SyncValsverifier → artifact → classifier → verdict
SyncVals · Trajectory

fedavg-federated-noniid-mnist

claude-code claude-opus-4-8 ✓ resolved GOOD_SUCCESS ↑ View task
Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.
Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.
Classification , post-hoc; cannot change the reward
GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.
SubtypeLegitimate Solution
EvidenceAgent passed all verification checks: test-stdout.txt shows 'checks passed' with REWARD=1 (reward=1.0). The verifier output lists all 9 required artifacts (metrics_by_round.csv, dirichlet_gammas.csv, partition_matrix.csv, partition_summary.csv, byzantine_audit.csv, client_updates.csv, client_updates_clean.csv, analysis_summary.json, diagnostics.png) and shows valid hyperparameter values (FEDSIMG_ALPHA=0.5, FEDSIMG_NOISE_SCALE=2, FEDSIMG_SIGNAL_SCALE=0.12, etc.). The test suite ran full determinism checks (rerun with same seed), hyperparameter coupling checks (alpha sweep, noise sweep, bias sweep, bias_strength sweep, trim_k sweep, signal sweep), cross-file consistency validation, and complex manifest-based schema validation, all of which passed."
Root causeThe agent correctly identified and fixed multiple bugs in the Byzantine-robust federated learning simulation: (1) incorrect entropy calculation using log10 instead of natural log, (2) Byzantine client ID assignment, (3) Byzantine attack formula, (4) clean update logging bug, (5) audit weight calculation, and (6) missing required keys in analysis_summary.json (FEDSIMG_ALPHA, FEDSIMG_NOISE_SCALE, FEDSIMG_TRIM_K, FEDSIMG_BIAS_STRENGTH, FEDSIMG_CLIENT_BIAS_CSV, byzantine_client_ids, and trim_k in simulation_meta). The solution passes a rigorous deterministic, hyperparameter-coupled test harness validating complex simulation invariants."
RecommendationN/A - task is fine
Trajectory
Tool-by-tool agent trajectory
108 tool calls · 3 tool types · 108 steps
## Task Overview You are given a broken `/app/analysis.R` that simulates Byzantine-robust federated learning on an MNIST-like non-IID partition. The rules are defined by a single source of truth: - `/app/dataset_manifest.json` Your job is to fix **only** `/app/analysis.R` so it follows the manifest contract exactly and writes the required artifacts. ### Dataset note This is a simulation of FedAvg on the MNIST training distribution (60,000 train, 10,000 test, 10 classes). The manifest references MNIST as the inspiration/source distribution: - `torchvision.datasets.MNIST` (downloaded from Yann LeCun’s MNIST site: http://yann.lecun.com/exdb/mnist/) However, the task does not require downloading images; it uses the MNIST class-count structure (6,000 per class) for partitioning. ## Environment variables (grader-controlled) The grader sets (defaults shown): - `TASK_VARIANT` (`public` or `hidden`) - `FEDSIMG_SEED` (int; you must `set.seed()`) - `FEDSIMG_OUTPUT_DIR` (default: `outputs`) - `FEDSIMG_MANIFEST_PATH` (default: `/app/dataset_manifest.json`) Variant-controlled hyperparameters: - `FEDSIMG_ALPHA` (Dirichlet concentration) - `FEDSIMG_NOISE_SCALE` (Byzantine noise scale) - `FEDSIMG_SIGNAL_SCALE` (shared signal magnitude) - `FEDSIMG_TRIM_K` (Trimmed-Mean trim parameter) - `FEDSIMG_BIAS_STRENGTH` (magnitude of the client-bias prototype term) - `FEDSIMG_CLIENT_BIAS_CSV` (path to a CSV defining per-client class-bias weights; may differ by variant) ## Required outputs Write **all** outputs under: `output_dir = Sys.getenv("FEDSIMG_OUTPUT_DIR", "outputs")` Required artifacts: ```text {output_dir}/metrics_by_round.csv {output_dir}/dirichlet_gammas.csv {output_dir}/partition_matrix.csv {output_dir}/partition_summary.csv {output_dir}/byzantine_audit.csv {output_dir}/client_updates.csv {output_dir}/client_updates_clean.csv {output_dir}/analysis_summary.json {output_dir}/diagnostics.png ``` Exact schemas, sorting, and rounding are specified in `/app/dataset_manifest.json`. Treat the manifest as authoritative for: - per-output schemas and sort order, - which numeric fields are rounded vs full precision, - `analysis_summary.json` key requirements and semantics. ## Important constraints 1. Implement your fix in `/app/analysis.R` (no notebooks). 2. Honor all env vars (including hidden fixtures and hyperparameters). 3. Do not read verifier or solution files from `analysis.R` (e.g. `/tests`, `/solution`). 4. Outputs must be regenerated on each run (no stale cached outputs). 5. `analysis_summary.json` must be derived from the CSV outputs. 6. JSON precision matters: `analysis_summary.json` numeric values that mirror CSV values must match the CSV exactly (full precision; avoid lossy rounding). 7. `analysis_summary.json` must include `provenance.selection_histogram`: the per-client selection counts derived from `metrics_by_round.csv` (length = `n_clients`). 8. `analysis_summary.json.env` is for hyperparameter provenance; do not rely on it for output-path provenance. 9. Output semantics (including `byzantine_audit.csv`, `partition_matrix.csv`, and `client_updates.csv`) are specified in `/app/dataset_manifest.json`; follow them exactly.
Let me start by exploring the current state of the project.
bash
ls -la /app/
output
total 35
drwxrws--T  3 h2tagent h2tagent  4096 May 28 19:31 .
drwxr-xr-x 27 root     root      4096 May 28 23:06 ..
-rw-rw----  1 h2tagent h2tagent 14067 May 28 19:31 analysis.R
-rw-rw----  1 h2tagent h2tagent  7752 May 28 19:31 dataset_manifest.json
-rw-rw----  1 h2tagent h2tagent   420 May 28 19:31 example_client_bias.csv
drwxrws---  4 h2tagent h2tagent  4096 May 28 19:31 outputs
bash
cat /app/dataset_manifest.json
output
{
  "manifest_version": "1.0",
  "dataset": {
    "name": "MNIST",
    "source": "torchvision.datasets.MNIST",
    "download_url": "http://yann.lecun.com/exdb/mnist/",
    "train_size": 60000,
    "test_size": 10000,
    "n_classes": 10,
    "per_class_train": 6000
  },
  "simulation": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trimmed_mean": {
      "space": "coordinate",
      "trim_k_default": 2
    },
    "fedavg": {
      "weighting": "by_num_samples"
    },
    "update_generation": {
      "w_star": "rnorm(param_dim)",
      "class_prototypes": "matrix(rnorm(n_classes * param_dim), nrow=n_classes, ncol=param_dim)",
      "honest_update": "signal_scale*w_star + bias_strength*(bias_row %*% class_prototypes) + rnorm(param_dim, 0, honest_noise_sd)",
      "honest_noise_sd": 0.08
    }
  },
  "partition": {
    "method": "dirichlet_per_class",
    "alpha_default": 0.5,
    "allocation": "largest_remainder_hamilton",
    "min_client_samples": 20,
    "entropy_log_base": "e"
  },
  "client_bias": {
    "csv_schema": {
      "client_id": "int",
      "class_k_weight": "float (k=0..9; nonnegative; rows sum to 1)"
    },
    "required_columns": [
      "client_id",
      "class_0_weight",
      "class_1_weight",
      "class_2_weight",
      "class_3_weight",
      "class_4_weight",
      "class_5_weight",
      "class_6_weight",
      "class_7_weight",
      "class_8_weight",
      "class_9_weight"
    ],
    "bias_strength_default": 0.35,
    "env_path_var": "FEDSIMG_CLIENT_BIAS_CSV",
    "env_strength_var": "FEDSIMG_BIAS_STRENGTH"
  },
  "attack": {
    "byzantine_selection": "fixed_lowest_client_ids",
    "type": "sign_flip_plus_noise",
    "noise_scale_default": 2.0,
    "apply_timing": "before_aggregation",
    "byzantine_update": "-u_clean + rnorm(param_dim, 0, noise_scale)"
  },
  "metrics": {
    "accuracy_proxy": "sigmoid_cosine_similarity",
    "accuracy_sigmoid_k_default": 7.5,
    "accuracy_sigmoid_b_default": 2.5,
    "round_noise_sd": 0.005
  },
  "rounding": {
    "accuracy_decimals": 6,
    "loss_decimals": 6,
    "share_decimals": 6
  },
  "outputs": {
    "metrics_csv": {
      "path": "metrics_by_round.csv",
      "columns": [
        "round",
        "method",
        "accuracy",
        "cosine_sim",
        "update_norm",
        "byzantine_selected",
        "selected_clients"
      ],
      "sort_by": [
        "round",
        "method"
      ]
    },
    "partition_matrix_csv": {
      "path": "partition_matrix.csv",
      "columns": [
        "client_id",
        "class_0_count",
        "class_1_count",
        "class_2_count",
        "class_3_count",
        "class_4_count",
        "class_5_count",
        "class_6_count",
        "class_7_count",
        "class_8_count",
        "class_9_count"
      ],
      "sort_by": [
        "client_id"
      ],
      "row_semantics": {
        "rows": "one row per client_id for all clients",
        "counts": "class_k_count is the number of training samples of class k assigned to the client"
      }
    },
    "dirichlet_gammas_csv": {
      "path": "dirichlet_gammas.csv",
      "columns": [
        "class",
        "client_id",
        "gamma_raw",
        "p",
        "allocated_count"
      ],
      "sort_by": [
        "class",
        "client_id"
      ],
      "row_semantics": {
        "rows": "one row per (class, client_id)",
        "gamma_raw": "raw Dirichlet gamma draw (positive)",
        "p": "normalized probability gamma_raw/sum(gamma_raw) for that class",
        "allocated_count": "Hamilton allocation of per_class_train samples for that class using p"
      },
      "numeric_precision": {
        "gamma_raw": "full_precision",
        "p": "full_precision",
        "allocated_count": "integer"
      }
    },
    "partition_csv": {
      "path": "partition_summary.csv",
      "columns": [
        "client_id",
        "is_byzantine",
        "n_train",
        "label_e
…[truncated]
bash
cat /app/analysis.R
output
#!/usr/bin/env Rscript

suppressPackageStartupMessages({
  library(ggplot2)
  library(gridExtra)
  library(jsonlite)
})

`%||%` <- function(x, y) if (!is.null(x) && !is.na(x) && nzchar(as.character(x))) x else y

sigmoid <- function(x) 1.0 / (1.0 + exp(-x))

safe_dir_create <- function(path) {
  if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}

read_bias_csv <- function(path, n_clients, n_classes) {
  if (is.null(path) || !nzchar(path) || !file.exists(path)) {
    return(NULL)
  }
  df <- read.csv(path, stringsAsFactors = FALSE)
  req <- c("client_id", sprintf("class_%d_weight", 0:(n_classes - 1)))
  if (!all(req %in% names(df))) {
    stop("bias csv missing required columns")
  }
  df <- df[order(df$client_id), ]
  if (nrow(df) != n_clients || any(df$client_id != 0:(n_clients - 1))) {
    stop("bias csv must have exactly one row per client")
  }
  mat <- as.matrix(df[, req[-1], drop = FALSE])
  storage.mode(mat) <- "double"
  col_sums <- colSums(mat)
  col_sums[col_sums <= 0] <- 1
  sweep(mat, 2, col_sums, "/")
}

hamilton_alloc <- function(p, total) {
  p <- as.numeric(p)
  p <- p / sum(p)
  raw <- p * total
  alloc <- as.integer(round(raw))
  gap <- as.integer(total - sum(alloc))
  if (gap > 0) {
    ord <- order(raw - alloc, decreasing = TRUE)
    alloc[ord[seq_len(gap)]] <- alloc[ord[seq_len(gap)]] + 1L
  } else if (gap < 0) {
    ord <- order(raw - alloc, decreasing = FALSE)
    take <- ord[seq_len(abs(gap))]
    alloc[take] <- pmax(0L, alloc[take] - 1L)
  }
  as.integer(alloc)
}

repair_partition <- function(partition, min_client_samples) {
  n_train <- rowSums(partition)
  while (min(n_train) < min_client_samples) {
    low <- which.min(n_train)
    high <- which.max(n_train)
    donor_class <- which.max(partition[high, ])
    if (partition[high, donor_class] <= 0) break
    partition[high, donor_class] <- partition[high, donor_class] - 1L
    partition[low, donor_class] <- partition[low, donor_class] + 1L
    n_train <- rowSums(partition)
  }
  partition
}

dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
  part <- matrix(0L, nrow = n_clients, ncol = n_classes)
  gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
  for (k in seq_len(n_classes)) {
    g <- rgamma(n_clients, shape = alpha, rate = 1)
    gammas[, k] <- g
    part[, k] <- hamilton_alloc(g, per_class_total)
  }
  part <- repair_partition(part, min_client_samples)
  list(partition = part, gammas = gammas)
}

shannon_entropy <- function(counts) {
  s <- sum(counts)
  if (s <= 0) return(0)
  p <- counts / s
  p <- p[p > 0]
  -sum(p * log10(p))
}

row_trimmed_mean <- function(mat, trim_k) {
  keep <- order(apply(mat, 1, function(x) sqrt(sum(x * x))))[(trim_k + 1):(nrow(mat) - trim_k)]
  colMeans(mat[keep, , drop = FALSE])
}

run_sim <- function() {
  task_variant <- Sys.getenv("TASK_VARIANT", "public")
  seed <- as.integer(Sys.getenv("FEDSIMG_SEED", "0"))
  set.seed(seed)

  output_dir <- Sys.getenv("FEDSIMG_OUTPUT_DIR", "outputs")
  manifest_path <- Sys.getenv("FEDSIMG_MANIFEST_PATH", "/app/dataset_manifest.json")
  safe_dir_create(output_dir)

  manifest <- fromJSON(manifest_path, simplifyVector = TRUE)

  n_clients <- as.integer(manifest$simulation$n_clients)
  n_byz <- as.integer(manifest$simulation$n_byzantine)
  clients_per_round <- as.integer(manifest$simulation$clients_per_round)
  n_rounds <- as.integer(manifest$simulation$n_rounds)
  param_dim <- as.integer(manifest$simulation$param_dim)
  n_classes <- as.integer(manifest$dataset$n_classes)
  per_class <- as.integer(manifest$dataset$per_class_train)

  alpha <- as.numeric(manifest$partition$alpha_default)
  noise_scale <- as.numeric(manifest$attack$noise_scale_default)
  signal_scale <- as.numeric(Sys.getenv("FEDSIMG_SIGNAL_SCALE", "0.12"))
  trim_k <- as.integer(manifest$simulation$trimmed_mean$trim_k_default)
  bias_strength <- as.numeric(manifest$client_bias$bias_strength_default)

  bias_csv <- Sys.getenv(m
…[truncated]
bash
cat /app/example_client_bias.csv
output
client_id,class_0_weight,class_1_weight,class_2_weight,class_3_weight,class_4_weight,class_5_weight,class_6_weight,class_7_weight,class_8_weight,class_9_weight
0,0.28,0.10,0.07,0.06,0.06,0.06,0.07,0.08,0.12,0.10
1,0.05,0.06,0.09,0.14,0.15,0.14,0.12,0.10,0.08,0.07
2,0.08,0.08,0.08,0.08,0.08,0.08,0.12,0.14,0.14,0.12
3,0.04,0.05,0.06,0.07,0.10,0.13,0.15,0.15,0.14,0.11
4,0.16,0.14,0.12,0.10,0.09,0.08,0.07,0.06,0.10,0.08
Now let me analyze all the issues in the current script: 1. **Entropy:** The `shannon_entropy` function uses `log10` but manifest says `entropy_log_base: "e"`. 2. **Byzantine IDs:** The attack section says `"byzantine_selection": "fixed_lowest_client_ids"` but code uses `(n_clients - n_byz):(n_clients - 1)` (highest IDs). Should be `0:(n_byz - 1)` (lowest). 3. **Byzantine attack:** `"byzantine_update": "-u_clean + rnorm(param_dim, 0, noise_scale)"` but code uses `(-0.8) * u_clean + rnorm(..., noise_scale / 2)`. 4. **Environment variables not read:** - `FEDSIMG_ALPHA` - not being read - `FEDSIMG_NOISE_SCALE` - not being read - `FEDSIMG_TRIM_K` - not being read - `FEDSIMG_BIAS_STRENGTH` - not being read (uses default) 5. **Env in summary:** Missing required env keys: `FEDSIMG_ALPHA`, `FEDSIMG_NOISE_SCALE`, `FEDSIMG_TRIM_K`, `FEDSIMG_BIAS_STRENGTH`. 6. **simulation_meta missing `trim_k`** - required. 7. **final_metrics delta:** Uses `abs()` but manifest says `trimmed_mean_accuracy - fedavg_accuracy` (no abs). 8. **Trimmed mean space:** Manifest says `"space": "coordinate"` but code uses row-based norm ordering (trims by L2 norm). Should trim each coordinate independently. 9. **partition_summary's label_entropy:** manifest says `label_entropy_decimals: 6` (under outputs.partition_csv.rounding). The code uses `loss_decimals` but that's 6 too. It should use natural log though. 10. **partition_summary's top_label_share:** The share is `count[top]/total_samples_for_client` not `max_count/per_class`. Let me re-read... "top_label_share" - typically this is the fraction of this client's samples in the top label: `max(counts_for_client) / sum(counts_for_client)`. The code uses `max(partition[i, ]) / per_class` which is wrong. 11. **Bias direction:** The honest update should use the bias CSV when provided. The bias_mat is the CSV-loaded bias. But currently, the code ignores `bias_mat` and uses empirical_mix. It should use `bias_mat` if available (the CSV rows), otherwise compute the empirical mix from partition. Actually, looking at the code, the bias_mat is set to `partition / rowSums` if null. Then inside the loop, `empirical_mix` is re-derived from partition. So `bias_mat` is unused! Let me fix this. Actually, re-reading, `bias_mat` is defined earlier then overwritten `bias_mat <- partition / pmax(rowSums(partition), 1)` if null. Then `empirical_mix` is computed from partition. So bias_mat is never used. Need to use `bias_mat[cid + 1, ]`. 12. **dirichlet_gammas:** The `allocated_count` column should be the Hamilton allocation of per_class samples (before repair). Currently it uses `partition[, k + 1]` which is after repair. Actually the manifest says "Hamilton allocation of per_class_train samples for that class using p", so it seems they want the initial Hamilton allocation (without repair). Wait, looking at the code, `dirichlet_partition` returns `part_out$partition` which is post-repair. The `allocated_count` in gdf is assigned from `partition[, k + 1]` (final partition). But the manifest says "Hamilton allocation using p" which sounds pre-repair. Hmm, but the manifest also requires `partition_matrix.csv` to sum to the final partition. Let me think about this... Actually if `allocated_count` is Hamilton allocation only, but partition is after repair, then sum of allocated_count for each class could be `per_class_total` but the actual partition won't match. Looking at the column name "allocated_count" it seems to represent what the Hamilton allocator assigned from the Dirichlet probabilities. Let me store the initial Hamilton allocations and output them. Actually, reading more carefully: "allocated_count: Hamilton allocation of per_class_train samples for that class using p". The semantics are "for that class using p" which is the Dirichlet-derived probability. This should be the initial Hamilton allocation before repair. Let me fix this. But that's potentially an issue because then `partition_matrix.csv` wo …[truncated]
Let me analyze the issues in the current script and the manifest requirements, then rewrite the script.
bash
ls /app/outputs/
output
hidden
public
bash
ls /app/outputs/public/ 2>/dev/null; ls /app/outputs/hidden/ 2>/dev/null
output
<system>Tool ran without output or errors</system>
bash
which Rscript; Rscript --version
output
/usr/bin/Rscript
Rscript (R) version 4.3.3 (2024-02-29)
bash
Rscript -e 'library(ggplot2); library(gridExtra); library(jsonlite); cat("ok\n")'
output
ok
Now let me list all the issues I identified and plan the fix: ### Bugs in current analysis.R: 1. **`shannon_entropy`**: uses `log10` but manifest says `entropy_log_base: "e"` → should use `log` (natural log). 2. **`byz_ids`**: Uses highest client IDs `(n_clients - n_byz):(n_clients - 1)` but manifest says `"fixed_lowest_client_ids"` → should be `0:(n_byz - 1)`. 3. **Byzantine attack formula**: Code does `(-0.8) * u_clean + rnorm(..., noise_scale/2)`. Manifest says `-u_clean + rnorm(param_dim, 0, noise_scale)`. 4. **Environment variables not read**: `FEDSIMG_ALPHA`, `FEDSIMG_NOISE_SCALE`, `FEDSIMG_TRIM_K`, `FEDSIMG_BIAS_STRENGTH` all missed or not used. 5. **`env` in summary JSON**: Missing required keys from `env_required_keys`. 6. **simulation_meta**: Missing `trim_k`. 7. **final_metrics.delta**: Uses `abs()`, manifest says `trimmed_mean_accuracy - fedavg_accuracy` (signed). 8. **Trimmed mean**: Currently row-trimmed-by-norm; should be coordinate-wise trimmed mean per manifest. 9. **`top_label_share`**: Uses `max(partition[i, ]) / per_class`, but should be `max(counts) / sum(counts)` for that client. 10. **`label_entropy` uses `loss_decimals`**: Manifest specifies `label_entropy_decimals: 6` for partition_csv. Happens to equal 6, but be explicit. 11. **`bias_mat` (CSV) is loaded but never used**: Should use CSV rows when provided, otherwise the per-client `empirical_mix` from partition. 12. **`dirichlet_gammas.csv`'s `allocated_count`**: Should be the initial Hamilton allocation (before repair), not post-repair partition. 13. **FedAvg weighting**: Uses global weights (normalized by total train over all clients), but manifest says "n_train(client)/sum(n_train of selected clients)". So weights should be re-normalized over selected clients. 14. **`byzantine_audit.update_norm`**: Uses clean_norms (pre-attack); should be submitted_norms (post-attack for byzantine), and 0 if not selected. 15. **`byzantine_audit.client_weight`**: Uses global over all clients; should be over selected only, 0 if not selected. 16. **`client_updates_clean.csv`'s `u_submitted`**: Code uses `u_clean` for u_submitted. Bug. 17. **`selection_histogram`**: Should be derived from `metrics_by_round.csv` (re-parse selected_clients column). 18. **`final_metrics`**: Must match CSV exactly. Read back from the written CSV. 19. Make sure numeric JSON precision matches CSV (full precision). Since CSV values are rounded, we should use the rounded values from the CSV directly. Let me also double check: - `FEDSIMG_SIGNAL_SCALE` default , manifest doesn't show a default. Current code uses 0.12. Let me keep. - Trim_k env var exists. Let me write the complete fixed script now.
Now I have a clear picture. Let me identify all the bugs: 1. `shannon_entropy` uses `log10`, manifest says base `e` 2. `byz_ids` uses highest IDs; manifest says "fixed_lowest_client_ids" 3. Byzantine attack is `-0.8 * u_clean + noise/2`; manifest says `-u_clean + rnorm(0, noise_scale)` 4. Env vars `FEDSIMG_ALPHA`, `FEDSIMG_NOISE_SCALE`, `FEDSIMG_TRIM_K`, `FEDSIMG_BIAS_STRENGTH` not honored 5. `final_metrics.delta` uses `abs()`, should be signed 6. `trim_k` missing from `simulation_meta` 7. Trimmed mean is row-norm; manifest requires coordinate-wise 8. `top_label_share` uses `/per_class`; should use `/sum(counts)` 9. `bias_mat` from CSV is ignored; `empirical_mix` is recomputed 10. `allocated_count` currently reflects post-repair partition, not initial Hamilton alloc 11. FedAvg weights normalized over ALL clients (should be only selected) 12. Audit `update_norm` uses clean norms; should be submitted & 0 if not selected 13. Audit `client_weight` is global; must be only over selected 14. `u_submitted` in clean csv actually stores `u_clean` 15. `selection_histogram` must be derived from `metrics_by_round.csv` 16. Missing env keys in JSON 17. Entropy rounding key
str_replace_based_edit_tool
command: create
path: /app/analysis.R
file_text: #!/usr/bin/env Rscript

suppressPackageStartupMessages({
  library(ggplot2)
  library(gridExtra)
  library(jsonlite)
})

sigmoid <- function(x) 1.0 / (1.0 + exp(-x))

safe_dir_create <- function(path) {
  if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
}

env_get <- function(name, default) {
  v <- Sys.getenv(name, unset = NA_character_)
  if (is.na(v) || !nzchar(v)) default else v
}

read_bias_csv <- function(path, n_clients, n_classes) {
  if (is.null(path) || !nzchar(path) || !file.exists(path)) {
    return(NULL)
  }
  df <- read.csv(path, stringsAsFactors = FALSE)
  req <- c("client_id", sprintf("class_%d_weight", 0:(n_classes - 1)))
  if (!all(req %in% names(df))) {
    stop("bias csv missing required columns")
  }
  bias_mat <- matrix(0, nrow = n_clients, ncol = n_classes)
  for (i in seq_len(nrow(df))) {
    cid <- as.integer(df$client_id[i])
    if (cid >= 0 && cid < n_clients) {
      row_vals <- as.numeric(df[i, req[-1]])
      row_vals[!is.finite(row_vals) | row_vals < 0] <- 0
      s <- sum(row_vals)
      if (s > 0) row_vals <- row_vals / s
      bias_mat[cid + 1, ] <- row_vals
    }
  }
  bias_mat
}

hamilton_alloc <- function(p, total) {
  p <- as.numeric(p)
  s <- sum(p)
  if (s <= 0) {
    # uniform fallback
    p <- rep(1 / length(p), length(p))
  } else {
    p <- p / s
  }
  raw <- p * total
  floor_alloc <- as.integer(floor(raw))
  remainder <- raw - floor_alloc
  gap <- as.integer(total - sum(floor_alloc))
  if (gap > 0) {
    ord <- order(remainder, decreasing = TRUE)
    floor_alloc[ord[seq_len(gap)]] <- floor_alloc[ord[seq_len(gap)]] + 1L
  } else if (gap < 0) {
    ord <- order(remainder, decreasing = FALSE)
    take <- ord[seq_len(abs(gap))]
    floor_alloc[take] <- pmax(0L, floor_alloc[take] - 1L)
  }
  as.integer(floor_alloc)
}

repair_partition <- function(partition, min_client_samples) {
  n_train <- rowSums(partition)
  guard <- 0L
  max_iters <- 100000L
  while (min(n_train) < min_client_samples && guard < max_iters) {
    low <- which.min(n_train)
    high <- which.max(n_train)
    if (low == high) break
    donor_class <- which.max(partition[high, ])
    if (partition[high, donor_class] <= 0) break
    partition[high, donor_class] <- partition[high, donor_class] - 1L
    partition[low, donor_class] <- partition[low, donor_class] + 1L
    n_train <- rowSums(partition)
    guard <- guard + 1L
  }
  partition
}

dirichlet_partition <- function(n_clients, n_classes, per_class_total, alpha, min_client_samples) {
  gammas <- matrix(0.0, nrow = n_clients, ncol = n_classes)
  hamilton_mat <- matrix(0L, nrow = n_clients, ncol = n_classes)
  for (k in seq_len(n_classes)) {
    g <- rgamma(n_clients, shape = alpha, rate = 1)
    gammas[, k] <- g
    hamilton_mat[, k] <- hamilton_alloc(g, per_class_total)
  }
  partition <- repair_partition(hamilton_mat, min_client_samples)
  list(partition = partition, gammas = gammas, hamilton_alloc = hamilton_mat)
}

# Natural-log Shannon entropy (manifest: entropy_log_base = e)
shannon_entropy <- function(counts) {
  s <- sum(counts)
  if (s <= 0) return(0)
  p <- counts / s
  p <- p[p > 0]
  -sum(p * log(p))
}

# Coordinate-wise trimmed mean (manifest: trimmed_mean.space = "coordinate")
coord_trimmed_mean <- function(mat, trim_k) {
  n <- nrow(mat)
  keep_n <- n - 2L * trim_k
  if (keep_n < 1L) stop("trim_k too large for number of rows")
  apply(mat, 2, function(col) {
    s <- sort(col)
    mean(s[(trim_k + 1L):(n - trim_k)])
  })
}

run_sim <- function() {
  task_variant <- env_get("TASK_VARIANT", "public")
  seed <- as.integer(env_get("FEDSIMG_SEED", "0"))
  set.seed(seed)

  output_dir <- env_get("FEDSIMG_OUTPUT_DIR", "outputs")
  manifest_path <- env_get("FEDSIMG_MANIFEST_PATH", "/app/dataset_manifest.json")
  safe_dir_create(output_dir)

  manifest <- fromJSON(manifest_path, simplifyVector = TRUE)

  n_clients <- as.integer(manifest$simulation$n_clients)
  n_byz <- as.integer(man
…[truncated]
output
Existing file was successfully overwritten at /app/analysis.R
Now let me test this:
bash
cd /app && rm -rf outputs/test_run && mkdir -p outputs/test_run && \
  TASK_VARIANT=public \
  FEDSIMG_SEED=42 \
  FEDSIMG_OUTPUT_DIR=outputs/test_run \
  FEDSIMG_ALPHA=0.5 \
  FEDSIMG_NOISE_SCALE=2.0 \
  FEDSIMG_SIGNAL_SCALE=0.12 \
  FEDSIMG_TRIM_K=2 \
  FEDSIMG_BIAS_STRENGTH=0.35 \
  FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \
  Rscript analysis.R 2>&1 | tail -30
output
<system>Tool ran without output or errors</system>
bash
ls /app/outputs/test_run/
output
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
All artifacts are generated. Let me verify each one against the manifest:
bash
head -5 /app/outputs/test_run/metrics_by_round.csv; echo "---"; wc -l /app/outputs/test_run/metrics_by_round.csv
output
"round","method","accuracy","cosine_sim","update_norm","byzantine_selected","selected_clients"
0,"fedavg",0.63209,0.406639,3.401821,1,"7 10 14 19 31 40 51 55 56 57 60 61 62 65 67 68 75 80 90 94"
0,"trimmed_mean",0.988682,0.985882,1.895403,1,"7 10 14 19 31 40 51 55 56 57 60 61 62 65 67 68 75 80 90 94"
1,"fedavg",0.86852,0.585828,3.253546,2,"1 6 13 15 19 30 32 35 36 38 42 47 53 55 64 72 74 78 90 95"
1,"trimmed_mean",0.988131,0.991961,1.940637,2,"1 6 13 15 19 30 32 35 36 38 42 47 53 55 64 72 74 78 90 95"
---
17 /app/outputs/test_run/metrics_by_round.csv
bash
cat /app/outputs/test_run/analysis_summary.json
output
{
  "task_variant": "public",
  "seed": 42,
  "env": {
    "FEDSIMG_ALPHA": 0.5,
    "FEDSIMG_NOISE_SCALE": 2,
    "FEDSIMG_SIGNAL_SCALE": 0.12,
    "FEDSIMG_TRIM_K": 2,
    "FEDSIMG_BIAS_STRENGTH": 0.35,
    "FEDSIMG_CLIENT_BIAS_CSV": "/app/example_client_bias.csv"
  },
  "simulation_meta": {
    "n_clients": 100,
    "n_byzantine": 10,
    "clients_per_round": 20,
    "n_rounds": 8,
    "param_dim": 256,
    "trim_k": 2
  },
  "byzantine_client_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
  "final_metrics": {
    "trimmed_mean_accuracy": 0.998764,
    "fedavg_accuracy": 0.980094,
    "delta": 0.01867
  },
  "provenance": {
    "manifest_md5": "475adf00a65314160823670bdaef093c",
    "selection_histogram": [2, 1, 1, 3, 1, 3, 1, 1, 0, 2, 2, 2, 3, 2, 3, 2, 0, 1, 1, 3, 1, 3, 0, 0, 2, 2, 0, 0, 0, 0, 1, 3, 2, 2, 1, 3, 3, 2, 3, 2, 2, 3, 2, 1, 2, 1, 2, 2, 3, 2, 2, 2, 0, 1, 2, 3, 2, 1, 1, 3, 3, 2, 2, 2, 3, 1, 2, 1, 1, 1, 2, 1, 1, 0, 1, 4, 2, 1, 1, 0, 3, 1, 1, 1, 0, 0, 2, 0, 2, 2, 3, 0, 2, 1, 2, 3, 3, 1, 1, 0]
  }
}
bash
head -5 /app/outputs/test_run/dirichlet_gammas.csv; echo "---total rows---"; wc -l /app/outputs/test_run/dirichlet_gammas.csv
output
"class","client_id","gamma_raw","p","allocated_count"
0,0,0.760516834810734,0.0165288490248016,99
0,1,0.293697522769724,0.00638313550814045,38
0,2,1.86634352361331,0.0405625607721065,243
0,3,1.48173928793115,0.0322036855244973,193
---total rows---
1001 /app/outputs/test_run/dirichlet_gammas.csv
bash
head -5 /app/outputs/test_run/partition_matrix.csv; echo "---"; wc -l /app/outputs/test_run/partition_matrix.csv
output
"client_id","class_0_count","class_1_count","class_2_count","class_3_count","class_4_count","class_5_count","class_6_count","class_7_count","class_8_count","class_9_count"
0,99,20,180,32,26,72,46,229,3,1
1,38,2,29,0,5,3,311,291,71,6
2,243,354,1,35,11,20,53,21,16,5
3,193,2,8,215,49,130,11,1,28,3
---
101 /app/outputs/test_run/partition_matrix.csv
bash
head -5 /app/outputs/test_run/partition_summary.csv; echo "---"; wc -l /app/outputs/test_run/partition_summary.csv
output
"client_id","is_byzantine","n_train","label_entropy","top_label","top_label_share"
0,1,708,1.792905,7,0.323446
1,1,756,1.339652,6,0.411376
2,1,759,1.427729,1,0.466403
3,1,640,1.563247,3,0.335938
---
101 /app/outputs/test_run/partition_summary.csv
bash
head -5 /app/outputs/test_run/byzantine_audit.csv; echo "---"; wc -l /app/outputs/test_run/byzantine_audit.csv
output
"round","client_id","is_byzantine","is_selected","client_weight","update_norm"
0,0,1,0,0,0
0,1,1,0,0,0
0,2,1,0,0,0
0,3,1,0,0,0
---
801 /app/outputs/test_run/byzantine_audit.csv
100 clients × 8 rounds = 800 audit rows + header = 801.
bash
head -3 /app/outputs/test_run/client_updates.csv; echo "---"; wc -l /app/outputs/test_run/client_updates.csv
echo "---"; head -3 /app/outputs/test_run/client_updates_clean.csv; wc -l /app/outputs/test_run/client_updates_clean.csv
output
"round","client_id","coord","update_value"
0,7,0,0.903569
0,7,1,-0.575743
---
40961 /app/outputs/test_run/client_updates.csv
---
"round","client_id","coord","u_clean","u_submitted"
0,7,0,-0.085945,0.903569
0,7,1,0.084368,-0.575743
40961 /app/outputs/test_run/client_updates_clean.csv
20 clients × 256 coords × 8 rounds = 40,960 rows + header = 40,961. And u_clean ≠ u_submitted (this row was byzantine client 7 → inverted sign is visible). Let me verify the Hamilton allocation sums per class = 6000 in dirichlet_gammas:
bash
cd /app && Rscript -e '
d <- read.csv("outputs/test_run/dirichlet_gammas.csv")
agg <- aggregate(allocated_count ~ class, d, sum)
print(agg)
cat("p sums per class (should be 1):\n")
print(aggregate(p ~ class, d, sum))

cat("partition matrix column sums (should each equal per-class total after repair):\n")
pm <- read.csv("outputs/test_run/partition_matrix.csv")
print(colSums(pm[,-1]))
'
output
class allocated_count
1      0            6000
2      1            6000
3      2            6000
4      3            6000
5      4            6000
6      5            6000
7      6            6000
8      7            6000
9      8            6000
10     9            6000
p sums per class (should be 1):
   class p
1      0 1
2      1 1
3      2 1
4      3 1
5      4 1
6      5 1
7      6 1
8      7 1
9      8 1
10     9 1
partition matrix column sums (should each equal per-class total after repair):
class_0_count class_1_count class_2_count class_3_count class_4_count 
         6000          6000          6000          6000          6000 
class_5_count class_6_count class_7_count class_8_count class_9_count 
         6000          6000          6000          6000          6000
All class counts sum to 6000 correctly. Let me verify minimum client samples:
bash
cd /app && Rscript -e '
ps <- read.csv("outputs/test_run/partition_summary.csv")
cat("min n_train:", min(ps$n_train), "max:", max(ps$n_train), "sum:", sum(ps$n_train),"\n")
cat("byzantine count (is_byzantine==1):", sum(ps$is_byzantine),"\n")
cat("byzantine clients:", ps$client_id[ps$is_byzantine==1],"\n")
'
output
min n_train: 125 max: 1199 sum: 60000 
byzantine count (is_byzantine==1): 10 
byzantine clients: 0 1 2 3 4 5 6 7 8 9
60000 total, byzantine are 0..9 (fixed_lowest_client_ids). Let me verify audit/weights correctness:
bash
cd /app && Rscript -e '
a <- read.csv("outputs/test_run/byzantine_audit.csv")
# Each round: sum of client_weight over selected should be 1, and only is_selected rows non-zero.
by_round <- aggregate(client_weight ~ round, a, sum)
print(by_round)
cat("any non-selected with weight != 0?", any(a$client_weight[a$is_selected==0] != 0),"\n")
cat("any non-selected with update_norm != 0?", any(a$update_norm[a$is_selected==0] != 0),"\n")
cat("selected per round (should be 20):",
    table(a$round, a$is_selected)[, "1"], "\n")
'
output
round client_weight
1     0      0.999999
2     1      0.999999
3     2      1.000002
4     3      1.000001
5     4      0.999999
6     5      0.999998
7     6      0.999999
8     7      0.999999
any non-selected with weight != 0? FALSE 
any non-selected with update_norm != 0? FALSE 
selected per round (should be 20): 20 20 20 20 20 20 20 20
Weights sum ≈ 1 (rounding to 6 digits); all non-selected have 0 weight and 0 update_norm; 20 selected per round. Let me verify metrics summary correctness and ordering:
bash
cd /app && Rscript -e '
m <- read.csv("outputs/test_run/metrics_by_round.csv", stringsAsFactors=FALSE)
# check sort
cat("sorted by round,method?",
    all(m$round == sort(m$round)) &&
    all(m$method == ave(m$method, m$round, FUN=function(x) sort(x))),"\n")

final_tm <- m$accuracy[m$method=="trimmed_mean" & m$round==max(m$round)]
final_fed <- m$accuracy[m$method=="fedavg" & m$round==max(m$round)]
cat("final tm:", final_tm, "final fed:", final_fed, "delta:", final_tm - final_fed, "\n")

# check selection histogram matches metrics csv
sel <- m$selected_clients[m$method=="fedavg"]
hist_v <- rep(0L, 100)
for (s in sel) {
  ids <- as.integer(strsplit(trimws(s), "\\s+")[[1]])
  for (id in ids) hist_v[id+1L] <- hist_v[id+1L] + 1L
}
cat("total selections:", sum(hist_v), "(should be 8*20 = 160)\n")

library(jsonlite)
j <- fromJSON("outputs/test_run/analysis_summary.json")
cat("JSON selection_histogram matches derived?",
    identical(as.integer(j$provenance$selection_histogram), hist_v), "\n")
cat("final_metrics matches CSV values exactly?",
    j$final_metrics$trimmed_mean_accuracy == final_tm &&
    j$final_metrics$fedavg_accuracy == final_fed &&
    j$final_metrics$delta == final_tm - final_fed, "\n")
'
output
sorted by round,method? TRUE 
final tm: 0.998764 final fed: 0.980094 delta: 0.01867 
total selections: 160 (should be 8*20 = 160)
JSON selection_histogram matches derived? TRUE 
final_metrics matches CSV values exactly? FALSE
JSON final_metrics has a precision issue! Let me check what's happening:
bash
cd /app && Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/test_run/analysis_summary.json")
m <- read.csv("outputs/test_run/metrics_by_round.csv", stringsAsFactors=FALSE)
final_tm <- m$accuracy[m$method=="trimmed_mean" & m$round==max(m$round)]
final_fed <- m$accuracy[m$method=="fedavg" & m$round==max(m$round)]

cat("tm accuracy in CSV:", sprintf("%.20f",final_tm),"\n")
cat("tm accuracy in JSON:", sprintf("%.20f",j$final_metrics$trimmed_mean_accuracy),"\n")
cat("fed:", sprintf("%.20f",final_fed),"  vs JSON:", sprintf("%.20f",j$final_metrics$fedavg_accuracy),"\n")
cat("delta CSV:", sprintf("%.20f", final_tm - final_fed), " JSON:", sprintf("%.20f",j$final_metrics$delta),"\n")
'
output
tm accuracy in CSV: 0.99876399999999998514 
tm accuracy in JSON: 0.99876399999999998514 
fed: 0.98009400000000002073   vs JSON: 0.98009400000000002073 
delta CSV: 0.01866999999999996440  JSON: 0.01866999999999999910
OK, the tm and fed accuracy match exactly. The delta computed differs by a tiny amount because `write_json` with `digits=NA` prints `0.01867` and the re-parse gets a slightly different binary representation. But actually they are exactly equal now: Looking more carefully: Both CSV values match , the difference is just because we computed delta = tm - fed and then wrote, then reread back. When written in JSON it becomes "0.01867" (likely the minimum representation), and when parsed back it's a slightly different float. Actually, my test compared j$final_metrics$delta with (final_tm - final_fed) both computed from R. The delta in JSON after round-trip is slightly different from `final_tm - final_fed` computed from CSV. Let me verify , the constraint says "JSON numeric values that mirror CSV values must match the CSV exactly". The delta is NOT a direct mirror of a CSV value; it's a computed quantity (diff of two CSV values). But semantically, it should be the diff of the CSV values. The issue is that `jsonlite::write_json(x, digits=NA)` writes minimum representation. Then if someone re-reads and compares, they might get slight float differences. But this is a fundamental issue of JSON precision. Actually, looking at the values: - CSV delta value after diff: `0.01866999999999996440` - JSON delta value after round-trip: `0.01866999999999999910` These differ by 3.47e-17 (1 ulp). When written using `digits=NA`, jsonlite writes the shortest string that round-trips, but the binary representation can have 1-ulp differences when subtracting similar values. Hmm, let me check if this test is just an artifact. A grader that reads the JSON and the CSV separately, then computes the diff from CSV, and compares , yes, that's a potential issue. Let me try using a higher precision output. Actually, let me ensure I'm writing `final_tm - final_fed` precisely in JSON by using `format` with high precision. Actually, the simplest is to just write the JSON delta as the actual double value that I have in R. But jsonlite round-trips through text. Let me try this approach: compute delta = tm_acc_csv_read - fed_acc_csv_read after re-reading. Actually, I already do this. The issue is purely in the text representation in JSON. Actually when I check: both tm_acc and fed_acc in JSON match CSV exactly via readCSV roundtrip , those should be numerically exact because they're single values. It's only the computed delta that has a tiny floating-point issue. Let me actually test this: if the grader re-reads `tm` and `fed` from JSON and CSV and computes `tm_csv - fed_csv` == `j$delta`, we need those to be equal. The tm_csv is `0.998764` (exact double representation of this decimal), fed_csv is `0.980094` (exact double representation). Then diff is 0.998764 - 0.980094 = 0.01866999999999996440 (depending on binary). In JSON, my delta is `0.01867` (6 digits). When read back: 0.01866999999999999910 approx. So the numbers don't match. The constraint says "numeric values that mirror CSV values must match exactly". The delta is strictly speaking NOT a CSV value. But to be safe let me see if jsonlite supports higher precision. Actually, `digits=NA` should be "as much as needed to round-trip". Let me check:
There's a tiny float precision issue with the computed `delta`. Let me investigate:
bash
cd /app && Rscript -e '
# jsonlite with digits=NA should give round-trippable. Test:
library(jsonlite)
x <- 0.998764 - 0.980094
cat("x=",sprintf("%.20f",x),"\n")
j <- toJSON(list(d=x), auto_unbox=TRUE, digits=NA)
cat("json:", j,"\n")
y <- fromJSON(j)
cat("y=",sprintf("%.20f", y$d),"\n")
cat("x==y?", x==y$d,"\n")
'
output
x= 0.01866999999999996440 
json: {"d":0.01867} 
y= 0.01866999999999999910 
x==y? FALSE
So `digits=NA` does NOT give full precision in jsonlite. I need to explicitly set high digits.
bash
cd /app && Rscript -e '
library(jsonlite)
x <- 0.998764 - 0.980094
# try digits=17
for (d in c(15, 16, 17, 20)) {
  j <- toJSON(list(d=x), auto_unbox=TRUE, digits=d)
  y <- fromJSON(j)
  cat(sprintf("digits=%d: json=%s  roundtrip_eq=%s\n", d, j, x==y$d))
}
'
output
digits=15: json={"d":0.01866999999999996}  roundtrip_eq=FALSE
digits=16: json={"d":0.018669999999999964}  roundtrip_eq=TRUE
digits=17: json={"d":0.018669999999999964}  roundtrip_eq=TRUE
digits=20: json={"d":0.018669999999999964}  roundtrip_eq=TRUE
I need `digits=17` for exact round-trip. Let me update:
str_replace_based_edit_tool
command: str_replace
path: /app/analysis.R
old_str:   write_json(
    summary,
    file.path(output_dir, manifest$outputs$summary_json$path),
    pretty = TRUE,
    auto_unbox = TRUE,
    digits = NA
  )
new_str:   # Use digits=17 so doubles round-trip losslessly in the JSON (avoids
  # imperceptible mismatch between CSV-read values and JSON-read numerics).
  write_json(
    summary,
    file.path(output_dir, manifest$outputs$summary_json$path),
    pretty = TRUE,
    auto_unbox = TRUE,
    digits = 17
  )
output
The file /app/analysis.R has been edited successfully.
bash
cd /app && rm -rf outputs/test_run && mkdir -p outputs/test_run && \
  TASK_VARIANT=public \
  FEDSIMG_SEED=42 \
  FEDSIMG_OUTPUT_DIR=outputs/test_run \
  FEDSIMG_ALPHA=0.5 \
  FEDSIMG_NOISE_SCALE=2.0 \
  FEDSIMG_SIGNAL_SCALE=0.12 \
  FEDSIMG_TRIM_K=2 \
  FEDSIMG_BIAS_STRENGTH=0.35 \
  FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv \
  Rscript analysis.R 2>&1 | tail -5
output
<system>Tool ran without output or errors</system>
bash
cd /app && Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/test_run/analysis_summary.json")
m <- read.csv("outputs/test_run/metrics_by_round.csv", stringsAsFactors=FALSE)
final_tm <- m$accuracy[m$method=="trimmed_mean" & m$round==max(m$round)]
final_fed <- m$accuracy[m$method=="fedavg" & m$round==max(m$round)]
cat("tm eq?", j$final_metrics$trimmed_mean_accuracy == final_tm,"\n")
cat("fed eq?", j$final_metrics$fedavg_accuracy == final_fed,"\n")
cat("delta eq?", j$final_metrics$delta == (final_tm - final_fed),"\n")
cat("delta CSV:", sprintf("%.20f", final_tm - final_fed), " JSON:", sprintf("%.20f",j$final_metrics$delta),"\n")
'
output
tm eq? TRUE 
fed eq? TRUE 
delta eq? TRUE 
delta CSV: 0.01866999999999996440  JSON: 0.01866999999999996440
All numeric mirrors are exact. Now let me verify more thoroughly:
bash
cd /app && Rscript -e '
library(jsonlite)

# Verify summary JSON has all required keys
j <- fromJSON("outputs/test_run/analysis_summary.json")

required_top <- c("task_variant","seed","env","simulation_meta",
                  "byzantine_client_ids","final_metrics","provenance")
cat("all top-level keys present?", all(required_top %in% names(j)),"\n")

env_required <- c("FEDSIMG_ALPHA","FEDSIMG_NOISE_SCALE","FEDSIMG_SIGNAL_SCALE",
                  "FEDSIMG_TRIM_K","FEDSIMG_BIAS_STRENGTH","FEDSIMG_CLIENT_BIAS_CSV")
cat("env keys present?", all(env_required %in% names(j$env)),"\n")
# env MUST exclude FEDSIMG_OUTPUT_DIR and FEDSIMG_MANIFEST_PATH
cat("env excludes OUTPUT_DIR?", !("FEDSIMG_OUTPUT_DIR" %in% names(j$env)),"\n")
cat("env excludes MANIFEST_PATH?", !("FEDSIMG_MANIFEST_PATH" %in% names(j$env)),"\n")

sim_req <- c("n_clients","n_byzantine","clients_per_round","n_rounds","param_dim","trim_k")
cat("simulation_meta required?", all(sim_req %in% names(j$simulation_meta)),"\n")

fm_req <- c("trimmed_mean_accuracy","fedavg_accuracy","delta")
cat("final_metrics required?", all(fm_req %in% names(j$final_metrics)),"\n")

prov_req <- c("manifest_md5","selection_histogram")
cat("provenance required?", all(prov_req %in% names(j$provenance)),"\n")

cat("length(selection_histogram)=",length(j$provenance$selection_histogram),"\n")
cat("sum(selection_histogram)=",sum(j$provenance$selection_histogram),"\n")
'
output
all top-level keys present? TRUE 
env keys present? TRUE 
env excludes OUTPUT_DIR? TRUE 
env excludes MANIFEST_PATH? TRUE 
simulation_meta required? TRUE 
final_metrics required? TRUE 
provenance required? TRUE 
length(selection_histogram)= 100 
sum(selection_histogram)= 160
Everything looks correct. Let me also verify the manifest md5 is accurate and confirm the log base in entropy:
bash
cd /app && Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/test_run/analysis_summary.json")
expected_md5 <- unname(as.character(tools::md5sum("/app/dataset_manifest.json")))
cat("md5 match:", j$provenance$manifest_md5 == expected_md5,"\n")

# Verify entropy uses natural log
pm <- read.csv("outputs/test_run/partition_matrix.csv")
ps <- read.csv("outputs/test_run/partition_summary.csv")
# Compute entropy manually for client 0 using ln
r0 <- as.numeric(pm[1, -1])
p0 <- r0 / sum(r0); p0 <- p0[p0>0]
e0 <- -sum(p0 * log(p0))
cat("client 0 entropy (nat log):", round(e0,6), "  CSV:", ps$label_entropy[1],"\n")

# top_label_share check
top_idx <- which.max(r0)
share <- r0[top_idx]/sum(r0)
cat("client 0 top label:", top_idx-1, "  CSV:", ps$top_label[1], " share:", round(share,6), " CSV:", ps$top_label_share[1],"\n")
'
output
md5 match: TRUE 
client 0 entropy (nat log): 1.792905   CSV: 1.792905 
client 0 top label: 7   CSV: 7  share: 0.323446  CSV: 0.323446
Entropy is natural log, top_label_share is correctly `max_count / sum_count_per_client`. Let me also check that the byzantine attack is exactly as specified, and that clean vs submitted updates show the expected pattern:
bash
cd /app && Rscript -e '
# Verify byzantine cluster
cl <- read.csv("outputs/test_run/client_updates_clean.csv", stringsAsFactors=FALSE)
# For byzantine client 7 in round 0, u_submitted ~ -u_clean + noise(noise_scale=2)
# Check sign mostly flipped
b7 <- cl[cl$round==0 & cl$client_id==7, ]
cat("Byz client 7 mean ratio u_submitted/u_clean (approx -1 plus noise):\n")
cat("  cor(u_clean, u_submitted):", cor(b7$u_clean, b7$u_submitted),"\n")
cat("  mean(u_submitted + u_clean):", mean(b7$u_submitted + b7$u_clean),"\n")

# And for non-byz (eg client 10)
h10 <- cl[cl$round==0 & cl$client_id==10, ]
cat("Honest client 10 clean==submitted?", all(h10$u_clean == h10$u_submitted),"\n")
'
output
Byz client 7 mean ratio u_submitted/u_clean (approx -1 plus noise):
  cor(u_clean, u_submitted): -0.1428291 
  mean(u_submitted + u_clean): -0.0657795 
Honest client 10 clean==submitted? TRUE
Byzantine attack: negative correlation (sign flip with noise), honest: u_clean == u_submitted. All good. Let me also test robustness to different env vars (hidden variant simulation):
bash
cd /app && rm -rf outputs/hidden_test && mkdir -p outputs/hidden_test && \
  TASK_VARIANT=hidden \
  FEDSIMG_SEED=123 \
  FEDSIMG_OUTPUT_DIR=outputs/hidden_test \
  FEDSIMG_ALPHA=0.25 \
  FEDSIMG_NOISE_SCALE=3.5 \
  FEDSIMG_SIGNAL_SCALE=0.20 \
  FEDSIMG_TRIM_K=3 \
  FEDSIMG_BIAS_STRENGTH=0.50 \
  FEDSIMG_CLIENT_BIAS_CSV= \
  Rscript analysis.R && \
  Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/hidden_test/analysis_summary.json")
cat("task_variant:", j$task_variant,"\n")
cat("seed:", j$seed,"\n")
cat("env:\n"); print(j$env)
cat("trim_k:", j$simulation_meta$trim_k,"\n")
cat("byz ids:", j$byzantine_client_ids,"\n")
m <- read.csv("outputs/hidden_test/metrics_by_round.csv")
cat("final tm:", m$accuracy[m$method=="trimmed_mean" & m$round==7], 
    " final fed:", m$accuracy[m$method=="fedavg" & m$round==7],"\n")
'
output
task_variant: hidden 
seed: 123 
env:
$FEDSIMG_ALPHA
[1] 0.25

$FEDSIMG_NOISE_SCALE
[1] 3.5

$FEDSIMG_SIGNAL_SCALE
[1] 0.2

$FEDSIMG_TRIM_K
[1] 3

$FEDSIMG_BIAS_STRENGTH
[1] 0.5

$FEDSIMG_CLIENT_BIAS_CSV
[1] ""

trim_k: 3 
byz ids: 0 1 2 3 4 5 6 7 8 9 
final tm: 0.965766  final fed: 0.952445
Env vars are all properly honored. Let me also verify the output files are all present and that running again does replace stale outputs:
bash
cd /app && ls -la outputs/hidden_test/ | head -15
echo "---checking client_updates schema (round 7)"
awk -F',' 'NR==1 || $1=="7"' outputs/hidden_test/client_updates.csv | head -3
echo "---counts per round---"
awk -F',' 'NR>1 {print $1}' outputs/hidden_test/client_updates.csv | sort | uniq -c
output
total 1962
drwxr-sr-x 2 h2tagent h2tagent    4096 May 28 23:11 .
drwxrws--- 6 h2tagent h2tagent    4096 May 28 23:10 ..
-rw-r--r-- 1 h2tagent h2tagent    1041 May 28 23:11 analysis_summary.json
-rw-r--r-- 1 h2tagent h2tagent   12615 May 28 23:10 byzantine_audit.csv
-rw-r--r-- 1 h2tagent h2tagent  731583 May 28 23:10 client_updates.csv
-rw-r--r-- 1 h2tagent h2tagent 1115885 May 28 23:10 client_updates_clean.csv
-rw-r--r-- 1 h2tagent h2tagent   82202 May 28 23:11 diagnostics.png
-rw-r--r-- 1 h2tagent h2tagent   46730 May 28 23:10 dirichlet_gammas.csv
-rw-r--r-- 1 h2tagent h2tagent    1737 May 28 23:10 metrics_by_round.csv
-rw-r--r-- 1 h2tagent h2tagent    3148 May 28 23:10 partition_matrix.csv
-rw-r--r-- 1 h2tagent h2tagent    2959 May 28 23:10 partition_summary.csv
---checking client_updates schema (round 7)
"round","client_id","coord","update_value"
7,2,0,4.500232
7,2,1,-3.949121
---counts per round---
   5120 0
   5120 1
   5120 2
   5120 3
   5120 4
   5120 5
   5120 6
   5120 7
20 clients × 256 coords = 5120 rows per round. Checking client_updates row counts: check that they correspond to the `selected_clients` column from metrics:
bash
cd /app && Rscript -e '
m <- read.csv("outputs/hidden_test/metrics_by_round.csv", stringsAsFactors=FALSE)
cu <- read.csv("outputs/hidden_test/client_updates.csv")
for (r in 0:7) {
  metric_row <- m[m$round==r & m$method=="fedavg", ]
  sel_ids <- as.integer(strsplit(trimws(metric_row$selected_clients), "\\s+")[[1]])
  cu_ids <- sort(unique(cu$client_id[cu$round==r]))
  stopifnot(identical(sort(sel_ids), cu_ids))
}
cat("client_updates client_ids match metrics selected_clients for all rounds\n")

# Check same sort: round,client_id,coord ascending
cat("sorted?", !is.unsorted(cu$round),
    "  within-round sorted?", TRUE,"\n")
# Stricter check
is_sorted <- all(order(cu$round, cu$client_id, cu$coord) == seq_len(nrow(cu)))
cat("all (round,client_id,coord) ascending?", is_sorted,"\n")

# Do same for byzantine_audit
a <- read.csv("outputs/hidden_test/byzantine_audit.csv")
cat("audit sort:", all(order(a$round, a$client_id) == seq_len(nrow(a))),"\n")
'
output
client_updates client_ids match metrics selected_clients for all rounds
sorted? TRUE   within-round sorted? TRUE 
all (round,client_id,coord) ascending? TRUE 
audit sort: TRUE
All sort orders are correct. Let me also sanity-check the diagnostics.png and the deterministic reproducibility with a fixed seed:
bash
cd /app && file outputs/test_run/diagnostics.png
# Re-run with same seed and compare metrics
rm -rf outputs/rep1 outputs/rep2
TASK_VARIANT=public FEDSIMG_SEED=42 FEDSIMG_OUTPUT_DIR=outputs/rep1 \
FEDSIMG_ALPHA=0.5 FEDSIMG_NOISE_SCALE=2.0 FEDSIMG_SIGNAL_SCALE=0.12 \
FEDSIMG_TRIM_K=2 FEDSIMG_BIAS_STRENGTH=0.35 \
FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv Rscript analysis.R
TASK_VARIANT=public FEDSIMG_SEED=42 FEDSIMG_OUTPUT_DIR=outputs/rep2 \
FEDSIMG_ALPHA=0.5 FEDSIMG_NOISE_SCALE=2.0 FEDSIMG_SIGNAL_SCALE=0.12 \
FEDSIMG_TRIM_K=2 FEDSIMG_BIAS_STRENGTH=0.35 \
FEDSIMG_CLIENT_BIAS_CSV=/app/example_client_bias.csv Rscript analysis.R
# diff all csvs
echo "--- CSV diff output ---"
for f in metrics_by_round.csv dirichlet_gammas.csv partition_matrix.csv partition_summary.csv \
         byzantine_audit.csv client_updates.csv client_updates_clean.csv analysis_summary.json; do
  if ! diff -q outputs/rep1/$f outputs/rep2/$f >/dev/null; then
    echo "DIFF in $f"
  fi
done
echo "done"
output
--- CSV diff output ---
done
[stderr]
bash: file: command not found
All CSVs and JSON are bit-identical across runs with the same seed. Fully deterministic.
bash
cd /app && ls outputs/rep1/diagnostics.png && head -c 8 outputs/rep1/diagnostics.png | od -c | head -1
output
outputs/rep1/diagnostics.png
0000000 211   P   N   G  \r  \n 032  \n
Valid PNG.
str_replace_based_edit_tool
command: view
path: /app/outputs/rep1/diagnostics.png
output
<system>Image resized from 1400x900 to 1372x882 to fit display limits</system>
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…[truncated]
All 4 panels present: accuracy by round, partition heatmap, selected update norms (byzantine vs honest), byzantine selected per round bars. The diagnostics clearly show the trimmed_mean robustness vs. fedavg (attacked). Let me clean up the test directories and do a final smoke test:
bash
cd /app && rm -rf outputs/test_run outputs/rep1 outputs/rep2 outputs/hidden_test
# Final clean run with no overrides (just seed + variant)
rm -rf outputs/final_check && mkdir -p outputs/final_check
TASK_VARIANT=public FEDSIMG_SEED=7 FEDSIMG_OUTPUT_DIR=outputs/final_check Rscript analysis.R && \
  ls outputs/final_check/ && \
  Rscript -e '
library(jsonlite)
j <- fromJSON("outputs/final_check/analysis_summary.json")
print(j$env)
print(j$final_metrics)
'
output
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
$FEDSIMG_ALPHA
[1] 0.5

$FEDSIMG_NOISE_SCALE
[1] 2

$FEDSIMG_SIGNAL_SCALE
[1] 0.12

$FEDSIMG_TRIM_K
[1] 2

$FEDSIMG_BIAS_STRENGTH
[1] 0.35

$FEDSIMG_CLIENT_BIAS_CSV
[1] ""

$trimmed_mean_accuracy
[1] 0.934627

$fedavg_accuracy
[1] 0.867644

$delta
[1] 0.066983
Works correctly with no overrides , defaults from the manifest are used. Let me also clean that up:
bash
cd /app && rm -rf outputs/final_check && ls outputs/
output
hidden
public
Back to clean state. Here's a summary of the fixes made to `/app/analysis.R`: ### Summary of fixes | # | Issue | Fix | |---|---|---| | 1 | `shannon_entropy` used `log10` | Now uses natural `log()` (manifest: `entropy_log_base: "e"`) | | 2 | Byzantine IDs were highest indices | Now `0:(n_byz-1)` (manifest: `fixed_lowest_client_ids`) | | 3 | Byz attack was `-0.8·u_clean + noise/2` | Now `-u_clean + rnorm(0, noise_scale)` | | 4 | Env vars `FEDSIMG_{ALPHA,NOISE_SCALE,TRIM_K,BIAS_STRENGTH}` ignored | All honored with manifest-default fallback | | 5 | Bias CSV was loaded but never used | Now used (`bias_row %*% class_prototypes`); falls back to empirical mix when CSV missing | | 6 | Trimmed mean was row-norm-based | Now coordinate-wise (manifest: `space: "coordinate"`) | | 7 | FedAvg weighted by global total | Now weighted by sum-over-selected (manifest spec) | | 8 | Audit `update_norm` used clean | Now post-attack, and 0 if not selected | | 9 | Audit `client_weight` used global | Now equals the FedAvg weight over selected; 0 if not selected | | 10 | `u_submitted` in clean CSV stored `u_clean` | Fixed to store actual submitted value | | 11 | `top_label_share` divided by `per_class` | Now `max_count / sum_per_client` | | 12 | `allocated_count` was post-repair | Now the pre-repair Hamilton allocation (partition_matrix remains post-repair) | | 13 | `selection_histogram` from internal tally | Now re-derived from `metrics_by_round.csv` selected_clients | | 14 | `delta` used `abs()` | Now `tm - fed` (signed) | | 15 | Summary missing `trim_k` in simulation_meta | Added | | 16 | Summary `env` missing required keys | All 6 required env keys present; `OUTPUT_DIR`/`MANIFEST_PATH` excluded | | 17 | JSON floats lost precision (`digits=NA`) | Now `digits=17` so doubles round-trip exactly | | 18 | `label_entropy` rounding key | Uses `outputs.partition_csv.rounding.label_entropy_decimals` explicitly |
Final diff , what the agent changed

No file changes, the agent left the workspace untouched.

Verifier output , the proof behind the reward
tests/test.sh
# SyncVals normalized verifier summary
# task: fedavg-federated-noniid-mnist
# attempt: 10  status: completed  raw_score: 1.0
# reward rule: binary: score 1.0 == 1.0
# => reward = 1.0  (PASS)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
analysis_summary.json
byzantine_audit.csv
client_updates.csv
client_updates_clean.csv
diagnostics.png
dirichlet_gammas.csv
metrics_by_round.csv
partition_matrix.csv
partition_summary.csv
$FEDSIMG_ALPHA
[1] 0.5

$FEDSIMG_NOISE_SCALE
[1] 2

$FEDSIMG_SIGNAL_SCALE
[1] 0.12

$FEDSIMG_TRIM_K
[1] 2

$FEDSIMG_BIAS_STRENGTH
[1] 0.35

$FEDSIMG_CLIENT_BIAS_CSV
[1] ""

$trimmed_mean_accuracy
[1] 0.934627

$fedavg_accuracy
[1] 0.867644

$delta
[1] 0.066983

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_88199da9333046a5. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_88199da9333046a5 · verifier authoritative; classifier explanatory.