 1] let the cell subtract state, enabling proper variable reassignment that standard RNNs (eigenvalues in [0,1]) cannot do.

Perception (linear attention) + Execution (state slots) + Structure (dynamic GNN) + Meta (recurrent controller with a verification head), combined via a learned cross-system bridge.

Every dimension a multiple of 16 for the Ascend NPU; log-space parallel scan for FP16 numerical stability.

Mode-collapse, "dumbcrafting" data (injected misspellings verified in the preference pairs), single-detector reward, no meaning constraint, forced register/person, self-sabotaging post-processing, and a backwards premise.

DEPO-style GRPO with a hard semantic-preservation constraint (BERTScore ≥ 0.85). Padding violates the constraint, so collapse is structurally prevented — not heuristic.

A level-matched, person-preserved dataset (11.7k pairs, no injected errors) replacing the flawed "errors = human" data.

MAGE / RADAR / Binoculars / Fast-DetectGPT / RoBERTa for cross-detector transfer instead of a single reward.

Not a fine-tune — networks seeded only by KataGo’s bootstrap net and trained end-to-end via self-play.

b28c512nbt (291MB) → efficient b18c384nbt (105MB) with aggressive auto-LR to reach competitive strength on far less compute.

A separate training line producing a released final at ~8.5M self-play samples.

Kata_web pulls the pure-CPU KataGo Eigen build plus the latest KW model and serves a live bot, deployed via GitHub Pages.

Generates moves in priority phases (TT → captures → quiets); in positions where a capture cuts off, the quiet generator is never invoked. Live-instrumented with cutoff stats.

PVS, LMR (noisy/quiet tables), null-move, singular + double extension + multi-cut, ProbCut, razoring, aspiration windows, SEE quiescence, lazy SMP, IIR/IID, multi-table correction history.

Hand-crafted MG/EG eval with self-derived PSTs, per-level mobility tables, a quadratic king-safety model, and KXK/KNK/KRK mating specials — no eval values borrowed from any engine.

A 148-parameter direct-optimization tuner with an OpenMP dataset generator.

A custom array-of-bytes pattern scanner; inline hooks auto-relocate live register/offset bytes so minor game-build changes are absorbed. ~22 feature signatures.

Resolves the game’s CDatabase vtable[9] (ExecuteQuery), builds a 34-byte x64 shellcode stub, allocates RWX memory, and fires it via CreateRemoteThread — driving the game’s own SQLite engine.

Handles the UWP edge case where .NET’s Process.MainModule throws AccessDenied, via EnumProcessModulesEx.

Ghidra decompilation proved the earlier "anti-cheat bypass" was actually corrupting a flag manager and causing the crashes — so it was removed and the false claims retracted (commit 39c8edc).