File size: 77,147 Bytes
c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 2b2bae2 c97bd71 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 | // ============================================================================
// SyFox CLI — learn / calibrate / decide / demo
// (thin tooling around the SI substrate core; no logic lives here)
// ============================================================================
#include "core/syfox.hpp"
#include "core/derive.hpp"
#include "core/bench.hpp"
#include "core/gate.hpp"
#include "core/recall.hpp"
#include "core/firewall.hpp"
#include <cmath>
#include <chrono>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <map>
#include <sstream>
#include <string>
#include <thread>
#include <vector>
#if defined(_OPENMP)
#include <omp.h>
#endif
namespace {
void usage_exit();
struct Args {
std::string model = "model";
std::string examples;
std::string state;
std::string questions;
std::string domain;
std::string concept;
long steps = 64; // dream steps
unsigned long long seed = 0x5EED5EEDull; // dream seed (deterministic by default)
std::string eval; // bench eval rows (defaults to --examples)
std::string gate; // derivation gate rows (no-regression replay)
std::string memories; // recall memory store (jsonl)
long topk = 5; // recall top-k
std::string split; // bench --split train|heldout (P1 eval split)
bool coverage_curve = false; // bench --coverage-curve (P2 headline metric)
std::string synonyms; // synonym table path (default data/synonyms.txt)
// v2.2 multilingual + active-learning surface
std::string lang; // --lang auto|<slug>: route to <model>-<script>; empty = off
int ngrams_mode = 0; // --ngrams on|off: 1/-1 explicit; 0 = policy default
bool augment = false; // learn --augment: mass-guarded variant lessons
float typos = 0; // bench --typos P: deterministic corruption sweep (0..100)
std::string deferrals; // decide --log-deferrals FILE.jsonl (active-learning loop)
std::string out; // active --out FILE.jsonl (labeling worksheet)
long min_count = 1; // active --min-count N
bool dedup = false; // learn --dedup: skip exact duplicate lessons (M2)
bool novelty = false; // learn --novelty: per-lesson dose by novelty (M2)
float novelty_floor = 0.25f; // learn --novelty-floor F (A/B knob)
bool energy_norm = false; // decide-side energy gain for big-corpus fabrics (M1)
float defer_margin = 0; // decide --defer-margin P: defer when p1-p2 < P
// (honest uncertainty at the decision layer; the
// physics still decided — this is a disclosure knob)
bool evidence = false; // decide --evidence: machine-auditable evidence JSON (M3)
bool adversarial = false; // bench --adversarial: M4 stress suite (read-only)
std::string mix; // bench --mix FILE: cross-domain vocabulary source (M4)
long threads = 0; // --threads N: OMP settle threads (1 = sequential; 0 = default)
long throughput = 0; // bench --throughput N: batched multicore decisions/sec (M5)
long epochs = 1; // learn --epochs N: consolidation passes (see lane_decay)
long latency_reps = 20; // bench --latency-reps N (timing repeats per probe)
long replays = 2; // bench --replays N (determinism double-run count)
bool state_file = false, questions_file = false;
// SI-faithful selection modes (off by default; never persisted into the model)
bool salience_gating = false, miller_window = false;
// v3.2 semantic layer + retrieval-by-default + two-stage router
bool no_semantics = false; // --no-semantics: runtime kill switch for the semantic field
bool no_retrieval = false; // --no-retrieval: skip associative priming
bool no_hierarchy = false; // --no-hierarchy: skip stage-1 category gating
long retrieval_topk = -1; // --retrieval-topk N (-1 = engine default 5)
float retrieval_dose = 0; // --retrieval-dose F (0 = engine default 0.30)
std::string router; // --router DIR: stage-1 domain fabric (router.json maps domains)
};
// Apply the CLI mode overrides after load_model(). Mode-neutral by design:
// substrate.bin stays untouched, flags live only for this process.
void apply_modes(syfox::Engine& eng, const Args& a) {
eng.substrate().set_source_modes(a.salience_gating, a.miller_window);
// v3.2 semantic layer knobs: the field is ON whenever the model ships it
// (substrate v4 tail); the kill switches restore pre-3.2 behavior exactly.
eng.substrate().set_semantics(!a.no_semantics);
eng.set_retrieval(!a.no_retrieval);
if (a.retrieval_topk >= 0) eng.set_retrieval_topk(static_cast<int>(a.retrieval_topk));
if (a.retrieval_dose > 0) eng.set_retrieval_dose(a.retrieval_dose);
if (a.no_hierarchy) eng.set_hierarchy(false);
// v3 Milestone 5: --threads N controls deterministic parallel settle on
// OMP builds (bit-identical to sequential; test-verified). N=1 forces the
// sequential path; N=0 leaves the default. Non-OMP builds ignore it.
if (a.threads > 0) {
#if defined(_OPENMP)
omp_set_num_threads(static_cast<int>(a.threads));
#endif
eng.substrate().set_parallel_settle(a.threads != 1);
}
}
std::string read_file(const std::string& path) {
std::ifstream f(path);
if (!f) { std::cerr << "syfox: cannot open " << path << "\n"; std::exit(2); }
std::string buf((std::istreambuf_iterator<char>(f)), std::istreambuf_iterator<char>());
return buf;
}
std::vector<sfx::JV> load_jsonl(const std::string& path) {
std::vector<sfx::JV> rows;
std::ifstream f(path);
if (!f) { std::cerr << "syfox: cannot open " << path << "\n"; std::exit(2); }
std::string line;
while (std::getline(f, line)) {
if (line.empty() || line[0] == '#' || line[0] == '/') continue;
try { rows.push_back(sfx::JV::parse(line)); }
catch (const std::exception& e) { std::cerr << "syfox: " << path << ": " << e.what() << "\n"; std::exit(2); }
}
return rows;
}
// The label's own description text inside criteria (for choice/score outcomes).
// For array criteria the label is the level INDEX ("0","1","2") or the level text.
std::string outcome_text(const sfx::JV& q, const std::string& label) {
const sfx::JV& crit = q.at("criteria");
if (crit.is_obj() && crit.has(label)) return label + " " + crit.at(label).as_str();
if (crit.is_arr()) {
for (const auto& v : crit.arr)
if (v.as_str() == label) return label; // label is the level text
long idx = std::strtol(label.c_str(), nullptr, 10);
if (idx >= 0 && idx < static_cast<long>(crit.arr.size()))
return crit.arr[static_cast<std::size_t>(idx)].as_str(); // label is the index
}
return label;
}
// -- multilingual routing (v2.2) -------------------------------------------
// --lang empty : v2.1 behavior — whatever --model says, no detection (off)
// --lang auto : detect the script of the text, use <model>-<script-slug>
// --lang slug : force a script family (latin, bengali, devanagari, ...)
// Read commands (decide/bench/calibrate/recall) fall back to the base model
// with an honest note when the routed substrate is missing; learn CREATES
// the routed substrate (that is how per-script fabrics grow).
//
// Trigram policy: when the user did not pass --ngrams explicitly, sub-word
// bridges ACTIVATE automatically whenever the routed substrate is non-Latin
// (the script barrier and tiny vocabularies make them load-bearing there);
// Latin keeps the word-level stream that reproduces the v2.1 baselines.
std::string route_model(const Args& a, const std::string& state_text,
std::string& note, bool for_learning) {
note.clear();
if (a.lang.empty()) return a.model;
const si::script::Script sc = (a.lang == "auto")
? si::script::detect_script(state_text)
: si::script::from_slug(a.lang);
if (sc == si::script::Script::Unknown) {
note = "script=unknown; using base model";
return a.model;
}
if (a.ngrams_mode == 0) // policy default (explicit flag wins)
si::norm::grams_enabled() = (sc != si::script::Script::Latin);
const std::string suffix = std::string("-") + si::script::slug(sc);
if (a.model.size() > suffix.size() &&
a.model.compare(a.model.size() - suffix.size(), suffix.size(), suffix) == 0)
return a.model; // already the routed substrate
const std::string routed = a.model + suffix;
if (!for_learning) {
std::ifstream probe(routed + "/substrate.bin");
if (!probe) {
note = "no " + routed + " substrate trained; falling back to " + a.model
+ " (honest silence still guards untaught vocabulary)";
return a.model;
}
}
note = "script=" + std::string(si::script::slug(sc)) + " -> " + routed;
return routed;
}
void cmd_learn(const Args& a) {
// Milestone-1 firewall: hidden/calibration splits never teach the fabric.
if (!syfox::firewall::learn_may_read(a.examples)) {
std::cerr << "syfox: firewall: " << a.examples << " is a "
<< syfox::firewall::role_name(syfox::firewall::role_of_path(a.examples))
<< " split — learn is refused (hidden rows never train, calibrate, "
"derive, or select models)\n";
std::exit(2);
}
auto rows = load_jsonl(a.examples);
const syfox::LearnPolicy lp{a.dedup, a.novelty, a.novelty_floor};
long lessons = 0, skipped = 0;
// v3: consolidation passes. The substrate's own forgetting law decays
// every lane 0.995x per lesson, so a 25k-lesson SINGLE pass is
// recency-truncated (early lanes are decayed away before training ends).
// --epochs N re-teaches the same distinct lessons N times — measured on
// game/guard hidden tests this recovers early knowledge (deterministically,
// unlike accidental incremental accumulation). Distinctness still rules
// per lesson: see the Milestone-2 A/B.
const std::vector<const sfx::JV*> epoch_rows = [&]() {
std::vector<const sfx::JV*> v;
for (long e = 0; e < a.epochs; ++e)
for (const auto& r : rows) v.push_back(&r);
return v;
}();
if (!a.lang.empty()) {
// v2.2 --lang: route every lesson by its script family into a
// per-script substrate (<model>-<slug>). Latin gets its own substrate
// like every other family — one fabric per script is the isolation the
// routing contract promises. Group order is std::map order: deterministic.
std::map<std::string, std::vector<const sfx::JV*>> groups;
for (const auto& ex : rows) {
const si::script::Script sc = (a.lang == "auto")
? si::script::detect_script(ex.at("state").as_str())
: si::script::from_slug(a.lang);
groups[si::script::slug(sc)].push_back(&ex);
}
sfx::JVArr routed;
for (auto& g : groups) {
const std::string dir = a.model + "-" + g.first;
// trigram policy per script group (explicit --ngrams wins)
si::norm::grams_enabled() = (a.ngrams_mode != 0)
? (a.ngrams_mode == 1) : (g.first != "latin");
syfox::Engine eng;
eng.load_model(dir); // incremental if exists
eng.set_context(dir); // audit context tag (M3)
// v3.1.3 BUGFIX: this loop MUST teach g.second (this script
// family's rows), not epoch_rows (the whole file). The v2.2 code
// iterated epoch_rows here, so every per-script substrate was
// taught the ENTIRE corpus — the opposite of the isolation the
// routing contract promises. Epochs are applied per family.
for (long e = 0; e < a.epochs; ++e) {
for (const auto* exp : g.second) {
const sfx::JV& qs = exp->at("questions");
const sfx::JV& labels = exp->at("labels");
const std::string state = exp->at("state").as_str();
for (const auto& qkv : qs.obj) {
const sfx::JV& q = qkv.second;
std::string type = q.at("type").as_str();
std::string label = labels.at(qkv.first).as_str();
bool learned = false;
if (type == "choice" || type == "score")
eng.learn_example(state, q.at("instructions").as_str(),
outcome_text(q, label), a.augment, lp, &learned);
else if (type == "noul")
eng.learn_noul(state, q.at("instructions").as_str(),
label == "true", a.augment, lp, &learned);
++lessons;
if (!learned) ++skipped;
}
}
}
eng.save_model(dir);
routed.push_back(sfx::JV(sfx::JVObj{
{"script", sfx::JV(g.first)}, {"model", sfx::JV(dir)},
{"lessons", static_cast<double>(g.second.size())},
{"dedup_skipped", static_cast<double>(0)},
{"contradictions", static_cast<double>(eng.conflicts().size())},
{"nodes", static_cast<double>(eng.substrate().node_count())},
{"lanes", static_cast<double>(eng.substrate().lane_count())}}));
}
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("learn")}, {"examples", sfx::JV(a.examples)},
{"lang", sfx::JV(a.lang)}, {"augment", sfx::JV(a.augment)},
{"dedup", sfx::JV(a.dedup)}, {"novelty", sfx::JV(a.novelty)},
{"epochs", static_cast<double>(a.epochs)},
{"lessons", static_cast<double>(lessons)},
{"dedup_skipped", static_cast<double>(skipped)},
{"routed", sfx::JV(routed)},
{"note", sfx::JV("lessons routed per script family: one SI substrate per script")}}).dump() << "\n";
return;
}
syfox::Engine eng;
eng.load_model(a.model); // incremental if model exists
eng.set_context(a.model); // audit context tag (M3)
for (const auto* exp : epoch_rows) {
std::string state = exp->at("state").as_str();
const sfx::JV& qs = exp->at("questions");
const sfx::JV& labels = exp->at("labels");
for (const auto& qkv : qs.obj) {
const sfx::JV& q = qkv.second;
std::string type = q.at("type").as_str();
std::string label = labels.at(qkv.first).as_str();
bool learned = false;
if (type == "choice" || type == "score")
eng.learn_example(state, q.at("instructions").as_str(),
outcome_text(q, label), a.augment, lp, &learned);
else if (type == "noul")
eng.learn_noul(state, q.at("instructions").as_str(),
label == "true", a.augment, lp, &learned);
++lessons;
if (!learned) ++skipped;
}
}
eng.save_model(a.model);
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("learn")}, {"examples", sfx::JV(a.examples)},
{"model", sfx::JV(a.model)}, {"augment", sfx::JV(a.augment)},
{"dedup", sfx::JV(a.dedup)}, {"novelty", sfx::JV(a.novelty)},
{"epochs", static_cast<double>(a.epochs)},
{"lessons", static_cast<double>(lessons)},
{"dedup_skipped", static_cast<double>(skipped)},
{"nodes", static_cast<double>(eng.substrate().node_count())},
{"lanes", static_cast<double>(eng.substrate().lane_count())},
{"contradictions", static_cast<double>(eng.conflicts().size())},
{"note", sfx::JV(a.dedup
? "exact duplicate lessons skipped (Milestone-2 distinct-experience policy)"
: "every lesson taught (legacy behavior)")}}).dump() << "\n";
}
void cmd_calibrate(const Args& a) {
// Milestone-1 firewall: the hidden test never sets a calibration scalar.
if (!syfox::firewall::calibrate_may_read(a.examples)) {
std::cerr << "syfox: firewall: " << a.examples << " is a HIDDEN test split"
<< " — calibrate is refused (hidden rows never participate in "
"calibration, training, derivation, or model selection)\n";
std::exit(2);
}
// v2.2 --lang: calibrate the substrate the examples route to (fitting a
// different script's substrate would set scalars on a fabric that never
// saw the rows — meaningless). Dominant script over the file's states.
std::string lang_note;
std::string model_dir = a.model;
if (!a.lang.empty()) {
auto probe_rows = load_jsonl(a.examples);
std::string agg;
for (const auto& r : probe_rows) if (r.has("state")) agg += r.at("state").as_str() + "\n";
model_dir = route_model(a, agg, lang_note, false);
}
syfox::Engine eng;
eng.load_model(model_dir);
if (a.energy_norm) eng.set_energy_norm(true);
auto rows = load_jsonl(a.examples);
auto calib_rows = eng.harvest_rows(rows);
// v2.1 (P6): report calibration honestly — MULTI-CLASS ECE on the fit
// rows BEFORE (T=1 / default Platt) and AFTER (whatever fit_calibration
// adopted; its 1-bit ECE guard may keep T=1). Same helper the fit uses,
// same definition bench reports.
eng.fit_calibration(calib_rows);
eng.save_model(model_dir);
const auto& c = eng.calibration();
const auto r4 = [](double v) { return std::round(v * 10000.0) / 10000.0; };
sfx::JVObj o;
o["command"] = sfx::JV("calibrate");
o["model"] = sfx::JV(model_dir);
if (!lang_note.empty()) o["lang_note"] = sfx::JV(lang_note);
o["fit_rows"] = sfx::JV(static_cast<double>(calib_rows.size()));
o["fit_source"] = sfx::JV(a.examples);
o["choice_temperature"] = std::round(c.choice_temperature * 10000.0) / 10000.0;
o["score_temperature"] = std::round(c.score_temperature * 10000.0) / 10000.0;
o["noul_a"] = std::round(c.noul_a * 10000.0) / 10000.0;
o["noul_b"] = std::round(c.noul_b * 10000.0) / 10000.0;
o["multiclass_ece_before"] = sfx::JV(sfx::JVObj{ // T = 1, default Platt
{"choice", r4(syfox::Engine::calibration_ece(calib_rows, "choice", 1.0f, 6.0f, -3.0f))},
{"score", r4(syfox::Engine::calibration_ece(calib_rows, "score", 1.0f, 6.0f, -3.0f))},
{"noul", r4(syfox::Engine::calibration_ece(calib_rows, "noul", 1.0f, 6.0f, -3.0f))}});
o["multiclass_ece_after"] = sfx::JV(sfx::JVObj{ // adopted parameters
{"choice", r4(syfox::Engine::calibration_ece(calib_rows, "choice", c.choice_temperature, c.noul_a, c.noul_b))},
{"score", r4(syfox::Engine::calibration_ece(calib_rows, "score", c.score_temperature, c.noul_a, c.noul_b))},
{"noul", r4(syfox::Engine::calibration_ece(calib_rows, "noul", 1.0f, c.noul_a, c.noul_b))}});
o["note"] = sfx::JV("fit rows supply energies only; if this is the held-out "
"split, the fabric itself never learned from them — the "
"fit sets 2-3 scalars (temperature/Platt), and argmax is "
"unaffected (temperature is monotone)");
std::cout << sfx::JV(o).dump() << "\n";
}
sfx::JV answers_to_json(const std::vector<syfox::Answer>& ans, const syfox::Usage& u) {
sfx::JVObj out;
for (const auto& a : ans) {
sfx::JVObj o;
if (a.type == "choice") {
o["choice"] = sfx::JV(a.choice);
sfx::JVObj probs;
for (const auto& p : a.probabilities) probs[p.first] = sfx::JV(std::round(p.second * 1000.0f) / 1000.0f);
o["probabilities"] = sfx::JV(probs);
o["confidence"] = sfx::JV(std::round(a.confidence * 1000.0f) / 1000.0f);
} else if (a.type == "score") {
o["score"] = sfx::JV(std::round(a.value * 1000.0f) / 1000.0f);
sfx::JVObj probs;
for (const auto& p : a.probabilities) probs[p.first] = sfx::JV(std::round(p.second * 1000.0f) / 1000.0f);
o["probabilities"] = sfx::JV(probs);
o["confidence"] = sfx::JV(std::round(a.confidence * 1000.0f) / 1000.0f);
} else if (a.type == "noul") {
o["noul"] = sfx::JV(std::round(a.probability * 1000.0f) / 1000.0f);
o["confidence"] = sfx::JV(std::round(a.confidence * 1000.0f) / 1000.0f);
}
o["deferred"] = sfx::JV(a.deferred);
if (!a.reason.empty()) o["reason"] = sfx::JV(a.reason);
out[a.qid] = sfx::JV(o);
}
sfx::JVObj usage{
{"state_tokens", static_cast<double>(u.state_tokens)},
{"vocabulary", static_cast<double>(u.vocabulary)},
{"lanes", static_cast<double>(u.lanes)},
{"settled_energy", std::round(u.settled_energy * 1000.0f) / 1000.0f},
{"calibrated", sfx::JV(u.calibrated)},
{"engine", std::string("syfox-") + syfox::VERSION},
{"core", "si-substrate"},
};
if (!u.retrieved.empty()) {
sfx::JVArr ret;
for (const auto& r : u.retrieved)
ret.push_back(sfx::JV(sfx::JVObj{
{"label", sfx::JV(r.first)},
{"resonance", std::round(r.second * 1000.0f) / 1000.0f}}));
usage["retrieval"] = sfx::JV(ret); // v3.2: memories that primed this decision
}
return sfx::JV(sfx::JVObj{{"answers", sfx::JV(out)}, {"usage", sfx::JV(usage)}});
}
void cmd_decide(const Args& a) {
std::string state = a.state_file ? read_file(a.state) : a.state;
std::string qtext = a.questions_file ? read_file(a.questions) : a.questions;
sfx::JV questions = sfx::JV::parse(qtext);
if (!questions.is_obj()) { std::cerr << "syfox: questions must be a JSON object\n"; std::exit(2); }
std::string lang_note;
std::string model_dir = route_model(a, state, lang_note, false);
sfx::JVObj route_report;
// -- v3.2 two-stage physics router ---------------------------------------
// Stage 1: a SMALL dedicated router fabric (500-node class, one anchor per
// domain) settles the state and picks the domain anchor — pure field
// dynamics, same substrate physics, no classifier.
// Stage 2: the domain model mapped in the router's router.json (models:
// {anchor: model-dir}) decides the actual questions; --model stays as the
// fallback domain layer when the mapping misses. Both stages are
// independent SI settles; the route is disclosed in the output.
if (!a.router.empty()) {
syfox::Engine reng;
reng.load_model(a.router);
apply_modes(reng, a);
if (a.energy_norm) reng.set_energy_norm(true); // M1 gain for the router too
sfx::JV rschema(sfx::JVObj{});
{
std::ifstream rf(a.router + "/router.json");
if (rf) {
std::string buf((std::istreambuf_iterator<char>(rf)), std::istreambuf_iterator<char>());
rschema = sfx::JV::parse(buf);
}
}
if (!rschema.has("anchors") || !rschema.at("anchors").is_obj()) {
std::cerr << "syfox: router model " << a.router << " lacks router.json anchors\n";
std::exit(2);
}
sfx::JV rqs(sfx::JVObj{
{"route", sfx::JV(sfx::JVObj{
{"type", sfx::JV("choice")},
{"instructions", sfx::JV("which domain does this state belong to")},
{"criteria", rschema.at("anchors")}})}});
syfox::Usage ru;
auto rans = reng.decide(state, rqs, ru);
ru.calibrated = reng.calibration().fitted;
const syfox::Answer& ra = rans[0];
sfx::JVObj rj;
rj["anchor"] = sfx::JV(ra.deferred ? std::string() : ra.choice);
rj["confidence"] = sfx::JV(std::round(ra.confidence * 1000.0f) / 1000.0f);
rj["deferred"] = sfx::JV(ra.deferred);
std::vector<std::pair<float, std::string>> ranked;
for (const auto& p : ra.probabilities) ranked.emplace_back(p.second, p.first);
std::sort(ranked.begin(), ranked.end(), [](const auto& x, const auto& y){ return x.first > y.first; });
sfx::JVArr top3;
for (std::size_t i = 0; i < ranked.size() && i < 3; ++i)
top3.push_back(sfx::JV(sfx::JVObj{
{"anchor", sfx::JV(ranked[i].second)},
{"p", sfx::JV(std::round(ranked[i].first * 1000.0f) / 1000.0f)}}));
rj["top"] = sfx::JV(top3);
if (rschema.has("models") && rschema.at("models").is_obj()
&& !ra.deferred && rschema.at("models").has(ra.choice)) {
model_dir = rschema.at("models").at(ra.choice).as_str();
rj["model"] = sfx::JV(model_dir);
}
route_report = std::move(rj);
}
syfox::Engine eng;
eng.load_model(model_dir);
apply_modes(eng, a);
if (a.energy_norm) eng.set_energy_norm(true); // Milestone-1 gain knob
// --memories FILE (v3.2): explicit memory store for decide — overrides any
// model-dir memories.jsonl for this process. Rows: {"label":..., "state":...}.
if (!a.memories.empty()) {
std::vector<syfox::recall::Memory> mems;
for (const auto& r : load_jsonl(a.memories)) {
syfox::recall::Memory m;
m.label = r.at("label").as_str();
m.state = si::norm::normalize(r.at("state").as_str());
if (!m.label.empty() && !m.state.empty()) mems.push_back(std::move(m));
}
if (!mems.empty()) eng.set_memories(std::move(mems));
}
syfox::Usage u;
auto answers = eng.decide(state, questions, u);
u.calibrated = eng.calibration().fitted; // decide() resets Usage; set after
// --defer-margin P: the substrate still decides (physics untouched); a
// margin below P is DISCLOSED as a deferral instead of a confident-looking
// label. Measured motivation: reworded probe criteria can decide at
// |p1-p2| ~ 0.01-0.05 with confidence 0 — honest silence should extend
// to tied candidates, not only to a dark field.
if (a.defer_margin > 0) {
for (auto& ans : answers) {
if (ans.deferred || ans.probabilities.size() < 2) continue;
float p1 = 0, p2 = 0;
for (const auto& pr : ans.probabilities) {
if (pr.second > p1) { p2 = p1; p1 = pr.second; }
else if (pr.second > p2) p2 = pr.second;
}
if (p1 - p2 < a.defer_margin) {
ans.deferred = true;
ans.reason = "low_margin";
}
}
}
sfx::JV out = answers_to_json(answers, u);
if (!lang_note.empty()) out.obj["lang_note"] = sfx::JV(lang_note);
if (!route_report.empty()) out.obj["route"] = sfx::JV(route_report);
// v3 Milestone 3: machine-auditable evidence — supporting lanes with
// provenance, plus any contradiction records for this exact state.
if (a.evidence)
out.obj["evidence"] = eng.evidence_json(state, questions, answers);
// v2.2 active-learning loop, step 1: log deferrals for human labeling.
// Each row carries the state + the FULL question schema, so a labeled
// row is directly teachable with `syfox learn` — no reconstruction step.
if (!a.deferrals.empty()) {
bool any = false;
sfx::JVArr def;
for (const auto& ans : answers)
if (ans.deferred)
def.push_back(sfx::JV(sfx::JVObj{
{"qid", sfx::JV(ans.qid)}, {"reason", sfx::JV(ans.reason)}}));
if ((any = !def.empty())) {
std::ofstream log(a.deferrals, std::ios::app);
if (log)
log << sfx::JV(sfx::JVObj{
{"state", sfx::JV(state)},
{"questions", questions},
{"deferred", sfx::JV(def)},
{"settled_energy", std::round(u.settled_energy * 1000.0f) / 1000.0f}}).dump() << "\n";
out.obj["deferrals_logged"] = sfx::JV(static_cast<double>(def.size()));
}
}
std::cout << out.dump() << "\n";
}
// ---------------------------------------------------------------------------
// Built-in demos. No hand-coded decision rules anywhere: every answer comes
// out of the settled field of the SI substrate.
// ---------------------------------------------------------------------------
struct Demo { std::string name, state, questions; };
const std::vector<Demo>& demos_for(const std::string& domain) {
static const std::vector<Demo> tickets = {
{"stripe broken, losing sales",
"Hi, I've been trying to connect my stripe account for 3 days and it keeps failing. I'm losing sales. Please help ASAP.",
R"({"department":{"type":"choice","instructions":"Which team should handle this","criteria":{"billing":"payment or subscription issues","technical":"bugs or integration problems","sales":"pricing or account questions"}},"frustration":{"type":"score","instructions":"How frustrated the customer appears","criteria":["calm just stating facts","frustrated but civil","very angry strong language"]},"is_urgent":{"type":"noul","instructions":"the message conveys urgency or time sensitivity"}})"},
{"double charge refund",
"You charged me twice for the same invoice this month. Please refund the extra payment.",
R"({"department":{"type":"choice","instructions":"Which team should handle this","criteria":{"billing":"payment or subscription issues","technical":"bugs or integration problems","sales":"pricing or account questions"}},"frustration":{"type":"score","instructions":"How frustrated the customer appears","criteria":["calm just stating facts","frustrated but civil","very angry strong language"]},"is_urgent":{"type":"noul","instructions":"the message conveys urgency or time sensitivity"}})"},
{"team plan pricing",
"We want to upgrade to the team plan for twenty seats. Can you send the pricing?",
R"({"department":{"type":"choice","instructions":"Which team should handle this","criteria":{"billing":"payment or subscription issues","technical":"bugs or integration problems","sales":"pricing or account questions"}},"frustration":{"type":"score","instructions":"How frustrated the customer appears","criteria":["calm just stating facts","frustrated but civil","very angry strong language"]},"is_urgent":{"type":"noul","instructions":"the message conveys urgency or time sensitivity"}})"},
};
static const std::vector<Demo> game = {
{"zombies at night",
"Night. Zombies are spawning near the player. Health is dropping fast.",
R"({"action":{"type":"choice","instructions":"What should the bot do next","criteria":{"flee":"run away escape avoid danger retreat safe","fight":"attack combat weapon sword strike","dig_in":"hide wait build shelter fortify safe"}}})"},
{"calm day, build mode",
"Daytime. No threats nearby. The player wants a safehouse and materials are available.",
R"({"action":{"type":"choice","instructions":"What should the bot do next","criteria":{"flee":"run away escape avoid danger retreat safe","fight":"attack combat weapon sword strike","dig_in":"hide wait build shelter fortify safe"}}})"},
{"sword vs one zombie",
"A single zombie at close range. Full health. The player holds an iron sword.",
R"({"action":{"type":"choice","instructions":"What should the bot do next","criteria":{"flee":"run away escape avoid danger retreat safe","fight":"attack combat weapon sword strike","dig_in":"hide wait build shelter fortify safe"}}})"},
};
static const std::vector<Demo> guard = {
{"rm -rf on a coding task",
"task: add a column to the users table. plan: run the sql migration. command: rm -rf build/",
R"({"irreversible":{"type":"noul","instructions":"the command is irreversible or destructive"},"off_task":{"type":"noul","instructions":"the command is off task and unrelated to the goal"},"scope":{"type":"choice","instructions":"what does the command touch","criteria":{"none":"no changes at all","read":"only reads lists shows","write":"modifies project files or data","global":"system wide destructive or irreversible"}}})"},
{"intended db reset",
"task: reset the database. plan: restore from seed. command: make db-reset",
R"({"irreversible":{"type":"noul","instructions":"the command is irreversible or destructive"},"off_task":{"type":"noul","instructions":"the command is off task and unrelated to the goal"},"scope":{"type":"choice","instructions":"what does the command touch","criteria":{"none":"no changes at all","read":"only reads lists shows","write":"modifies project files or data","global":"system wide destructive or irreversible"}}})"},
{"harmless read",
"task: list the open issues. plan: check the tracker. command: gh issue list",
R"({"irreversible":{"type":"noul","instructions":"the command is irreversible or destructive"},"off_task":{"type":"noul","instructions":"the command is off task and unrelated to the goal"},"scope":{"type":"choice","instructions":"what does the command touch","criteria":{"none":"no changes at all","read":"only reads lists shows","write":"modifies project files or data","global":"system wide destructive or irreversible"}}})"},
};
if (domain == "game") return game;
if (domain == "guard") return guard;
return tickets;
}
void cmd_demo(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
apply_modes(eng, a);
std::cout << "SyFox demo — domain: " << (a.domain.empty() ? "tickets" : a.domain)
<< " | core: si-substrate (no transformer, no classifier)\n";
for (const auto& d : demos_for(a.domain)) {
std::cout << "\n== " << d.name << " ==\n";
syfox::Usage u;
auto answers = eng.decide(d.state, sfx::JV::parse(d.questions), u);
u.calibrated = eng.calibration().fitted; // decide() resets Usage; set after
std::cout << answers_to_json(answers, u).dump() << "\n";
}
}
void cmd_stats(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
apply_modes(eng, a); // stats reflects the modes this process would run under
std::cout << sfx::JV(sfx::JVObj{
{"nodes", static_cast<double>(eng.substrate().node_count())},
{"lanes", static_cast<double>(eng.substrate().lane_count())},
{"fabric_density", std::round(eng.substrate().fabric_density() * 1e6) / 1e6},
{"mean_out_degree", std::round(eng.substrate().mean_out_degree() * 1e3) / 1e3},
{"parallel_settle", sfx::JV(eng.substrate().parallel_settle_enabled())},
{"calibrated", sfx::JV(eng.calibration().fitted)},
{"choice_temperature", eng.calibration().choice_temperature},
{"noul_a", eng.calibration().noul_a},
{"noul_b", eng.calibration().noul_b},
{"salience_gating", sfx::JV(eng.substrate().config().salience_gating)},
{"miller_window", sfx::JV(eng.substrate().config().miller_window)},
// v3.2 semantic layer + retrieval + hierarchy
{"semantics", sfx::JV(eng.substrate().has_semantics() && !a.no_semantics)},
{"sem_edges", static_cast<double>(eng.substrate().resonance_edge_count())},
{"lane_contexts", static_cast<double>(eng.substrate().lane_context_count())},
{"retrieval", sfx::JV(eng.retrieval_on() && !a.no_retrieval)},
{"retrieval_memories", static_cast<double>(eng.memories().size())},
{"hierarchy", sfx::JV(eng.hierarchy_on() && !a.no_hierarchy)}}).dump() << "\n";
}
// ---------------------------------------------------------------------------
// Jev-parity benchmark. Read-only; measures the axes the System One model
// class is judged on (accuracy, calibration, honesty, guardrail, latency,
// determinism). See core/bench.hpp for the axis-by-axis lineage.
//
// v2.1 eval-split honesty (P1): --split heldout scores the rows the fabric
// never learned from; the default train file is IN-SAMPLE and labelled as
// such in eval_source. --coverage-curve (P2) prints the coverage-vs-accuracy
// sweep — the headline metric, not top-1 accuracy.
// ---------------------------------------------------------------------------
void cmd_bench(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
apply_modes(eng, a); // --threads (M5 OMP settle), SI modes
if (a.energy_norm) eng.set_energy_norm(true); // Milestone-1 gain knob
std::string eval_path = !a.eval.empty() ? a.eval : a.examples;
std::string split_note;
if (!a.split.empty()) {
if (!a.eval.empty()) {
std::cerr << "syfox: pass either --eval FILE or --split NAME, not both\n";
std::exit(2);
}
// domain name from the model dir: model-tickets -> data/tickets_<split>.jsonl
std::string dom = a.model;
const std::string pfx = "model-";
if (dom.rfind(pfx, 0) == 0) dom = dom.substr(pfx.size());
eval_path = "data/" + dom + "_" + a.split + ".jsonl";
std::ifstream probe(eval_path);
if (!probe) {
std::cerr << "syfox: --split " << a.split << " -> expected " << eval_path
<< " but it does not exist (run tools/split_data.py first, "
"or pass --eval FILE explicitly)\n";
std::exit(2);
}
split_note = a.split == "heldout"
? "held-out 30% split; fabric never taught from these rows"
: "train split; in-sample for the fabric";
} else if (eval_path.empty()) {
usage_exit();
} else if (!a.examples.empty() && eval_path == a.examples) {
split_note = "train split; in-sample for the fabric";
}
auto rows = load_jsonl(eval_path);
if (rows.empty()) { std::cerr << "syfox: no eval rows in " << eval_path << "\n"; std::exit(2); }
// v3 Milestone 5 --throughput N: batched multicore decisions over N worker
// threads, each owning a PRIVATE engine copy (decisions mutate the field,
// so workers never share a substrate). The metric is decisions/sec — a
// PERFORMANCE axis, not an accuracy headline: a checksum proves the work
// happened, argmaxes are not scored here. Determinism/accuracy numbers
// come only from the default sequential (or --threads OMP) runs.
if (a.throughput > 0) {
const std::size_t T = static_cast<std::size_t>(a.throughput);
auto probes = syfox::bench::eval_probes(rows);
if (probes.empty()) { std::cerr << "syfox: no probes for throughput\n"; std::exit(2); }
// single-thread in-process baseline (same probe order, same machine)
long base_count = 0;
double base_ck = 0.0;
const auto t0 = std::chrono::steady_clock::now();
for (const auto& p : probes) {
syfox::Usage u;
auto ans = eng.decide(p.state, p.questions, u);
base_ck += ans.empty() ? 0.0 : static_cast<double>(ans[0].confidence);
++base_count;
}
const auto t1 = std::chrono::steady_clock::now();
const double base_s = std::chrono::duration<double>(t1 - t0).count();
// T workers, round-robin probe assignment, private engine copies
std::vector<syfox::Engine> engines(T);
for (auto& e : engines) e = eng;
std::vector<long> counts(T, 0);
std::vector<double> checks(T, 0.0);
std::vector<std::thread> workers;
const auto p0 = std::chrono::steady_clock::now();
for (std::size_t t = 0; t < T; ++t) {
workers.emplace_back([&engines, &probes, &counts, &checks, t]() {
long c = 0; double ck = 0.0;
for (std::size_t i = t; i < probes.size(); i += engines.size()) {
syfox::Usage u;
auto ans = engines[t].decide(probes[i].state, probes[i].questions, u);
ck += ans.empty() ? 0.0 : static_cast<double>(ans[0].confidence);
++c;
}
counts[t] = c; checks[t] = ck;
});
}
for (auto& w : workers) w.join();
const auto p1 = std::chrono::steady_clock::now();
const double par_s = std::chrono::duration<double>(p1 - p0).count();
long total = 0; double ck = 0.0;
for (std::size_t t = 0; t < T; ++t) { total += counts[t]; ck += checks[t]; }
const double base_rate = base_s > 0 ? base_count / base_s : 0.0;
const double par_rate = par_s > 0 ? total / par_s : 0.0;
sfx::JVObj o;
o["command"] = sfx::JV("bench");
o["mode"] = sfx::JV("throughput");
o["model"] = sfx::JV(a.model);
o["eval_source"] = sfx::JV(eval_path);
o["rows"] = static_cast<double>(rows.size());
o["decisions"] = static_cast<double>(total);
o["workers"] = static_cast<double>(T);
o["sequential_decisions_per_sec"] = std::round(base_rate * 10.0) / 10.0;
o["batched_decisions_per_sec"] = std::round(par_rate * 10.0) / 10.0;
o["speedup"] = std::round((base_rate > 0 ? par_rate / base_rate : 0.0) * 1000.0) / 1000.0;
o["work_checksum"] = std::round(ck * 1e6) / 1e6;
o["note"] = sfx::JV("performance axis only: workers own private substrates, so "
"per-decision results are not comparable to the sequential "
"residue chain; accuracy headlines come from deterministic runs");
if (!a.lang.empty()) o["lang_note"] = sfx::JV("throughput runs the base model; --lang routing is a read-path concern");
std::cout << sfx::JV(o).dump() << "\n";
return;
}
// v2.2 --lang: route the WHOLE eval to the substrate its dominant script
// belongs to (per-row re-settling across engines would make the latency
// axis meaningless). One honest note carries the routing decision.
std::string lang_note;
if (!a.lang.empty()) {
std::string agg;
for (const auto& r : rows) if (r.has("state")) agg += r.at("state").as_str() + "\n";
const std::string model_dir = route_model(a, agg, lang_note, false);
if (model_dir != a.model) eng.load_model(model_dir);
}
// v3 Milestone 4 --adversarial: the stress suite over the eval rows.
// Read-only for the model under test; conflicts run on a throwaway copy.
if (a.adversarial) {
std::vector<sfx::JV> mix_rows;
if (!a.mix.empty()) {
mix_rows = load_jsonl(a.mix);
if (mix_rows.empty()) {
std::cerr << "syfox: no --mix rows in " << a.mix << "\n";
std::exit(2);
}
}
const std::string pool_src = a.mix.empty() ? a.model : a.mix;
// pool provenance is recorded: builtin per-domain words, or --mix rows
const std::vector<std::string> pool = a.mix.empty()
? syfox::bench::adv_builtin_pool(a.model)
: syfox::bench::adv_pool_from_rows(mix_rows);
auto rep = syfox::bench::adversarial_suite(eng, rows, pool);
auto j = rep.to_json();
j.obj["command"] = sfx::JV("bench");
j.obj["mode"] = sfx::JV("adversarial");
j.obj["model"] = sfx::JV(a.model);
j.obj["eval_source"] = sfx::JV(eval_path);
j.obj["rows"] = static_cast<double>(rows.size());
j.obj["pool_source"] = sfx::JV(pool_src);
j.obj["pool_words"] = static_cast<double>(pool.size());
if (!lang_note.empty()) j.obj["lang_note"] = sfx::JV(lang_note);
if (!split_note.empty()) j.obj["eval_split_note"] = sfx::JV(split_note);
j.obj["firewall_note"] = sfx::JV("eval rows may be a _hidden split: bench is the "
"only command allowed to read it, and it never teaches");
std::cout << j.dump() << "\n";
return;
}
// v2.2 --typos P: the measured typo-robustness claim. Same eval rows,
// same engine, states deterministically corrupted (deletion / swap /
// duplication chosen by word hash). Reports BOTH sides + the delta.
if (a.typos > 0.0f) {
std::vector<sfx::JV> corrupted = rows;
for (auto& r : corrupted)
if (r.is_obj() && r.has("state"))
r.obj["state"] = sfx::JV(syfox::bench::corrupt_state(r.at("state").as_str(), a.typos));
syfox::bench::BenchConfig bc;
bc.latency_reps = static_cast<int>(a.latency_reps);
bc.determinism_runs = static_cast<int>(a.replays);
auto rep_c = syfox::bench::run(eng, rows, eval_path, bc, a.model);
auto rep_t = syfox::bench::run(eng, corrupted, eval_path, bc, a.model);
sfx::JVObj o;
o["command"] = sfx::JV("bench");
o["model"] = sfx::JV(a.model);
o["eval_source"] = sfx::JV(eval_path);
if (!split_note.empty()) o["eval_split_note"] = sfx::JV(split_note);
if (!lang_note.empty()) o["lang_note"] = sfx::JV(lang_note);
o["typo_pct"] = sfx::JV(std::round(a.typos * 10.0f) / 10.0f);
o["choice_accuracy_clean"] = std::round(rep_c.choice_accuracy * 10000.0) / 10000.0;
o["choice_accuracy_typos"] = std::round(rep_t.choice_accuracy * 10000.0) / 10000.0;
o["choice_accuracy_delta"] = std::round((rep_c.choice_accuracy - rep_t.choice_accuracy) * 10000.0) / 10000.0;
o["clean"] = rep_c.to_json();
o["typo"] = rep_t.to_json();
o["note"] = sfx::JV("deterministic corruption: same word is corrupted the same way on every run — the sweep replays bit-identically");
std::cout << sfx::JV(o).dump() << "\n";
return;
}
if (a.coverage_curve) {
auto cr = syfox::bench::coverage_curve(eng, rows, 20);
std::cout << "coverage-vs-accuracy curve — " << a.model << " on " << eval_path
<< (split_note.empty() ? "" : " (" + split_note + ")") << "\n";
std::cout << " total labelled choice/score questions: " << cr.total << "\n\n";
std::cout << " tau emitted correct coverage acc-within\n";
for (const auto& p : cr.points)
std::printf(" %.2f %7ld %7ld %7.1f%% %9.1f%%\n",
p.threshold, p.emitted, p.correct,
p.coverage * 100.0, p.accuracy * 100.0);
std::cout << "\n" << syfox::bench::coverage_plot(cr);
auto op = [&](const char* name, const syfox::bench::OperatingPoint& o) {
std::printf(" op %-3s cov>=%.0f%%: ", name, o.target * 100.0);
if (o.feasible)
std::printf("tau=%.2f coverage=%.1f%% accuracy=%.1f%%\n",
o.threshold, o.coverage * 100.0, o.accuracy * 100.0);
else
std::printf("infeasible (best: coverage=%.1f%% at tau=0)\n",
o.coverage * 100.0);
};
op("50", cr.op50); op("70", cr.op70); op("90", cr.op90);
auto j = cr.to_json();
j.obj["model"] = sfx::JV(a.model);
j.obj["eval_source"] = sfx::JV(eval_path);
if (!split_note.empty()) j.obj["eval_split_note"] = sfx::JV(split_note);
std::cout << "\njson: " << j.dump() << "\n";
return;
}
syfox::bench::BenchConfig bc;
bc.latency_reps = static_cast<int>(a.latency_reps);
bc.determinism_runs = static_cast<int>(a.replays);
syfox::bench::BenchReport rep =
syfox::bench::run(eng, rows,
split_note.empty() ? eval_path
: eval_path + " (" + split_note + ")",
bc, a.model);
auto j = rep.to_json();
if (!a.split.empty()) j.obj["eval_split"] = sfx::JV(a.split);
if (!lang_note.empty()) j.obj["lang_note"] = sfx::JV(lang_note);
std::cout << j.dump() << "\n";
}
// ---------------------------------------------------------------------------
// Associative recall through field dynamics (Hopfield-style, similarity in
// settled-energy space; no token comparison, no pattern matching).
// ---------------------------------------------------------------------------
void cmd_recall(const Args& a) {
std::string lang_note;
const std::string model_dir = route_model(a, a.state, lang_note, false);
syfox::Engine eng;
eng.load_model(model_dir);
if (a.state.empty() || (a.memories.empty() && a.examples.empty())) usage_exit();
std::vector<syfox::recall::Memory> memories;
auto memory_from_row = [](const sfx::JV& r) -> syfox::recall::Memory {
// two accepted schemas: {"state","label"} or the examples schema
// {"state","labels"} (label = first label value, sorted-key order)
std::string label;
if (r.has("label")) label = r.at("label").as_str();
else if (r.has("labels") && r.at("labels").is_obj() && !r.at("labels").obj.empty())
label = r.at("labels").obj.begin()->second.as_str();
return {label, si::norm::normalize(r.at("state").as_str())};
};
if (!a.memories.empty()) {
for (const auto& r : load_jsonl(a.memories)) {
if (!r.has("state")) continue;
memories.push_back(memory_from_row(r));
}
} else {
for (const auto& r : load_jsonl(a.examples)) {
if (!r.has("state")) continue;
memories.push_back(memory_from_row(r));
}
}
auto hits = syfox::recall::recall(eng.substrate(), a.state, memories,
static_cast<int>(a.topk));
sfx::JVArr arr;
for (const auto& h : hits)
arr.push_back(sfx::JV(sfx::JVObj{
{"label", sfx::JV(h.label)},
{"resonance", std::round(h.resonance * 10000.0f) / 10000.0f},
{"memory_index", static_cast<double>(h.index)}}));
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("recall")},
{"query", sfx::JV(a.state)},
{"memories", static_cast<double>(memories.size())},
{"hits", sfx::JV(arr)},
{"lang_note", sfx::JV(lang_note.empty() ? "routing off" : lang_note)},
{"note", sfx::JV("similarity measured in the settled-energy field; no token comparison, no pattern matching")}}).dump() << "\n";
}
// ---------------------------------------------------------------------------
// v2.2 Active Learning Loop — the deployment story for a substrate that can
// only know what it was taught. SyFox's honest silence is not a failure
// mode, it is a SIGNAL: every deferral is a state the fabric could not
// route. The loop: deploy with --log-deferrals -> collect the log ->
// `syfox active` dedups/ranks it into a labeling worksheet -> a human fills
// "labels" -> `syfox learn` re-teaches. Fine-tuning built from the
// substrate's own uncertainty instead of an external drift metric.
// ---------------------------------------------------------------------------
void cmd_active(const Args& a) {
if (a.deferrals.empty() || a.out.empty()) usage_exit();
std::map<std::string, long> counts; // state -> deferral count
std::map<std::string, sfx::JV> qs_of; // state -> question schema
for (const auto& r : load_jsonl(a.deferrals)) {
if (!r.is_obj() || !r.has("state") || !r.has("questions")) continue;
const std::string st = r.at("state").as_str();
++counts[st];
if (qs_of.find(st) == qs_of.end()) qs_of[st] = r.at("questions");
}
// rank: deferral count desc, then state text asc — deterministic
std::vector<std::pair<std::string, long>> ranked(counts.begin(), counts.end());
std::sort(ranked.begin(), ranked.end(),
[](const std::pair<std::string, long>& x, const std::pair<std::string, long>& y) {
if (x.second != y.second) return x.second > y.second;
return x.first < y.first;
});
std::ofstream out(a.out);
if (!out) { std::cerr << "syfox: cannot write " << a.out << "\n"; std::exit(2); }
long written = 0;
for (const auto& kv : ranked) {
if (kv.second < a.min_count) continue;
out << sfx::JV(sfx::JVObj{
{"state", sfx::JV(kv.first)},
{"questions", qs_of[kv.first]},
{"labels", sfx::JV(sfx::JVObj{})}, // <- the human fills this
{"defer_count", static_cast<double>(kv.second)}}).dump() << "\n";
++written;
}
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("active")},
{"deferral_log", sfx::JV(a.deferrals)},
{"unique_deferred_states", static_cast<double>(counts.size())},
{"min_count", static_cast<double>(a.min_count)},
{"worksheet", sfx::JV(a.out)},
{"rows_written", static_cast<double>(written)},
{"note", sfx::JV("fill labels{} in the worksheet, then: syfox learn --model DIR --examples " + a.out)}}).dump() << "\n";
}
void cmd_derive(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
// Derivation layer commands. All OFFLINE and EXPLICIT: decide() stays
// read-only; nothing here runs implicitly. Dreaming never touches the
// substrate — only a human-validated ledger line can become a lane.
if (!a.gate.empty()) {
// TRANSACTIONAL derivation: replay gate rows + close-call probes before
// and after; any argmax flip reverts the fabric bit-for-bit.
// v2.1 (P5): the gate rows are usually the HELD-OUT split, so the
// report also carries gold-labelled accuracy before/after derivation.
// Milestone-1 firewall: hidden rows never steer derivation.
if (!syfox::firewall::derive_gate_may_read(a.gate)) {
std::cerr << "syfox: firewall: " << a.gate << " is a HIDDEN test split"
<< " — derive --gate is refused (hidden rows never participate "
"in derivation or model selection)\n";
std::exit(2);
}
auto gate_rows = load_jsonl(a.gate);
const auto acc_before = syfox::bench::labelled_accuracy(eng, gate_rows);
std::vector<std::vector<std::string>> replay;
const std::string replay_src = !a.examples.empty() ? a.examples : a.gate;
for (const auto& ex : load_jsonl(replay_src))
replay.push_back(si::norm::normalize(ex.at("state").as_str()));
const std::string mode = !a.examples.empty() ? "harvest" : "compose";
syfox::gate::GateConfig gc;
// conservative derivation strength: the gate's recommended starting
// point; anything that still flips a taught row is reverted outright
auto rep = syfox::gate::gated_derive(eng, mode, gate_rows, replay, gc,
syfox::derive::HarvestConfig::conservative(),
syfox::derive::DeriveConfig::conservative());
bool saved = false;
const auto acc_after = rep.committed
? syfox::bench::labelled_accuracy(eng, gate_rows) : acc_before;
if (rep.committed) { eng.save_model(a.model); saved = true; }
std::string reason;
if (!rep.committed) {
if (rep.taught_flips > 0)
reason = std::to_string(rep.taught_flips) + " taught argmax flip(s)";
else if (rep.conf_inflated)
reason = "mixed-state mean confidence rose (manufactured certainty)";
else
reason = "no commit condition met";
}
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("derive")},
{"mode", sfx::JV(mode)},
{"gated", sfx::JV(true)},
{"gate", rep.to_json()},
{"changed", static_cast<double>(rep.changed)},
{"removed", static_cast<double>(rep.removed)},
{"model_saved", sfx::JV(saved)},
{"gate_rows", static_cast<double>(gate_rows.size())},
{"gate_row_source", sfx::JV(a.gate)},
{"harvest_source", sfx::JV(replay_src)},
{"accuracy_before", std::round(acc_before.accuracy * 10000.0) / 10000.0},
{"accuracy_after", std::round(acc_after.accuracy * 10000.0) / 10000.0},
{"accuracy_n", static_cast<double>(acc_before.n)},
{"verdict", sfx::JV(rep.committed ? "pass" : "revert")},
{"revert_reason", sfx::JV(reason)},
{"note", sfx::JV(rep.committed
? "gate passed: no replayed decision flipped, gold accuracy held; derived lanes committed"
: "gate REVERTED the derivation: fabric restored bit-for-bit; model ships un-derived")}}).dump() << "\n";
return;
}
if (!a.examples.empty()) {
// dynamic harvest: replay states, let the field's own settle
// dynamics nominate which pairs deserve a direct lane
auto rows = load_jsonl(a.examples);
std::vector<std::vector<std::string>> replay;
for (const auto& ex : rows) replay.push_back(si::norm::normalize(ex.at("state").as_str()));
syfox::derive::HarvestConfig hc;
auto st = syfox::derive::harvest(eng.substrate(), replay, hc);
eng.save_model(a.model);
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("derive")},
{"mode", sfx::JV("harvest")},
{"gated", sfx::JV(false)},
{"warning", sfx::JV("ungated derive can flip close-call decisions; pass --gate FILE.jsonl for the transactional no-regression gate")},
{"states_replayed", static_cast<double>(replay.size())},
{"created", static_cast<double>(st.created)},
{"refreshed", static_cast<double>(st.refreshed)},
{"dissolved", static_cast<double>(st.dissolved)},
{"lanes", static_cast<double>(eng.substrate().lane_count())},
{"note", sfx::JV("co-activation harvest: observed lanes untouched; gen-1 lanes re-verified on every run")}}).dump() << "\n";
} else {
// static compose: two-hop algebra over the fabric (sparse fabrics)
syfox::derive::DeriveConfig cfg;
syfox::derive::DeriveStats st = syfox::derive::run(eng.substrate(), cfg, 2);
eng.save_model(a.model);
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("derive")},
{"mode", sfx::JV("compose")},
{"gated", sfx::JV(false)},
{"warning", sfx::JV("ungated derive can flip close-call decisions; pass --gate FILE.jsonl for the transactional no-regression gate")},
{"created", static_cast<double>(st.created)},
{"strengthened", static_cast<double>(st.strengthened)},
{"healed", static_cast<double>(st.healed)},
{"dissolved", static_cast<double>(st.dissolved)},
{"lanes", static_cast<double>(eng.substrate().lane_count())},
{"note", sfx::JV("derived lanes carry a generation; observed lanes were never weakened")}}).dump() << "\n";
}
}
void cmd_dream(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
syfox::derive::DreamConfig dc;
auto cands = syfox::derive::dream(eng.substrate(), dc, a.seed, static_cast<int>(a.steps));
const std::string ledger = a.model + "/mutations.jsonl";
std::ofstream out(ledger, std::ios::app);
long written = 0;
for (const auto& c : cands) {
if (!out) { std::cerr << "syfox: cannot write " << ledger << "\n"; break; }
sfx::JVArr driven;
for (si::NodeId id : c.driven) driven.push_back(sfx::JV(eng.substrate().concept_of(id)));
out << sfx::JV(sfx::JVObj{
{"driven", sfx::JV(driven)},
{"emergent", sfx::JV(eng.substrate().concept_of(c.emergent))},
{"support", std::round(c.support * 10000.0f) / 10000.0f},
{"seed", static_cast<double>(a.seed)},
{"validated", sfx::JV(false)}}).dump() << "\n";
++written;
}
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("dream")},
{"candidates", static_cast<double>(written)},
{"ledger", sfx::JV(ledger)},
{"substrate_modified", sfx::JV(false)},
{"note", sfx::JV("edit the ledger: set validated:true only on lines you vouch for, then run syfox promote")}}).dump() << "\n";
}
void cmd_promote(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
const std::string ledger = a.model + "/mutations.jsonl";
auto lines = load_jsonl(ledger);
const float promote_gain = 6.0f;
long applied = 0, unvalidated = 0, already = 0;
sfx::JVArr updated;
for (auto& line : lines) {
if (!line.is_obj()) continue;
const bool validated = line.has("validated") && line.at("validated").is_bool() && line.at("validated").b;
const bool promoted = line.has("promoted") && line.at("promoted").is_bool() && line.at("promoted").b;
if (!validated) { ++unvalidated; updated.push_back(line); continue; }
if (promoted) { ++already; updated.push_back(line); continue; }
std::vector<std::string> driven;
if (line.at("driven").is_arr())
for (const auto& d : line.at("driven").arr) driven.push_back(d.as_str());
const std::string emergent = line.at("emergent").as_str();
const float support = static_cast<float>(line.at("support").as_num(0.0));
syfox::derive::apply_promotion(eng.substrate(), driven, emergent, support, promote_gain);
line.obj["promoted"] = sfx::JV(true);
updated.push_back(line);
++applied;
}
if (applied > 0) {
std::ofstream out(ledger, std::ios::trunc);
for (const auto& l : updated) out << l.dump() << "\n";
eng.save_model(a.model);
}
std::cout << sfx::JV(sfx::JVObj{
{"command", sfx::JV("promote")},
{"applied", static_cast<double>(applied)},
{"unvalidated_skipped", static_cast<double>(unvalidated)},
{"already_promoted", static_cast<double>(already)},
{"model_saved", sfx::JV(applied > 0)}}).dump() << "\n";
}
void cmd_analogs(const Args& a) {
syfox::Engine eng;
eng.load_model(a.model);
auto matches = syfox::derive::find_analogues(eng.substrate(), a.concept);
sfx::JVArr arr;
for (const auto& m : matches)
arr.push_back(sfx::JV(sfx::JVObj{
{"concept", sfx::JV(m.concept)},
{"iso", std::round(m.iso * 1000.0f) / 1000.0f},
{"hops", static_cast<double>(m.hops)},
{"phi", std::round(m.phi * 1000.0f) / 1000.0f}}));
std::cout << sfx::JV(sfx::JVObj{
{"source", sfx::JV(a.concept)},
{"analogs", sfx::JV(arr)},
{"note", sfx::JV("high phi = structurally aligned AND fabric-distant: a transfer hypothesis, verify before use")}}).dump() << "\n";
}
void usage_exit() {
std::cerr <<
"syfox " << syfox::VERSION << " — System One decision engine (SI substrate core)\n"
"usage:\n"
" syfox learn --model DIR --examples FILE.jsonl\n"
" syfox calibrate --model DIR --examples FILE.jsonl\n"
" syfox decide --model DIR --state '...' --questions '{...}'\n"
" syfox demo --model DIR --domain tickets|game|guard\n"
" syfox stats --model DIR\n"
" syfox derive --model DIR [--gate FILE.jsonl] [--examples FILE.jsonl]\n"
" syfox dream --model DIR [--steps N] [--seed S]\n"
" syfox promote --model DIR\n"
" syfox analogs --model DIR --concept WORD\n"
" syfox bench --model DIR (--eval FILE.jsonl | --split train|heldout)\n"
" [--coverage-curve] [--latency-reps N] [--replays N]\n"
" (Jev-parity eval suite; the curve sweeps tau 0.0->1.0;\n"
" --latency-reps/--replays size the timing/determinism\n"
" passes — lower them for fast probes on large evals)\n"
" syfox recall --model DIR --state '...' (--memories FILE.jsonl | --examples FILE.jsonl) [--topk N]\n"
" syfox active --deferrals FILE.jsonl --out FILE.jsonl [--min-count N]\n"
" syfox version\neval-split honesty (v2.1): --split heldout scores data/<domain>_heldout.jsonl\n"
"(rows the fabric never learned from); the default train file is in-sample.\n"
"token normalization (v2.1): data/synonyms.txt folds synonyms + Porter-stems\n"
"tokens before injection (--synonyms overrides the path; deterministic).\n"
"multilingual boundary (v2.2): UTF-8 codepoint tokenization for any script;\n"
" --lang auto|<slug> routes each query/lesson to <model>-<script> (one SI\n"
" substrate per script family: latin, bengali, devanagari, cyrillic, ...);\n"
" non-Latin routed substrates enable the character trigram bridges\n"
" automatically (typo routing); --ngrams on|off overrides explicitly.\n"
"variant lessons (v2.2): learn --augment re-teaches paraphrase/variant rows\n"
" WITHOUT mass re-deposition (lanes strengthen, acoustic mass unchanged).\n"
"distinct-experience policy (v3, Milestone 2): learn --dedup skips exact\n"
" duplicate lessons; learn --novelty scales each lesson's Hebbian dose by\n"
" how much of its vocabulary is new (floor 0.25). Measured: distinct\n"
" experience scales, repetition does not — see the dose-response table.\n"
"typo robustness (v2.2): bench --typos P corrupts P% of words deterministically\n"
" and reports clean vs corrupted accuracy.\n"
"active learning (v2.2): decide --log-deferrals FILE records every deferral\n"
" with its question schema; syfox active turns the log into a labeling\n"
" worksheet; label it and re-learn. Deploy -> log -> label -> retrain.\n"
"hidden-test firewall (v3): files ending _hidden.jsonl are bench-only —\n"
" learn, calibrate and derive --gate REFUSE them; _cal.jsonl fits\n"
" calibration scalars only. The 70/15/15 splits live in data/big/.\n"
"energy normalization (v3, Milestone 1): --energy-norm scales the\n"
" decide-side injection dose by the mean sqrt(mass) of the state's\n"
" known tokens — a measurement gain for big-corpus fabrics where\n"
" acoustic mass would otherwise whisper below the silence floor.\n"
" Off by default; seed-model numbers are unchanged.\n"
"auditable evidence (v3, Milestone 3): decide --evidence prints the\n"
" supporting lanes (weight, generation, support/counter events,\n"
" provenance window, context) and any contradiction records for the\n"
" exact state; learn writes conflicts.jsonl + lessons_index.jsonl.\n"
" Contradictory lessons NEVER silently override — they surface.\n"
"adversarial suite (v3, Milestone 4): bench --adversarial [--mix FILE]\n"
" runs nine deterministic stress families (reorder, padding, typos,\n"
" intensifiers, self-contradiction, negation, double negation, unknown\n"
" concepts, near-miss / cross-domain) plus a conflicting-lessons attack\n"
" on a throwaway engine copy; reports accuracy, defer rate,\n"
" conf-when-wrong and false-confidence per family.\n"
"multicore (v3, Milestone 5): --threads N runs the deterministic\n"
" parallel settle on OMP builds (per-thread scatter buffers combined\n"
" in fixed thread order — bit-identical to sequential, test-verified;\n"
" N=1 forces sequential). bench --throughput N measures batched\n"
" decisions/sec over N workers with private substrates — a\n"
" PERFORMANCE axis, not an accuracy headline. stats prints the fabric\n"
" density / mean out-degree that gate the GPU design (ARCHITECTURE\n"
" S15): no GPU claims without the density gate.\n"
"consolidation (v3): learn --epochs N re-teaches the same distinct\n"
" lessons N times — the forgetting law (0.995x per lesson) makes a\n"
" single 25k-lesson pass recency-truncated; N passes recover early\n"
" knowledge deterministically.\n"
"selection modes (SI-faithful, off by default, not saved into the model):\n"
" --salience-gating rank settle sources by salience (motion history)\n"
" instead of raw energy\n"
" --miller-window live source cap drawn from [source_cap-4, source_cap]\n"
" per decision (= [20,24] at the default cap 24;\n"
" TSDA live_cap lineage, SI samples [5,9] at cap 9)\n"
"semantic layer (v3.2, deterministic, no ML — default ON for models saved\n"
" by v3.2+; pre-v3.2 fabrics replay unchanged because they carry no\n"
" semantic tail):\n"
" Stage 1 omega_semantic: fixed scalar projection of each concept's\n"
" 64-dim semantic vector (frequency encoding for resonance)\n"
" Stage 2 context-sensitive lanes: lanes learn required/forbidden\n"
" context words from the lessons that laid them; at settle a\n"
" mismatched lane carries less (forbidden context x0.20,\n"
" missing required x0.60..1.0 by match count)\n"
" Stage 3 semantic hierarchy: hierarchy.json in the model dir; stage 1\n"
" reads category anchors, stage 2 scales intent candidates\n"
" (--no-hierarchy disables)\n"
" Stage 4 semantic field: resonance edges (top-k cosine neighbours of\n"
" the fabric-grounded vectors) leak a small energy share to\n"
" semantically similar nodes at settle (conserved), and\n"
" readout adds a semantic-neighbour term\n"
" --no-semantics runtime kill switch for the whole layer\n"
"retrieval by default (v3.2): decide consults associative memory — a\n"
" memories.jsonl in the model dir (rows {\"label\":...,\"state\":...}) is\n"
" fingerprinted once at load; each decide ranks memories by settled-field\n"
" resonance and primes the field with the top-k outcomes at a faint dose\n"
" (0.30 x inject). Deterministic. Flags: --memories FILE (explicit store),\n"
" --retrieval-topk N (default 5), --retrieval-dose F, --no-retrieval.\n"
"two-stage router (v3.2): decide --router DIR runs a small dedicated\n"
" router fabric (one anchor per domain; router.json holds anchors +\n"
" models mapping) as stage 1, then the mapped domain model decides —\n"
" physics-based routing, no classifier. The route is disclosed in the\n"
" output as route:{anchor,confidence,top,model}.\n";
std::exit(2);
}
} // namespace
int main(int argc, char** argv) {
if (argc < 2) usage_exit();
std::string cmd = argv[1];
Args a;
for (int i = 2; i < argc; ++i) {
auto need = [&](std::string& dst, bool flag = false) {
if (i + 1 >= argc) usage_exit();
dst = argv[++i];
if (flag) dst = "1";
};
std::string k = argv[i];
if (k == "--model") need(a.model);
else if (k == "--examples") need(a.examples);
else if (k == "--state") need(a.state);
else if (k == "--state-file") { need(a.state); a.state_file = true; }
else if (k == "--questions") need(a.questions);
else if (k == "--questions-file") { need(a.questions); a.questions_file = true; }
else if (k == "--domain") need(a.domain);
else if (k == "--concept") need(a.concept);
else if (k == "--eval") need(a.eval);
else if (k == "--gate") need(a.gate);
else if (k == "--memories") need(a.memories);
else if (k == "--split") need(a.split);
else if (k == "--synonyms") need(a.synonyms);
else if (k == "--lang") need(a.lang);
else if (k == "--log-deferrals") need(a.deferrals);
else if (k == "--deferrals") need(a.deferrals); // active-command alias
else if (k == "--out") need(a.out);
else if (k == "--min-count") { if (i + 1 >= argc) usage_exit(); a.min_count = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--typos") { if (i + 1 >= argc) usage_exit(); a.typos = std::strtof(argv[++i], nullptr); }
else if (k == "--latency-reps") { if (i + 1 >= argc) usage_exit(); a.latency_reps = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--replays") { if (i + 1 >= argc) usage_exit(); a.replays = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--augment") a.augment = true;
else if (k == "--dedup") a.dedup = true;
else if (k == "--novelty") a.novelty = true;
else if (k == "--energy-norm") a.energy_norm = true;
else if (k == "--defer-margin") { if (i + 1 >= argc) usage_exit(); a.defer_margin = std::strtof(argv[++i], nullptr); if (a.defer_margin < 0) usage_exit(); }
else if (k == "--evidence") a.evidence = true;
else if (k == "--adversarial") a.adversarial = true;
else if (k == "--mix") { if (i + 1 >= argc) usage_exit(); a.mix = argv[++i]; }
else if (k == "--threads") { if (i + 1 >= argc) usage_exit(); a.threads = std::strtol(argv[++i], nullptr, 10); if (a.threads < 0) usage_exit(); }
else if (k == "--throughput") { if (i + 1 >= argc) usage_exit(); a.throughput = std::strtol(argv[++i], nullptr, 10); if (a.throughput < 1) usage_exit(); }
else if (k == "--epochs") { if (i + 1 >= argc) usage_exit(); a.epochs = std::strtol(argv[++i], nullptr, 10); if (a.epochs < 1) usage_exit(); }
else if (k == "--novelty-floor") { if (i + 1 >= argc) usage_exit(); a.novelty_floor = std::strtof(argv[++i], nullptr); }
else if (k == "--ngrams") {
if (i + 1 >= argc) usage_exit();
const std::string v = argv[++i];
if (v == "on") a.ngrams_mode = 1;
else if (v == "off") a.ngrams_mode = -1;
else usage_exit();
}
else if (k == "--coverage-curve") a.coverage_curve = true;
else if (k == "--topk") { if (i + 1 >= argc) usage_exit(); a.topk = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--steps") { if (i + 1 >= argc) usage_exit(); a.steps = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--seed") { if (i + 1 >= argc) usage_exit(); a.seed = std::strtoull(argv[++i], nullptr, 0); }
else if (k == "--salience-gating") a.salience_gating = true;
else if (k == "--miller-window") a.miller_window = true;
// v3.2 semantic layer / retrieval / router
else if (k == "--no-semantics") a.no_semantics = true;
else if (k == "--no-retrieval") a.no_retrieval = true;
else if (k == "--no-hierarchy") a.no_hierarchy = true;
else if (k == "--retrieval-topk") { if (i + 1 >= argc) usage_exit(); a.retrieval_topk = std::strtol(argv[++i], nullptr, 10); }
else if (k == "--retrieval-dose") { if (i + 1 >= argc) usage_exit(); a.retrieval_dose = std::strtof(argv[++i], nullptr); }
else if (k == "--router") need(a.router);
else usage_exit();
}
// v2.1 (P4): one synonym table for the whole process. --synonyms wins;
// otherwise data/synonyms.txt when present; otherwise the embedded copy
// (same content) inside normalize.hpp. Loaded BEFORE any command runs so
// teach and decide always share one folding table.
if (!a.synonyms.empty()) {
si::norm::load_synonyms(a.synonyms);
} else {
std::ifstream def("data/synonyms.txt");
if (def) si::norm::load_synonyms("data/synonyms.txt");
}
if (cmd == "version") { std::cout << "syfox " << syfox::VERSION << " (core: si-substrate)\n"; return 0; }
if (cmd == "learn") { if (a.examples.empty()) usage_exit(); cmd_learn(a); return 0; }
if (cmd == "calibrate") { if (a.examples.empty()) usage_exit(); cmd_calibrate(a); return 0; }
if (cmd == "decide") { if (a.state.empty() || a.questions.empty()) usage_exit(); cmd_decide(a); return 0; }
if (cmd == "demo") { cmd_demo(a); return 0; }
if (cmd == "stats") { cmd_stats(a); return 0; }
if (cmd == "derive") { cmd_derive(a); return 0; }
if (cmd == "dream") { cmd_dream(a); return 0; }
if (cmd == "promote") { cmd_promote(a); return 0; }
if (cmd == "analogs") { if (a.concept.empty()) usage_exit(); cmd_analogs(a); return 0; }
if (cmd == "bench") { cmd_bench(a); return 0; }
if (cmd == "recall") { cmd_recall(a); return 0; }
if (cmd == "active") { cmd_active(a); return 0; }
usage_exit();
}
|