diff --git a/variants/variant_branchless_flopcount.cpp b/variants/variant_branchless_flopcount.cpp
new file mode 100644
index 0000000..d903659
--- /dev/null
+++ b/variants/variant_branchless_flopcount.cpp
@@ -0,0 +1,426 @@
+// This file is merged from the forces.cpp and social_forces_baseline.cpp
+// The goal is that we from now on only have one file as our baseline
+// This should make it more easy in the future to implement optimizations
+// Our naming convention from now on is: variant_<variant_name>.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore)
+
+// # Changes: FLOPCOUNT
+// # Predecessor: variant_branchless.cpp
+
+// n_iter * [n_p ** 2 * (15 A + 24 M + 1 D + 4 S + 1 E) + n_p * (- 4 A - 4 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (13 A + 19 M + 1 D + 2 S + 1 E)]
+
+
+
+#include "../headers/board.hpp"
+#include "../headers/constants.hpp"
+#include "../headers/forces.hpp"
+#include "../headers/pedestrian.hpp"
+#include "../headers/social_force.hpp"
+#include "../headers/vector2d.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <math.h> // keep — forces code uses unqualified sqrt/exp
+#include <random>
+#include <tuple>
+#include <vector>
+
+#define SEED 42
+#define BOARD_WIDTH 10.0
+#define BOARD_LENGTH 50.0
+#define WALL_EPS 1e-3
+#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
+
+#ifndef RANDOM_SEED
+#define RANDOM_SEED 0
+#endif
+
+#ifndef BENCHMARK
+#define BENCHMARK 0
+#endif
+
+using namespace std;
+
+// forces.cpp
+
+// Flop count: 3 ADDs, 6 MULTs, 1 SQRT
+double w(Vector2d e, Vector2d f) {
+
+  // 1 ADD + 2 MULT
+  double dot = e.dot(f);
+  // 1 ADD + 2 MULT + 1 SQRT
+  double f_norm = f.norm();
+  // 1 MULT
+  double cond = (double)(dot >= f_norm * cos_phi); // Should be 1 if in the field of view, else 0
+  // 1 ADD + 1 MULT
+  return c_neg_times_dt + cond * (dt_neg - c_neg_times_dt);
+}
+
+// Flop count: 2 ADDs, 4 MULTs
+Vector2d acceleration_term_dt(const Pedestrian &alpha) {
+
+  // # 2 MULTs
+  Vector2d desired_velocity = alpha.e * alpha.desired_v0;
+
+  Vector2d actual_velocity = alpha.curr_vel;
+
+  // # 2 ADDs, 2 MULTs
+  Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
+
+  return f_alpha_0_dt;
+}
+
+// Flop count: 13 ADDs, 24 MULTs, 1 DIV, 4 SQRTs, 1 EXP
+Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta}$
+  Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
+  double r_ab_norm = r_ab.norm();
+  r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
+
+  // $v_\beta \Delta t \vec{e}_\beta$
+  Vector2d s_b_e = beta.s_b_e;
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
+  Vector2d r_ab2 = r_ab - s_b_e;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab2_norm = r_ab2.norm();
+  r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
+
+  // # 1 ADD
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
+
+  // # 1 ADD, 1 MULTs, 1 SQRT
+  //
+  // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
+  //
+  double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
+  two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
+  double two_b = sqrt(two_b_sq);
+
+  // # 5 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
+  //
+  double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
+
+  // # 2 ADDs, 4 MULTs
+  //
+  // $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
+  //
+  //
+  //          $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
+  //          v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  //
+  // $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
+  //
+  //
+  //       $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
+
+  // # 3 ADDs, 8 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
+  //
+  Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
+
+  return f_alpha_beta_dt;
+}
+
+// Flop count: 11 ADDs, 19 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
+Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
+
+  // # 7 ADDs, 5 MULTs
+  // $\vec{r}_{\alpha B}$
+  Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
+  double r_aB_norm = r_aB.norm();
+  r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
+
+  // # 2 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
+  //
+  double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
+
+  // # 2 MULTs
+  //
+  // $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
+  //
+  Vector2d f_alpha_B_neg = r_aB * scalar_mult;
+
+  // # 3 ADDs, 8 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
+  //
+  Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
+
+  return f_alpha_B_dt;
+}
+
+// social_force_baseline.cpp
+
+Pedestrian *init_pedestrians(int n_p) {
+#if RANDOM_SEED
+  random_device rd;  // Platform-provided random seed source.
+  mt19937 gen(rd()); // Non-reproducible initialization.
+#else
+  mt19937 gen(SEED); // Reproducible initialization.
+#endif
+
+  normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
+  uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
+  uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
+  uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
+
+  Pedestrian *P = new Pedestrian[n_p];
+
+  int n_p_half = n_p / 2;
+
+  // Group of pedestrians walking from left to right.
+  for (int i = 0; i < n_p_half; i++) {
+    double x = x_gen_left(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  // Group of pedestrians walking from right to left.
+  for (int i = n_p_half; i < n_p; i++) {
+    double x = x_gen_right(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(-30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  return P;
+}
+
+Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
+
+int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
+
+#if BENCHMARK
+  // Avoid compiler unused parameter warning
+  (void)dump_path;
+#endif
+
+#if !BENCHMARK
+  const int dump_interval = 1;
+
+  // Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
+  vector<tuple<int, int, double, double, double, double, int>> samples;
+
+  if (dump_path) {
+    samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
+
+    // Store the initial state before the first simulation step.
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+      samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+    }
+  }
+#endif
+
+  // n_iter * [ {n_p * {[(n_p - 1) * (13 ADDs, 24 MULTs, 1 DIV, 4 SQRTs, 1 EXP + 2 ADDs)] + [2 ADD + 4 MULT + (n_obstacles * (11 ADDs, 19 MULTs, 1 DIV, 2 SQRTs, 1 EXP) + n_obstacles * 2 ADDs) + 2 ADD ]}} + {(7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p} ]
+  // n_iter * [ {n_p * {[(n_p - 1) * (13 A + 24 M + 1 D + 4 S + 1 E + 2 A)] + [2 A + 4 M + (n_obstacles * (11 A + 19 M + 1 D + 2 S + 1 E) + n_obstacles * 2 A) + 2 A ]}} + {n_p * (7 A + 16 M + 2 D + 3 S)} ]
+  // n_iter * [n_p ** 2 * (15 A + 24 M + 1 D + 4 S + 1 E) + n_p * (- 11 A - 20 M - 1 D - 4 S - 1 E) + n_obstacles * n_p * (13 A + 19 M + 1 D + 2 S + 1 E) + n_p * (7 A + 16 M + 2 D + 3 S)]
+  // n_iter * [n_p ** 2 * (15 A + 24 M + 1 D + 4 S + 1 E) + n_p * (- 11 A - 20 M - 1 D - 4 S - 1 E) + n_p * (7 A + 16 M + 2 D + 3 S) + n_obstacles * n_p * (13 A + 19 M + 1 D + 2 S + 1 E)]
+  // n_iter * [n_p ** 2 * (15 A + 24 M + 1 D + 4 S + 1 E) + n_p * (- 4 A - 4 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (13 A + 19 M + 1 D + 2 S + 1 E)]
+
+
+  for (int t = 0; t < n_iter; t++) {
+    // Pass 1: Compute all forces from the current state.
+    // n_p * {[(n_p - 1) * (13 ADDs, 24 MULTs, 1 DIV, 4 SQRTs, 1 EXP + 2 ADDs)] + [(2 ADD + 4 MULT + (n_obstacles * (11 ADDs, 19 MULTs, 1 DIV, 2 SQRTs, 1 EXP) + n_obstacles * 2 ADDs) + 2 ADD) ]}
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 2 ADDs, 4 MULTs
+      //
+      // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
+      //
+      Vector2d f_alpha_dt = acceleration_term_dt(p);
+
+      // # (N-1) * (13 ADDs, 24 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
+      //   + (N-1) * 2 ADDs
+      //
+      // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
+      //
+      // for (int j = 0; j < n_p; j++) {
+      //  if (i == j)
+      //    continue;
+      //
+      //  f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      //}
+      for (int j = 0; j < i; j++) {
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+      for (int j = i + 1; j < n_p; j++) {
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+      // # board.n_obstacles * (11 ADDs, 19 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
+      //   + board.n_obstacles * 2 ADDs
+      //
+      // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
+      //
+      for (int k = 0; k < board.n_obstacles; k++) {
+        f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
+      }
+
+      // # 2 ADDs
+      //
+      //
+      // $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
+      //
+      p.w += f_alpha_dt;
+    }
+
+    // Pass 2: Update pedestrian states after all forces have been computed.
+    // # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 1 ADD, 2 MULTs, 1 SQRT
+      double w_norm = p.w.norm();
+
+      double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
+
+      // # 1 DIV
+      double correction_factor = std::min(1.0, p.v_max / w_norm2);
+
+      // # 2 MULTs
+      p.curr_vel = p.w * correction_factor;
+
+      // # 2 ADDs, 2 MULTs
+      //
+      // $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
+      //
+      p.curr_pos = p.curr_pos + p.curr_vel * dt;
+
+      // When a pedestrian reaches the end of the corridor, teleport them back
+      // to the opposite entrance while keeping the same y-coordinate. This
+      // maintains a constant pedestrian flow and makes lane formation easier to
+      // observe.
+      if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
+        p.curr_pos.x = 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+      if (p.color == BLACK && p.curr_pos.x < 1.5) {
+        p.curr_pos.x = BOARD_LENGTH - 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(-p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+
+      // Keep pedestrians inside the board boundaries.
+      p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
+
+      // # 2 ADDs
+      Vector2d e = p.destination - p.curr_pos;
+
+      // # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
+      double e_norm = e.norm();
+      double e_norm2 = std::fmax(e_norm, EPS); // Avoid division by zero.
+      double e_norm_inv = 1.0 / e_norm2;
+
+      // # 2 MULTs
+      p.e = e * e_norm_inv;
+
+      // # 1 ADD, 2 MULTs
+      double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
+
+      // # 1 SQRT
+      double velocity_norm = std::sqrt(velocity_norm_sq);
+
+      // # 1 MULT
+      p.s_b_sq = velocity_norm_sq * delta_t_sq;
+
+      // # 3 MULTs
+      p.s_b_e = p.e * (velocity_norm * delta_t);
+
+#if !BENCHMARK
+      // Store the current pedestrian state for visualization.
+      if (dump_path && (t % dump_interval == 0)) {
+        samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+      }
+#endif
+    }
+  }
+
+#if !BENCHMARK
+  // Export recorded simulation states to CSV.
+  if (dump_path) {
+    ofstream out(dump_path);
+    out << "time,id,x,y,p.e.x,p.e.y,color\n";
+
+    for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
+      out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
+    }
+  }
+#endif
+
+  return 0;
+}
\ No newline at end of file
diff --git a/variants/variant_branchless_sqrtfree_flopcount.cpp b/variants/variant_branchless_sqrtfree_flopcount.cpp
new file mode 100644
index 0000000..ed3ff6b
--- /dev/null
+++ b/variants/variant_branchless_sqrtfree_flopcount.cpp
@@ -0,0 +1,428 @@
+// This file is merged from the forces.cpp and social_forces_baseline.cpp
+// The goal is that we from now on only have one file as our baseline
+// This should make it more easy in the future to implement optimizations
+// Our naming convention from now on is: variant_<variant_name>.cpp (Please make sure to keep that consistent, otherwise our Makefile wont work anymore)
+
+// # Changes: FLOPCOUNT
+// # Predecessor: variant_branchless_sqrtfree.cpp
+// n_iter * [n_p ** 2 * (15 A + 25 M + 1 D + 3 S + 1 E) + n_p * (- 4 A - 5 M + 1 D - 1 E) + n_obstacles * n_p * (13 A + 20 M + 1 D + 1 S + 1 E)]
+
+
+#include "../headers/board.hpp"
+#include "../headers/constants.hpp"
+#include "../headers/forces.hpp"
+#include "../headers/pedestrian.hpp"
+#include "../headers/social_force.hpp"
+#include "../headers/vector2d.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <math.h> // keep — forces code uses unqualified sqrt/exp
+#include <random>
+#include <tuple>
+#include <vector>
+
+#define SEED 42
+#define BOARD_WIDTH 10.0
+#define BOARD_LENGTH 50.0
+#define WALL_EPS 1e-3
+#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
+
+#ifndef RANDOM_SEED
+#define RANDOM_SEED 0
+#endif
+
+#ifndef BENCHMARK
+#define BENCHMARK 0
+#endif
+
+using namespace std;
+
+// forces.cpp
+
+// Flop count: 3 ADDs, 7 MULTs (no SQRT, no branch)
+// Branchless + sqrt-free: cos_phi < 0 so f.norm()*cos_phi <= 0.
+//   - If dot >= 0: condition is trivially true.
+//   - If dot < 0: both sides non-positive; square them safely:
+//     dot^2 <= f.dot(f) * cos_phi^2.
+// Bitwise | (not ||) keeps both sides evaluated and avoids a branch.
+
+// 3 ADD + 7 MULT
+double w(Vector2d e, Vector2d f) {
+
+  double dot = e.x * f.x + e.y * f.y;
+  double f_dot_f = f.x * f.x + f.y * f.y;
+  double cond = (dot >= 0.0) | (dot * dot <= f_dot_f * cos_phi_sq);
+  return c_neg_times_dt + cond * (dt_neg - c_neg_times_dt);
+}
+
+// Flop count: 2 ADDs, 4 MULTs
+Vector2d acceleration_term_dt(const Pedestrian &alpha) {
+
+  // # 2 MULTs
+  Vector2d desired_velocity = alpha.e * alpha.desired_v0;
+
+  Vector2d actual_velocity = alpha.curr_vel;
+
+  // # 2 ADDs, 2 MULTs
+  Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
+
+  return f_alpha_0_dt;
+}
+
+// Flop count: 13 ADDs, 25 MULTs, 1 DIV, 3 SQRTs, 1 EXP
+Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta}$
+  Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
+  double r_ab_norm = r_ab.norm();
+  r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
+
+  // $v_\beta \Delta t \vec{e}_\beta$
+  Vector2d s_b_e = beta.s_b_e;
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
+  Vector2d r_ab2 = r_ab - s_b_e;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab2_norm = r_ab2.norm();
+  r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
+
+  // # 1 ADD
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
+
+  // # 1 ADD, 1 MULTs, 1 SQRT
+  //
+  // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
+  //
+  double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
+  two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
+  double two_b = sqrt(two_b_sq);
+
+  // # 5 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
+  //
+  double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
+
+  // # 2 ADDs, 4 MULTs
+  //
+  // $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
+  //
+  //
+  //          $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
+  //          v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  //
+  // $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
+  //
+  //
+  //       $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
+
+  // # 3 ADDs, 9 MULTs
+  //
+  // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
+  //
+  Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
+
+  return f_alpha_beta_dt;
+}
+
+// Flop count: 11 ADDs, 20 MULTs, 1 DIVs, 1 SQRT, 1 EXP
+Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
+
+  // # 7 ADDs, 5 MULTs
+  // $\vec{r}_{\alpha B}$
+  Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
+  double r_aB_norm = r_aB.norm();
+  r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
+
+  // # 2 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
+  //
+  double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
+
+  // # 2 MULTs
+  //
+  // $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
+  //
+  Vector2d f_alpha_B_neg = r_aB * scalar_mult;
+
+  // # 3 ADDs, 9 MULTs
+  //
+  // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
+  //
+  Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
+
+  return f_alpha_B_dt;
+}
+
+// social_force_baseline.cpp
+
+Pedestrian *init_pedestrians(int n_p) {
+#if RANDOM_SEED
+  random_device rd;  // Platform-provided random seed source.
+  mt19937 gen(rd()); // Non-reproducible initialization.
+#else
+  mt19937 gen(SEED); // Reproducible initialization.
+#endif
+
+  normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
+  uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
+  uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
+  uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
+
+  Pedestrian *P = new Pedestrian[n_p];
+
+  int n_p_half = n_p / 2;
+
+  // Group of pedestrians walking from left to right.
+  for (int i = 0; i < n_p_half; i++) {
+    double x = x_gen_left(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  // Group of pedestrians walking from right to left.
+  for (int i = n_p_half; i < n_p; i++) {
+    double x = x_gen_right(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(-30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  return P;
+}
+
+Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
+
+int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
+
+#if BENCHMARK
+  // Avoid compiler unused parameter warning
+  (void)dump_path;
+#endif
+
+#if !BENCHMARK
+  const int dump_interval = 1;
+
+  // Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
+  vector<tuple<int, int, double, double, double, double, int>> samples;
+
+  if (dump_path) {
+    samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
+
+    // Store the initial state before the first simulation step.
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+      samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+    }
+  }
+#endif
+
+  // n_iter * [{n_p * [(n_p - 1) * (13 ADDs, 25 MULTs, 1 DIV, 3 SQRTs, 1 EXP) + (n_p-1) * (2 ADDs) + n_obstacles * (11 ADDs, 20 MULTs, 1 DIV, 1 SQRTs, 1 EXP) + n_obstacles * (2 ADDs) + 4 ADDs + 4 MULTs]} + {(7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p}]
+  // n_iter * [{n_p * [(n_p - 1) * (13 A + 25 M + 1 D + 3 S + 1 E) + (n_p - 1) * (2 A) + n_obstacles * (11 A + 20 M + 1 D + 1 S + 1 E) + n_obstacles * (2 A) + 4 A + 4 M]} + {(7 A + 16 M + 2 D + 3 S) * n_p}]
+  // n_iter * [{n_p * [n_p * (13 A + 25 M + 1 D + 3 S + 1 E) - 1 * (13 A + 25 M + 1 D + 3 S + 1 E) + n_p * (2 A) - 1 * (2 A) + n_obstacles * (11 A + 20 M + 1 D + 1 S + 1 E) + n_obstacles * (2 A) + 4 A + 4 M]} + {n_p * (7 A + 16 M + 2 D + 3 S)}]
+  // n_iter * [{n_p * [n_p * (15 A + 25 M + 1 D + 3 S + 1 E) - 1 * (15 A + 25 M + 1 D + 3 S + 1 E - 4 A - 4 M) + n_obstacles * (13 A + 20 M + 1 D + 1 S + 1 E)]} + {n_p * (7 A + 16 M + 2 D + 3 S)}]
+  // n_iter * [n_p ** 2 * (15 A + 25 M + 1 D + 3 S + 1 E) - n_p * (11 A + 21 M + 1 D + 3 S + 1 E) + n_obstacles * n_p * (13 A + 20 M + 1 D + 1 S + 1 E) + n_p * (7 A + 16 M + 2 D + 3 S)]
+  // n_iter * [n_p ** 2 * (15 A + 25 M + 1 D + 3 S + 1 E) + n_p * (- 11 A - 21 M - 1 D - 3 S - 1 E) + n_p * (7 A + 16 M + 2 D + 3 S) + n_obstacles * n_p * (13 A + 20 M + 1 D + 1 S + 1 E)]
+  // n_iter * [n_p ** 2 * (15 A + 25 M + 1 D + 3 S + 1 E) + n_p * (- 4 A - 5 M + 1 D - 1 E) + n_obstacles * n_p * (13 A + 20 M + 1 D + 1 S + 1 E)]
+  for (int t = 0; t < n_iter; t++) {
+    // Pass 1: Compute all forces from the current state.
+    
+    // n_p * [(n_p - 1) * (13 ADDs, 24 MULTs, 1 DIV, 3 SQRTs, 1 EXP) + (n_p-1) * (2 ADDs) + n_obstacles * (11 ADDs, 19 MULTs, 1 DIV, 1 SQRT, 1 EXP) + n_obstacles * (2 ADDs) + 4 ADDs + 4 MULTs]
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 2 ADDs, 4 MULTs
+      //
+      // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
+      //
+      Vector2d f_alpha_dt = acceleration_term_dt(p);
+
+      // # (N-1) * (13 ADDs, 25 MULTs, 1 DIV, 3 SQRT, 1 EXP)
+      //   + (N-1) * 2 ADDs
+      //
+      // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
+      //
+      // for (int j = 0; j < n_p; j++) {
+      //  if (i == j)
+      //    continue;
+      //
+      //  f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      //}
+      for (int j = 0; j < i; j++) {
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+      for (int j = i + 1; j < n_p; j++) {
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+      // # board.n_obstacles * (11 ADDs, 20 MULTs, 1 DIV, 1 SQRT, 1 EXP)
+      //   + board.n_obstacles * 2 ADDs
+      //
+      // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
+      //
+      for (int k = 0; k < board.n_obstacles; k++) {
+        f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
+      }
+
+      // # 2 ADDs
+      //
+      //
+      // $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
+      //
+      p.w += f_alpha_dt;
+    }
+
+    // Pass 2: Update pedestrian states after all forces have been computed.
+    // # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 1 ADD, 2 MULTs, 1 SQRT
+      double w_norm = p.w.norm();
+
+      double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
+
+      // # 1 DIV
+      double correction_factor = std::min(1.0, p.v_max / w_norm2);
+
+      // # 2 MULTs
+      p.curr_vel = p.w * correction_factor;
+
+      // # 2 ADDs, 2 MULTs
+      //
+      // $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
+      //
+      p.curr_pos = p.curr_pos + p.curr_vel * dt;
+
+      // When a pedestrian reaches the end of the corridor, teleport them back
+      // to the opposite entrance while keeping the same y-coordinate. This
+      // maintains a constant pedestrian flow and makes lane formation easier to
+      // observe.
+      if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
+        p.curr_pos.x = 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+      if (p.color == BLACK && p.curr_pos.x < 1.5) {
+        p.curr_pos.x = BOARD_LENGTH - 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(-p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+
+      // Keep pedestrians inside the board boundaries.
+      p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
+
+      // # 2 ADDs
+      Vector2d e = p.destination - p.curr_pos;
+
+      // # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
+      double e_norm = e.norm();
+      double e_norm2 = std::fmax(e_norm, EPS); // Avoid division by zero.
+      double e_norm_inv = 1.0 / e_norm2;
+
+      // # 2 MULTs
+      p.e = e * e_norm_inv;
+
+      // # 1 ADD, 2 MULTs
+      double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
+
+      // # 1 SQRT
+      double velocity_norm = std::sqrt(velocity_norm_sq);
+
+      // # 1 MULT
+      p.s_b_sq = velocity_norm_sq * delta_t_sq;
+
+      // # 3 MULTs
+      p.s_b_e = p.e * (velocity_norm * delta_t);
+
+#if !BENCHMARK
+      // Store the current pedestrian state for visualization.
+      if (dump_path && (t % dump_interval == 0)) {
+        samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+      }
+#endif
+    }
+  }
+
+#if !BENCHMARK
+  // Export recorded simulation states to CSV.
+  if (dump_path) {
+    ofstream out(dump_path);
+    out << "time,id,x,y,p.e.x,p.e.y,color\n";
+
+    for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
+      out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
+    }
+  }
+#endif
+
+  return 0;
+}
\ No newline at end of file
diff --git a/variants/variant_cutoff_callsite_flopcount.cpp b/variants/variant_cutoff_callsite_flopcount.cpp
new file mode 100644
index 0000000..969f59c
--- /dev/null
+++ b/variants/variant_cutoff_callsite_flopcount.cpp
@@ -0,0 +1,436 @@
+// This file is copied from the baseline and modified
+// Our goal is to only calculate pedestrian-pedestrian forces for pedestrian which are near
+// Our tolerance is 1cm and our cutoff is a 5m radius.
+// Since we have some tolerance and we put pedestrians back at the end of the hallway once they are on one end. We only test/verify on a smaller subsample.
+// Otherwise we had huge gaps (because of those jumps back)
+// In this version we do the cutoff before we call the pedestrian forces function
+
+// # Changes: FLOPCOUNT
+// # Predecessor: variant_cutoff_callsite.cpp
+
+// // n_iter * [ n_p ** 2 * (14 A + 23 M + 1 D + 4 S + 1 E) + n_p * (- 3 A - 3 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (12 A + 18 M + 1 D + 2 S + 1 E) ]
+
+
+#include "../headers/board.hpp"
+#include "../headers/constants.hpp"
+#include "../headers/pedestrian.hpp"
+#include "../headers/vector2d.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <math.h> // keep — forces code uses unqualified sqrt/exp
+#include <random>
+#include <tuple>
+#include <vector>
+
+#define SEED 42
+#define BOARD_WIDTH 10.0
+#define BOARD_LENGTH 50.0
+#define WALL_EPS 1e-3
+#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
+
+#ifndef RANDOM_SEED
+#define RANDOM_SEED 0
+#endif
+
+#ifndef BENCHMARK
+#define BENCHMARK 0
+#endif
+
+using namespace std;
+
+// forces.cpp
+
+// Flop count: 2 ADDs, 5 MULTs, 1 SQRT
+double w(Vector2d e, Vector2d f) {
+  if (e.dot(f) >= f.norm() * cos_phi)
+    return dt_neg;
+  else
+    return c_neg_times_dt;
+}
+
+// Flop count: 2 ADDs, 4 MULTs
+Vector2d acceleration_term_dt(const Pedestrian &alpha) {
+
+  // # 2 MULTs
+  Vector2d desired_velocity = alpha.e * alpha.desired_v0;
+
+  Vector2d actual_velocity = alpha.curr_vel;
+
+  // # 2 ADDs, 2 MULTs
+  Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
+
+  return f_alpha_0_dt;
+}
+
+// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP
+Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta}$
+  Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
+  double r_ab_norm = r_ab.norm();
+  // Now we wanna check if it is a close pedestrian or wheter it is far away and can be ignored
+  // double r_ab_sq = r_ab.x * r_ab.x + r_ab.y * r_ab.y; // squared distance
+  // if (r_ab_sq > CUTOFF_SQ)
+  //  return Vector2d(0.0, 0.0);      // cheap reject
+  // double r_ab_norm = sqrt(r_ab_sq); // reuse r_ab_sq, no recompute
+
+  r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
+
+  // $v_\beta \Delta t \vec{e}_\beta$
+  Vector2d s_b_e = beta.s_b_e;
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
+  Vector2d r_ab2 = r_ab - s_b_e;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab2_norm = r_ab2.norm();
+  r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
+
+  // # 1 ADD
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
+
+  // # 1 ADD, 1 MULTs, 1 SQRT
+  //
+  // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
+  //
+  double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
+  two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
+  double two_b = sqrt(two_b_sq);
+
+  // # 5 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
+  //
+  double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
+
+  // # 2 ADDs, 4 MULTs
+  //
+  // $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
+  //
+  //
+  //          $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
+  //          v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  //
+  // $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
+  //
+  //
+  //       $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
+
+  // # 2 ADDs, 7 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
+  //
+  Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
+
+  return f_alpha_beta_dt;
+}
+
+// Flop count: 10 ADDs, 18 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
+Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
+
+  // # 7 ADDs, 5 MULTs
+  // $\vec{r}_{\alpha B}$
+  Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
+  double r_aB_norm = r_aB.norm();
+  r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
+
+  // # 2 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
+  //
+  double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
+
+  // # 2 MULTs
+  //
+  // $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
+  //
+  Vector2d f_alpha_B_neg = r_aB * scalar_mult;
+
+  // # 2 ADDs, 7 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
+  //
+  Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
+
+  return f_alpha_B_dt;
+}
+
+// social_force_baseline.cpp
+
+Pedestrian *init_pedestrians(int n_p) {
+#if RANDOM_SEED
+  random_device rd;  // Platform-provided random seed source.
+  mt19937 gen(rd()); // Non-reproducible initialization.
+#else
+  mt19937 gen(SEED); // Reproducible initialization.
+#endif
+
+  normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
+  uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
+  uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
+  uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
+
+  Pedestrian *P = new Pedestrian[n_p];
+
+  int n_p_half = n_p / 2;
+
+  // Group of pedestrians walking from left to right.
+  for (int i = 0; i < n_p_half; i++) {
+    double x = x_gen_left(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  // Group of pedestrians walking from right to left.
+  for (int i = n_p_half; i < n_p; i++) {
+    double x = x_gen_right(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(-30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  return P;
+}
+
+Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
+
+int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
+
+#if BENCHMARK
+  // Avoid compiler unused parameter warning
+  (void)dump_path;
+#endif
+
+#if !BENCHMARK
+  const int dump_interval = 1;
+
+  // Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
+  vector<tuple<int, int, double, double, double, double, int>> samples;
+
+  if (dump_path) {
+    samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
+
+    // Store the initial state before the first simulation step.
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+      samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+    }
+  }
+#endif
+
+  // n_iter * [ { n_p * (n_p - 1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP + 2 ADDs) + n_p * (4 ADD + 4 MULT + board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP + 2 ADD) ) } + {(7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p} ]
+
+  // n_iter * [ { n_p * (n_p - 1) * (12 A + 23 M + 1 D + 4 S + 1 E + 2 A) + n_p * (4 A + 4 M + n_obstacles * (10 A + 18 M + 1 D + 2 S + 1 E + 2 A) ) } + {n_p * (7 A + 16 M + 2 D + 3 S)} ]
+
+  // n_iter * [ { n_p ** 2 * (12 A + 23 M + 1 D + 4 S + 1 E + 2 A) - n_p * (12 A + 23 M + 1 D + 4 S + 1 E + 2 A) + n_p * (4 A + 4 M) + n_obstacles * n_p * (10 A + 18 M + 1 D + 2 S + 1 E + 2 A) } + {n_p * (7 A + 16 M + 2 D + 3 S)} ]
+
+
+  // n_iter * [ n_p ** 2 * (12 A + 23 M + 1 D + 4 S + 1 E + 2 A) + n_p * (- 8 A - 19 M - 1 D - 4 S - 1 E - 2 A) + n_obstacles * n_p * (10 A + 18 M + 1 D + 2 S + 1 E + 2 A) + n_p * (7 A + 16 M + 2 D + 3 S) ]
+
+
+  // n_iter * [ n_p ** 2 * (14 A + 23 M + 1 D + 4 S + 1 E) + n_p * (- 3 A - 3 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (12 A + 18 M + 1 D + 2 S + 1 E) ]
+
+// NOTE: this flop count charges every ped-ped pair the full cost. The cutoff
+// (L80-81) early-rejects pairs farther than 5m, so the real ped-ped work is only
+// a fraction of this (roughly 40-50% on a 10x50 hallway, depending on the layout).
+// We keep it at 100% here as the upper-bound / fixed-W baseline, the exact fraction
+// can be folded into the n_p**2 term later if we want the actual executed flops.
+
+  for (int t = 0; t < n_iter; t++) {
+    // Pass 1: Compute all forces from the current state.
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 2 ADDs, 4 MULTs
+      //
+      // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
+      //
+      Vector2d f_alpha_dt = acceleration_term_dt(p);
+
+      // # (N-1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
+      //   + (N-1) * 2 ADDs
+      //
+      // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
+      //
+      for (int j = 0; j < n_p; j++) {
+        if (i == j)
+          continue;
+
+        // Cutoff check at the call site (so we dont have an additional call)
+        Vector2d r_ab_pre = p.curr_pos - pedestrians[j].curr_pos;
+        if (r_ab_pre.x * r_ab_pre.x + r_ab_pre.y * r_ab_pre.y > CUTOFF_SQ)
+          continue;
+
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+
+      // # board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
+      //   + board.n_obstacles * 2 ADDs
+      //
+      // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
+      //
+      for (int k = 0; k < board.n_obstacles; k++) {
+        f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
+      }
+
+      // # 2 ADDs
+      //
+      //
+      // $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
+      //
+      p.w += f_alpha_dt;
+    }
+
+    // Pass 2: Update pedestrian states after all forces have been computed.
+    // # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 1 ADD, 2 MULTs, 1 SQRT
+      double w_norm = p.w.norm();
+
+      double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
+
+      // # 1 DIV
+      double correction_factor = std::min(1.0, p.v_max / w_norm2);
+
+      // # 2 MULTs
+      p.curr_vel = p.w * correction_factor;
+
+      // # 2 ADDs, 2 MULTs
+      //
+      // $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
+      //
+      p.curr_pos = p.curr_pos + p.curr_vel * dt;
+
+      // When a pedestrian reaches the end of the corridor, teleport them back
+      // to the opposite entrance while keeping the same y-coordinate. This
+      // maintains a constant pedestrian flow and makes lane formation easier to
+      // observe.
+      if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
+        p.curr_pos.x = 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+      if (p.color == BLACK && p.curr_pos.x < 1.5) {
+        p.curr_pos.x = BOARD_LENGTH - 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(-p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+
+      // Keep pedestrians inside the board boundaries.
+      p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
+
+      // # 2 ADDs
+      Vector2d e = p.destination - p.curr_pos;
+
+      // # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
+      double e_norm = e.norm();
+      double e_norm2 = e_norm > EPS ? e_norm : EPS; // Avoid division by zero.
+      double e_norm_inv = 1.0 / e_norm2;
+
+      // # 2 MULTs
+      p.e = e * e_norm_inv;
+
+      // # 1 ADD, 2 MULTs
+      double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
+
+      // # 1 SQRT
+      double velocity_norm = std::sqrt(velocity_norm_sq);
+
+      // # 1 MULT
+      p.s_b_sq = velocity_norm_sq * delta_t_sq;
+
+      // # 3 MULTs
+      p.s_b_e = p.e * (velocity_norm * delta_t);
+
+#if !BENCHMARK
+      // Store the current pedestrian state for visualization.
+      if (dump_path && (t % dump_interval == 0)) {
+        samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+      }
+#endif
+    }
+  }
+
+#if !BENCHMARK
+  // Export recorded simulation states to CSV.
+  if (dump_path) {
+    ofstream out(dump_path);
+    out << "time,id,x,y,p.e.x,p.e.y,color\n";
+
+    for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
+      out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
+    }
+  }
+#endif
+
+  return 0;
+}
\ No newline at end of file
diff --git a/variants/variant_cutoff_flopcount.cpp b/variants/variant_cutoff_flopcount.cpp
new file mode 100644
index 0000000..bffd1a9
--- /dev/null
+++ b/variants/variant_cutoff_flopcount.cpp
@@ -0,0 +1,434 @@
+// This file is copied from the baseline and modified
+// Our goal is to only calculate pedestrian-pedestrian forces for pedestrian which are near
+// Our tolerance is 1cm and our cutoff is a 5m radius.
+// Since we have some tolerance and we put pedestrians back at the end of the hallway once they are on one end. We only test/verify on a smaller subsample.
+// Otherwise we had huge gaps (because of those jumps back)
+
+// # Changes: FLOPCOUNT
+// # Predecessor: variant_cutoff.cpp
+
+
+// n_iter * [ n_p ** 2 * (14 A + 23 M + 1 D + 4 S + 1 E) + n_p * (- 3 A - 3 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (12 A + 18 M + 1 D + 2 S + 1 E) ]
+
+
+#include "../headers/board.hpp"
+#include "../headers/constants.hpp"
+#include "../headers/pedestrian.hpp"
+#include "../headers/vector2d.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <math.h> // keep — forces code uses unqualified sqrt/exp
+#include <random>
+#include <tuple>
+#include <vector>
+
+#define SEED 42
+#define BOARD_WIDTH 10.0
+#define BOARD_LENGTH 50.0
+#define WALL_EPS 1e-3
+#define BOARD_WIDTH_MINUS_WALL_EPS 9.999
+
+#ifndef RANDOM_SEED
+#define RANDOM_SEED 0
+#endif
+
+#ifndef BENCHMARK
+#define BENCHMARK 0
+#endif
+
+using namespace std;
+
+// forces.cpp
+
+// Flop count: 2 ADDs, 5 MULTs, 1 SQRT
+double w(Vector2d e, Vector2d f) {
+  if (e.dot(f) >= f.norm() * cos_phi)
+    return dt_neg;
+  else
+    return c_neg_times_dt;
+}
+
+// Flop count: 2 ADDs, 4 MULTs
+Vector2d acceleration_term_dt(const Pedestrian &alpha) {
+
+  // # 2 MULTs
+  Vector2d desired_velocity = alpha.e * alpha.desired_v0;
+
+  Vector2d actual_velocity = alpha.curr_vel;
+
+  // # 2 ADDs, 2 MULTs
+  Vector2d f_alpha_0_dt = (desired_velocity - actual_velocity) * dt_times_tau_alpha_inv;
+
+  return f_alpha_0_dt;
+}
+
+// Flop count: 12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP
+Vector2d pedestrian_repulsion_dt(const Pedestrian &alpha, const Pedestrian &beta) {
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta}$
+  Vector2d r_ab = alpha.curr_pos - beta.curr_pos;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert$
+  // double r_ab_norm = r_ab.norm();
+  // Now we wanna check if it is a close pedestrian or wheter it is far away and can be ignored
+  double r_ab_sq = r_ab.x * r_ab.x + r_ab.y * r_ab.y; // squared distance
+  if (r_ab_sq > CUTOFF_SQ)
+    return Vector2d(0.0, 0.0);      // cheap reject
+  double r_ab_norm = sqrt(r_ab_sq); // reuse r_ab_sq, no recompute
+
+  r_ab_norm = r_ab_norm > EPS ? r_ab_norm : EPS; // Avoid division by zero
+
+  // $v_\beta \Delta t \vec{e}_\beta$
+  Vector2d s_b_e = beta.s_b_e;
+
+  // # 2 ADDs
+  // $\vec{r}_{\alpha\beta} - \vec{v}_{\beta}\varDelta_{t}\vec{e}_{\beta}$
+  Vector2d r_ab2 = r_ab - s_b_e;
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab2_norm = r_ab2.norm();
+  r_ab2_norm = r_ab2_norm > EPS ? r_ab2_norm : EPS; // Avoid division by zero
+
+  // # 1 ADD
+  // $\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert$
+  double r_ab_norm_sum = r_ab_norm + r_ab2_norm;
+
+  // # 1 ADD, 1 MULTs, 1 SQRT
+  //
+  // $2b = \sqrt{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)^2 - (v_\beta \Delta t)^2}$
+  //
+  double two_b_sq = r_ab_norm_sum * r_ab_norm_sum - beta.s_b_sq;
+  two_b_sq = two_b_sq > EPS_SQ ? two_b_sq : EPS_SQ; // Avoid sqrt of negative and avoid division by zero
+  double two_b = sqrt(two_b_sq);
+
+  // # 5 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_common = (r_ab_norm_sum / (two_b * r_ab_norm * r_ab2_norm)) * (NEG_V0_AB_div_TWO_SIGMA * exp(two_b * NEG_TWO_SIGMA_INV));
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}$
+  //
+  double scalar_mult_r_ab = scalar_mult_common * r_ab2_norm;
+
+  // # 1 MULT
+  //
+  // $\frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}$
+  //
+  double scalar_mult_r_ab2 = scalar_mult_common * r_ab_norm;
+
+  // # 2 ADDs, 4 MULTs
+  //
+  // $\vec{-f}_{\alpha \beta}(\vec{r}_{\alpha \beta}) = \frac{-V^0_{\alpha \beta}}{2\sigma}e^{-b / \sigma} 2\grad_{\vec{r}_{\alpha \beta}}b $
+  //
+  //
+  //          $= \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{-V^0_{\alpha\beta}}{2\sigma}\,e^{-b/\sigma}\,\frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} -
+  //          v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  //
+  // $2\grad_{\vec{r}_{\alpha \beta}}b = \frac{(\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert)(\frac{\vec{r}_{\alpha \beta}}{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert} + \frac{\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta}{\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert})}{2b}$
+  //
+  //
+  //       $= \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert}\,\vec{r}_{\alpha \beta} + \frac{\Arrowvert \vec{r}_{\alpha \beta} \Arrowvert + \Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}{2b\,\Arrowvert \vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta \Arrowvert}\,(\vec{r}_{\alpha \beta} - v_\beta \Delta t \vec{e}_\beta)$
+  //
+  //
+  Vector2d f_alpha_beta_neg = r_ab * scalar_mult_r_ab + r_ab2 * scalar_mult_r_ab2;
+
+  // # 2 ADDs, 7 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha\beta}(\vec{e}_{\alpha}, \vec{r}_{\alpha \beta}).dt = w(\vec{e}_{\alpha}, -\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta})).\vec{f}_{\alpha\beta}(\vec{r}_{\alpha \beta}).dt$
+  //
+  Vector2d f_alpha_beta_dt = f_alpha_beta_neg * w(alpha.e, f_alpha_beta_neg);
+
+  return f_alpha_beta_dt;
+}
+
+// Flop count: 10 ADDs, 18 MULTs, 1 DIVs, 2 SQRTs, 1 EXP
+Vector2d obstacle_repulsion_dt(const Pedestrian &alpha, const Obstacle &B) {
+
+  // # 7 ADDs, 5 MULTs
+  // $\vec{r}_{\alpha B}$
+  Vector2d r_aB = alpha.curr_pos - B.find_nearest_obstacle_point(alpha.curr_pos);
+
+  // # 1 ADD, 2 MULTs, 1 SQRT
+  // $\Arrowvert \vec{r}_{\alpha B} \Arrowvert$
+  double r_aB_norm = r_aB.norm();
+  r_aB_norm = r_aB_norm > EPS ? r_aB_norm : EPS; // Avoid division by zero
+
+  // # 2 MULTs, 1 DIV, 1 EXP
+  //
+  // $\frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R}$
+  //
+  double scalar_mult = (NEG_U0_AB_div_R * exp(r_aB_norm * NEG_R_INV)) / r_aB_norm;
+
+  // # 2 MULTs
+  //
+  // $-\vec{F}_{\alpha B}(\vec{r}_{\alpha B}) = \frac{-U^0_{\alpha B}}{R} e^{- \Arrowvert \vec{r}_{\alpha B} \Arrowvert / R} \frac{\vec{r}_{\alpha B}}{\Arrowvert \vec{r}_{\alpha B} \Arrowvert}$
+  //
+  Vector2d f_alpha_B_neg = r_aB * scalar_mult;
+
+  // # 2 ADDs, 7 MULTs, 1 SQRT
+  //
+  // $\vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha B}).dt = w(\vec{e}_{\alpha}, -\vec{F}_{\alpha B}(\vec{r}_{\alpha B})).\vec{F}_{\alpha B}(\vec{r}_{\alpha B}).dt$
+  //
+  Vector2d f_alpha_B_dt = f_alpha_B_neg * w(alpha.e, f_alpha_B_neg);
+
+  return f_alpha_B_dt;
+}
+
+// social_force_baseline.cpp
+
+Pedestrian *init_pedestrians(int n_p) {
+#if RANDOM_SEED
+  random_device rd;  // Platform-provided random seed source.
+  mt19937 gen(rd()); // Non-reproducible initialization.
+#else
+  mt19937 gen(SEED); // Reproducible initialization.
+#endif
+
+  normal_distribution<double> velocity_gen(v0_mean, v0_std_dev);
+  uniform_real_distribution<double> y_gen(0.3, BOARD_WIDTH - 0.3);
+  uniform_real_distribution<double> x_gen_left(2.0, BOARD_LENGTH / 2.0);
+  uniform_real_distribution<double> x_gen_right(BOARD_LENGTH / 2.0, BOARD_LENGTH - 2.0);
+
+  Pedestrian *P = new Pedestrian[n_p];
+
+  int n_p_half = n_p / 2;
+
+  // Group of pedestrians walking from left to right.
+  for (int i = 0; i < n_p_half; i++) {
+    double x = x_gen_left(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(BOARD_LENGTH + 30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(WHITE, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  // Group of pedestrians walking from right to left.
+  for (int i = n_p_half; i < n_p; i++) {
+    double x = x_gen_right(gen);
+    double y = y_gen(gen);
+
+    Vector2d start_pos = Vector2d(x, y);
+    Vector2d destination = Vector2d(-30.0, y);
+
+    Vector2d init_e = destination - start_pos;
+    double init_e_norm = init_e.norm();
+    Vector2d init_e_unit = init_e / init_e_norm;
+
+    // Reject near-zero desired speeds to avoid pedestrians that barely move,
+    // and reject negative speeds because they would reverse the intended
+    // walking direction.
+    double desired_v0;
+    do {
+      desired_v0 = velocity_gen(gen);
+    } while (desired_v0 <= 0.1);
+
+    double v_max = 1.3 * desired_v0;
+
+    P[i] = Pedestrian(BLACK, Vector2d(), start_pos, init_e_unit, v_max, desired_v0, destination);
+  }
+
+  return P;
+}
+
+Board init_board_without_obstacles() { return Board(BOARD_LENGTH, BOARD_WIDTH, 0, nullptr); }
+
+int run_simulation(Pedestrian *pedestrians, Board &board, int n_p, int n_iter, const char *dump_path) {
+
+#if BENCHMARK
+  // Avoid compiler unused parameter warning
+  (void)dump_path;
+#endif
+
+#if !BENCHMARK
+  const int dump_interval = 1;
+
+  // Each sample stores: time step, pedestrian id, x, y, e.x, e.y, color.
+  vector<tuple<int, int, double, double, double, double, int>> samples;
+
+  if (dump_path) {
+    samples.reserve(static_cast<size_t>(n_iter / dump_interval + 1) * n_p);
+
+    // Store the initial state before the first simulation step.
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+      samples.emplace_back(0, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+    }
+  }
+#endif
+
+  // n_iter * [{ n_p * { [(n_p - 1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP + 2 ADDs)] + [n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP + 2 ADDs) + 4 ADDs + 4 MULTs] } } + { (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p }]
+
+
+  // n_iter * [{ n_p * { [(n_p - 1) * (12 A + 23 M + 1 D + 4 S + 1 E + 2 A)] + [n_obstacles * (10 A + 18 M + 1 D + 2 S + 1 E + 2 A) + 4 A + 4 M] } } + { n_p * (7 A + 16 M + 2 D + 3 S) }]
+
+  // n_iter * [{ n_p * { n_p * (14 A + 23 M + 1 D + 4 S + 1 E) - 1 * (10 A + 19 M + 1 D + 4 S + 1 E) + n_obstacles * (12 A + 18 M + 1 D + 2 S + 1 E) }} + { n_p * (7 A + 16 M + 2 D + 3 S) }]
+
+
+  // n_iter * [ n_p ** 2 * (14 A + 23 M + 1 D + 4 S + 1 E) - n_p * (10 A + 19 M + 1 D + 4 S + 1 E) + n_obstacles * n_p * (12 A + 18 M + 1 D + 2 S + 1 E) + n_p * (7 A + 16 M + 2 D + 3 S) ]
+
+
+  // n_iter * [ n_p ** 2 * (14 A + 23 M + 1 D + 4 S + 1 E) + n_p * (- 3 A - 3 M + 1 D - 1 S - 1 E) + n_obstacles * n_p * (12 A + 18 M + 1 D + 2 S + 1 E) ]
+
+// NOTE: this flop count charges every ped-ped pair the full cost. The cutoff
+// (L80-81) early-rejects pairs farther than 5m, so the real ped-ped work is only
+// a fraction of this (roughly 40-50% on a 10x50 hallway, depending on the layout).
+// We keep it at 100% here as the upper-bound / fixed-W baseline, the exact fraction
+// can be folded into the n_p**2 term later if we want the actual executed flops.
+
+
+  for (int t = 0; t < n_iter; t++) {
+    // Pass 1: Compute all forces from the current state.
+    // n_p * { [(n_p - 1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP + 2 ADDs)] + [n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP + 2 ADDs) + 4 ADDs + 4 MULTs] }
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 2 ADDs, 4 MULTs
+      //
+      // $\vec{F}_\alpha(\vec{v}_\alpha, v^0_\alpha \vec{e}_\alpha) = \frac{1}{\tau_\alpha}(v^0_\alpha \vec{e}_\alpha - \vec{v}_\alpha)$
+      //
+      Vector2d f_alpha_dt = acceleration_term_dt(p);
+
+      // # (N-1) * (12 ADDs, 23 MULTs, 1 DIV, 4 SQRTs, 1 EXP)
+      //   + (N-1) * 2 ADDs
+      //
+      // $\sum_{\beta} \vec{F}_{\alpha\beta}(\vec{e}_{\alpha},\vec{r}_{\alpha} - \vec{r}_{\beta})$
+      //
+      for (int j = 0; j < n_p; j++) {
+        if (i == j)
+          continue;
+
+        f_alpha_dt += pedestrian_repulsion_dt(p, pedestrians[j]);
+      }
+
+      // # board.n_obstacles * (10 ADDs, 18 MULTs, 1 DIV, 2 SQRTs, 1 EXP)
+      //   + board.n_obstacles * 2 ADDs
+      //
+      // $\sum_{B} \vec{F}_{\alpha B}(\vec{e}_{\alpha}, \vec{r}_{\alpha} - \vec{r}_{B})$
+      //
+      for (int k = 0; k < board.n_obstacles; k++) {
+        f_alpha_dt += obstacle_repulsion_dt(p, board.obstacles[k]);
+      }
+
+      // # 2 ADDs
+      //
+      //
+      // $\frac{d\vec{w}_\alpha}{dt} = \vec{F}_{\alpha}(t)$
+      //
+      p.w += f_alpha_dt;
+    }
+
+    // Pass 2: Update pedestrian states after all forces have been computed.
+    // # (7 ADDs, 16 MULTs, 2 DIVs, 3 SQRTs) * n_p
+    for (int i = 0; i < n_p; i++) {
+      Pedestrian &p = pedestrians[i];
+
+      // # 1 ADD, 2 MULTs, 1 SQRT
+      double w_norm = p.w.norm();
+
+      double w_norm2 = w_norm > EPS ? w_norm : EPS; // Avoid division by zero.
+
+      // # 1 DIV
+      double correction_factor = std::min(1.0, p.v_max / w_norm2);
+
+      // # 2 MULTs
+      p.curr_vel = p.w * correction_factor;
+
+      // # 2 ADDs, 2 MULTs
+      //
+      // $\vec{r}_i^{t+1} = \vec{r}_i^t + \vec{v}_i^t dt$
+      //
+      p.curr_pos = p.curr_pos + p.curr_vel * dt;
+
+      // When a pedestrian reaches the end of the corridor, teleport them back
+      // to the opposite entrance while keeping the same y-coordinate. This
+      // maintains a constant pedestrian flow and makes lane formation easier to
+      // observe.
+      if (p.color == WHITE && p.curr_pos.x > (BOARD_LENGTH - 1.5)) {
+        p.curr_pos.x = 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+      if (p.color == BLACK && p.curr_pos.x < 1.5) {
+        p.curr_pos.x = BOARD_LENGTH - 1.0;
+        p.destination.y = p.curr_pos.y;
+        p.w = Vector2d(-p.desired_v0, 0.0);
+        p.curr_vel = p.w;
+      }
+
+      // Keep pedestrians inside the board boundaries.
+      p.curr_pos.y = clamp(p.curr_pos.y, WALL_EPS, BOARD_WIDTH_MINUS_WALL_EPS);
+
+      // # 2 ADDs
+      Vector2d e = p.destination - p.curr_pos;
+
+      // # 1 ADD, 2 MULTs, 1 DIV, 1 SQRT
+      double e_norm = e.norm();
+      double e_norm2 = e_norm > EPS ? e_norm : EPS; // Avoid division by zero.
+      double e_norm_inv = 1.0 / e_norm2;
+
+      // # 2 MULTs
+      p.e = e * e_norm_inv;
+
+      // # 1 ADD, 2 MULTs
+      double velocity_norm_sq = p.curr_vel.dot(p.curr_vel);
+
+      // # 1 SQRT
+      double velocity_norm = std::sqrt(velocity_norm_sq);
+
+      // # 1 MULT
+      p.s_b_sq = velocity_norm_sq * delta_t_sq;
+
+      // # 3 MULTs
+      p.s_b_e = p.e * (velocity_norm * delta_t);
+
+#if !BENCHMARK
+      // Store the current pedestrian state for visualization.
+      if (dump_path && (t % dump_interval == 0)) {
+        samples.emplace_back(t + 1, i, p.curr_pos.x, p.curr_pos.y, p.e.x, p.e.y, p.color);
+      }
+#endif
+    }
+  }
+
+#if !BENCHMARK
+  // Export recorded simulation states to CSV.
+  if (dump_path) {
+    ofstream out(dump_path);
+    out << "time,id,x,y,p.e.x,p.e.y,color\n";
+
+    for (auto &[t, id, x, y, e_x, e_y, c] : samples) {
+      out << t << ',' << id << ',' << x << ',' << y << ',' << e_x << ',' << e_y << ',' << c << '\n';
+    }
+  }
+#endif
+
+  return 0;
+}
\ No newline at end of file