tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la /workspace/app/ && ls -la /workspace/
total 424 drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 08:53 .. -rw-rw---- 1 h2tagent h2tagent 1725 Jun 1 08:20 dataset_manifest.json -rw-rw---- 1 h2tagent h2tagent 423783 Jun 1 08:20 lgd_workouts_source.csv total 19 drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 08:53 . drwxr-xr-x 28 root root 4096 Jun 1 09:33 .. -rw-rw---- 1 h2tagent h2tagent 2821 Jun 1 08:20 analysis.R drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 app drwxrws--- 2 h2tagent h2tagent 4096 Jun 1 08:53 output
cat /workspace/analysis.R
#!/usr/bin/env Rscript
# LGD analysis , DRAFT. Audit and finish.
suppressPackageStartupMessages({
library(readr); library(dplyr); library(tidyr); library(jsonlite); library(ggplot2)
})
LGD_PATH <- Sys.getenv("LGD_PATH", "/workspace/app/lgd_workouts_source.csv")
OUT <- Sys.getenv("LGD_OUTPUT_DIR", "/workspace/output")
dir.create(OUT, showWarnings = FALSE, recursive = TRUE)
df <- read_csv(LGD_PATH, show_col_types = FALSE)
# Naive LGD over all rows (this is the biased baseline)
df <- df %>% mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
naive_lgd <- mean(df$observed_lgd)
# "corrected" , but the draft uses ALL rows again, just trimming outliers
corrected_lgd <- mean(df$observed_lgd[df$observed_lgd > 0.0 & df$observed_lgd < 1.0])
# bootstrap on the wrong (full) dataset , and only 200 reps
set.seed(20260606)
boot <- replicate(200, mean(sample(df$observed_lgd, replace = TRUE)))
ci_lo <- quantile(boot, 0.025); ci_hi <- quantile(boot, 0.975)
bg <- df %>% group_by(grade) %>% summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
bp <- df %>% group_by(purpose) %>% summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- df %>% group_by(grade, purpose) %>%
summarise(n_total = n(), n_resolved = sum(is_resolved),
naive_lgd = mean(observed_lgd),
corrected_lgd = mean(observed_lgd), .groups = "drop")
write_csv(panel, file.path(OUT, "lgd_panel.csv"))
bias_diff <- naive_lgd - corrected_lgd
direction <- if (abs(bias_diff) < 0.05) "close" else if (bias_diff > 0) "materially_overestimates" else "materially_underestimates"
metrics <- list(
data_summary = list(n_total = nrow(df), n_resolved = sum(df$is_resolved),
n_censored = nrow(df) - sum(df$is_resolved),
share_resolved = mean(df$is_resolved)),
naive_lgd = list(value = naive_lgd),
corrected_lgd = list(value = corrected_lgd),
bootstrap_ci = list(ci_low = unname(ci_lo), ci_high = unname(ci_hi)),
lgd_by_grade = setNames(as.list(bg$corrected_lgd), bg$grade),
lgd_by_purpose = setNames(as.list(bp$corrected_lgd), bp$purpose),
bias_assessment = list(naive_minus_corrected = bias_diff, direction = direction)
)
write_json(metrics, file.path(OUT, "metrics.json"), auto_unbox = TRUE,
digits = 6, pretty = TRUE)
cat(paste0("Naive LGD: ", round(naive_lgd, 3),
"\nCorrected: ", round(corrected_lgd, 3),
"\nDirection: ", direction, "\n"),
file = file.path(OUT, "insights.md"))
write_json(list(figures = list()), file.path(OUT, "plot_contract.json"),
auto_unbox = TRUE, pretty = TRUE)
png(file.path(OUT, "fig1_lgd_by_grade.png"), width = 600, height = 400); plot.new(); dev.off()
png(file.path(OUT, "fig2_naive_vs_corrected.png"), width = 600, height = 400); plot.new(); dev.off()cat /workspace/app/dataset_manifest.json
{
"dataset_name": "Defaulted-loan workout panel for Loss Given Default modelling",
"source": "Calibrated to Lending Club historical loan-level disclosures of defaulted accounts and their post-default workout ledger (https://www.lendingclub.com/info/statistics.action , historical bulk files).",
"license": "Synthetic but matches the Lending Club workout schema for grading.",
"columns": {
"loan_id": "Anonymous loan identifier.",
"grade": "Lending Club grade A-G assigned at origination.",
"purpose": "Borrower-stated loan purpose.",
"home_ownership": "RENT / OWN / MORTGAGE.",
"principal_at_default": "Outstanding principal at the moment of default (USD).",
"default_month": "Months from origination to default.",
"workout_months_observed": "Months of post-default observation in the panel.",
"time_to_resolution": "Months from default to terminal resolution (recovery curve flat-lines).",
"is_resolved": "1 if workout_months_observed >= time_to_resolution; else 0 (right-censored workout).",
"recovered_amount": "Cumulative dollars recovered as of the as-of date (USD).",
"annual_inc": "Borrower annual income at origination."
},
"definitions": {
"lgd": "LGD = 1 - recovered_amount / principal_at_default , only valid when is_resolved == 1.",
"censoring": "Rows with is_resolved == 0 are right-censored: their LGD is incompletely observed. Censored rows have partial recovery, so recovered_amount understates the true terminal recovery , a naive mean of recovered/principal is biased downward, and therefore LGD = 1 - recovered/principal is biased upward (naive overestimates true LGD). Restrict to resolved workouts or model the cure mechanism."
}
}head -3 /workspace/app/lgd_workouts_source.csv && echo "---" && wc -l /workspace/app/lgd_workouts_source.csv
loan_id,grade,purpose,home_ownership,principal_at_default,default_month,workout_months_observed,time_to_resolution,is_resolved,recovered_amount,annual_inc LC0000000,C,small_business,MORTGAGE,9248.9,12,16,8,1,2351.97,37618.14 LC0000001,A,debt_consolidation,RENT,14442.6,10,25,15,1,7783.99,135186.3 --- 6001 /workspace/app/lgd_workouts_source.csv
Rscript -e '
df <- read.csv("/workspace/app/lgd_workouts_source.csv")
cat("rows:", nrow(df), "\n")
cat("resolved:", sum(df$is_resolved), "\n")
cat("grades:", paste(sort(unique(df$grade)), collapse=","), "\n")
cat("purposes:", paste(sort(unique(df$purpose)), collapse=","), "\n")
cat("naive LGD (all):", mean(1 - df$recovered_amount / df$principal_at_default), "\n")
r <- df[df$is_resolved == 1, ]
cat("corrected LGD (resolved):", mean(1 - r$recovered_amount / r$principal_at_default), "\n")
# check all grade x purpose have at least 1 resolved
pnl <- aggregate(is_resolved ~ grade + purpose, data=df, sum)
cat("groups with n_resolved==0:", sum(pnl$is_resolved == 0), "\n")
'
rows: 6000 resolved: 3185 grades: A,B,C,D,E,F,G purposes: credit_card,debt_consolidation,home_improvement,major_purchase,other,small_business naive LGD (all): 0.677057 corrected LGD (resolved): 0.5651259 groups with n_resolved==0: 0
Rscript -e '
df <- read.csv("/workspace/app/lgd_workouts_source.csv")
r <- df[df$is_resolved == 1, ]
cat("grades in resolved subset:", paste(sort(unique(r$grade)), collapse=","), "\n")
cat("purposes in resolved subset:", paste(sort(unique(r$purpose)), collapse=","), "\n")
pnl <- aggregate(is_resolved ~ grade + purpose, data=df, function(x) c(sum(x), length(x)))
print(head(pnl))
'
grades in resolved subset: A,B,C,D,E,F,G purposes in resolved subset: credit_card,debt_consolidation,home_improvement,major_purchase,other,small_business grade purpose is_resolved.1 is_resolved.2 1 A credit_card 136 219 2 B credit_card 165 321 3 C credit_card 137 278 4 D credit_card 115 215 5 E credit_card 46 90 6 F credit_card 29 56
command: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# ------------------------------------------------------------------
# LGD analysis , Lending Club defaulted-loan workout panel.
#
# The manifest documents that a simple mean of 1 - recovered/principal
# over ALL rows is biased: censored workouts (is_resolved == 0) have
# partial recoveries, so their recovered_amount understates the true
# terminal recovery. Hence the naive mean is biased UPWARD relative
# to the true terminal LGD. The defensible read is to restrict the
# sample to resolved workouts (is_resolved == 1).
# ------------------------------------------------------------------
suppressPackageStartupMessages({
library(readr)
library(dplyr)
library(tidyr)
library(jsonlite)
library(ggplot2)
})
LGD_PATH <- Sys.getenv("LGD_PATH", "/workspace/app/lgd_workouts_source.csv")
OUT <- Sys.getenv("LGD_OUTPUT_DIR", "/workspace/output")
dir.create(OUT, showWarnings = FALSE, recursive = TRUE)
# --------------------------- Load ---------------------------------
df <- read_csv(LGD_PATH, show_col_types = FALSE)
# Per-row observed ratio (valid only when is_resolved == 1; kept on
# all rows so the naive baseline can be computed for documentation).
df <- df %>%
mutate(observed_lgd = 1 - recovered_amount / principal_at_default)
resolved <- df %>% filter(is_resolved == 1)
# -------------------------- Headline ------------------------------
# Naive: mean over ALL rows (biased , includes right-censored loans).
naive_lgd <- mean(df$observed_lgd)
# Corrected: mean over resolved loans only.
corrected_lgd <- mean(resolved$observed_lgd)
# -------------------------- Bootstrap CI --------------------------
# 1000 paired resamples over resolved loans; 2.5 / 97.5 percentile.
B <- 1000
lgd_resolved_vec <- resolved$observed_lgd
n_res <- length(lgd_resolved_vec)
set.seed(20260606)
boot_means <- numeric(B)
for (b in seq_len(B)) {
idx <- sample.int(n_res, n_res, replace = TRUE)
boot_means[b] <- mean(lgd_resolved_vec[idx])
}
ci_low <- unname(quantile(boot_means, 0.025))
ci_high <- unname(quantile(boot_means, 0.975))
# --------------------- Per-grade / per-purpose --------------------
# Corrected population only.
bg <- resolved %>%
group_by(grade) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(grade)
bp <- resolved %>%
group_by(purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
arrange(purpose)
# ------------------------- Panel table ----------------------------
# Totals (n_total, naive_lgd) come from the full population;
# n_resolved and corrected_lgd from the resolved subset so that
# corrected_lgd is never NaN when n_resolved > 0.
totals <- df %>%
group_by(grade, purpose) %>%
summarise(
n_total = n(),
n_resolved = sum(is_resolved == 1),
naive_lgd = mean(observed_lgd),
.groups = "drop"
)
resolved_group <- resolved %>%
group_by(grade, purpose) %>%
summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")
panel <- totals %>%
left_join(resolved_group, by = c("grade", "purpose")) %>%
arrange(grade, purpose) %>%
select(grade, purpose, n_total, n_resolved, naive_lgd, corrected_lgd)
write_csv(panel, file.path(OUT, "lgd_panel.csv"))
# -------------------------- Bias call -----------------------------
bias_diff <- naive_lgd - corrected_lgd
direction <- if (naive_lgd < corrected_lgd - 0.05) {
"materially_underestimates"
} else if (naive_lgd > corrected_lgd + 0.05) {
"materially_overestimates"
} else {
"close"
}
# --------------------------- metrics.json -------------------------
metrics <- list(
data_summary = list(
n_total = nrow(df),
n_resolved = sum(df$is_resolved == 1),
n_censored = sum(df$is_resolved == 0),
share_resolved = mean(df$is_resolved == 1)
),
naive_lgd = list(value = naive_lgd),
corrected_lgd = list(value = corrected_lgd),
bootstrap_ci = list(ci_low = ci_lo
…[truncated]Existing file was successfully overwritten at /workspace/analysis.R
grep -nE 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|system\(|system2\(|shell\(|\bpipe\(|saveRDS|writeBin|as\.raw|sink\(|readRDS|readBin|\bload\(' /workspace/analysis.R || echo "no banned calls found"no banned calls found
rm -rf /workspace/output && mkdir -p /workspace/output && cd /workspace && LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript analysis.R
n_total=6000 n_resolved=3185 naive=0.6771 corrected=0.5651 ci=[0.5577,0.5727] dir=materially_overestimates
ls -la /workspace/output/ && echo "---" && cat /workspace/output/metrics.json
total 57
drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 1 09:34 .
drwxrws--- 4 h2tagent h2tagent 4096 Jun 1 09:34 ..
-rw-r--r-- 1 h2tagent h2tagent 23620 Jun 1 09:34 fig1_lgd_by_grade.png
-rw-r--r-- 1 h2tagent h2tagent 18985 Jun 1 09:34 fig2_naive_vs_corrected.png
-rw-r--r-- 1 h2tagent h2tagent 722 Jun 1 09:34 insights.md
-rw-r--r-- 1 h2tagent h2tagent 2592 Jun 1 09:34 lgd_panel.csv
-rw-r--r-- 1 h2tagent h2tagent 807 Jun 1 09:34 metrics.json
-rw-r--r-- 1 h2tagent h2tagent 927 Jun 1 09:34 plot_contract.json
---
{
"data_summary": {
"n_total": 6000,
"n_resolved": 3185,
"n_censored": 2815,
"share_resolved": 0.53083333
},
"naive_lgd": {
"value": 0.677057
},
"corrected_lgd": {
"value": 0.56512588
},
"bootstrap_ci": {
"ci_low": 0.55767503,
"ci_high": 0.57265928
},
"lgd_by_grade": {
"A": 0.43529755,
"B": 0.49919225,
"C": 0.57150847,
"D": 0.65262141,
"E": 0.71929622,
"F": 0.79746682,
"G": 0.84954799
},
"lgd_by_purpose": {
"credit_card": 0.5911141,
"debt_consolidation": 0.54887667,
"home_improvement": 0.54207074,
"major_purchase": 0.61012939,
"other": 0.54548081,
"small_business": 0.6255756
},
"bias_assessment": {
"naive_minus_corrected": 0.11193112,
"direction": "materially_overestimates"
}
}cat /workspace/output/plot_contract.json
echo "---"
cat /workspace/output/insights.md
echo "---"
head -5 /workspace/output/lgd_panel.csv
echo "---"
wc -l /workspace/output/lgd_panel.csv
echo "---"
# any NaN in panel?
grep -i nan /workspace/output/lgd_panel.csv || echo "no NaN in panel"
# image size probe
Rscript -e 'for (f in c("/workspace/output/fig1_lgd_by_grade.png","/workspace/output/fig2_naive_vs_corrected.png")) { img <- png::readPNG(f); cat(f, dim(img)[2], "x", dim(img)[1], "\n") }'{
"figures": [
{
"name": "fig1_lgd_by_grade",
"series": [
{
"grade": "A",
"corrected_lgd": 0.43529755
},
{
"grade": "B",
"corrected_lgd": 0.49919225
},
{
"grade": "C",
"corrected_lgd": 0.57150847
},
{
"grade": "D",
"corrected_lgd": 0.65262141
},
{
"grade": "E",
"corrected_lgd": 0.71929622
},
{
"grade": "F",
"corrected_lgd": 0.79746682
},
{
"grade": "G",
"corrected_lgd": 0.84954799
}
]
},
{
"name": "fig2_naive_vs_corrected",
"series": [
{
"measure": "naive_lgd",
"value": 0.677057
},
{
"measure": "corrected_lgd",
"value": 0.56512588
}
]
}
]
}
---
# LGD read , Lending Club defaulted-loan workout panel
The naive mean of `1 - recovered_amount / principal_at_default` over **all** rows is 0.677. Restricted to resolved workouts (`is_resolved == 1`), the corrected mean LGD is 0.565 with a paired-bootstrap 95% CI of [0.558, 0.573] (B = 1000 resamples, seed 20260606).
The naive baseline **materially_overestimates** the corrected LGD by 0.112 (naive minus corrected). Right-censored workouts have only partial recoveries to date, so 1 - recovered/principal on censored rows is biased upward, pulling the naive pooled mean above the defensible resolved-only estimate. All defensible per-grade and per-purpose readings in this report use the resolved-only population.
---
grade,purpose,n_total,n_resolved,naive_lgd,corrected_lgd
A,credit_card,219,136,0.5607065958509241,0.45012049865070586
A,debt_consolidation,564,297,0.5702761415442725,0.4202599413316665
A,home_improvement,114,64,0.5292722852167128,0.4073998487935503
A,major_purchase,67,35,0.6368190733531681,0.5021729573367807
---
43 /workspace/output/lgd_panel.csv
---
no NaN in panel
[stderr]
Error in loadNamespace(x) : there is no package called ‘png’
Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
Execution haltedfile /workspace/output/*.png
/workspace/output/fig1_lgd_by_grade.png: PNG image data, 720 x 480, 8-bit/color RGBA, non-interlaced /workspace/output/fig2_naive_vs_corrected.png: PNG image data, 720 x 480, 8-bit/color RGBA, non-interlaced
command: view path: /workspace/output/fig1_lgd_by_grade.png
<output_image> …[truncated]
command: view path: /workspace/output/fig2_naive_vs_corrected.png
<output_image> …[truncated]
Rscript -e '
m <- jsonlite::fromJSON("/workspace/output/metrics.json")
stopifnot(m$data_summary$n_total == 6000)
stopifnot(m$data_summary$n_resolved + m$data_summary$n_censored == 6000)
stopifnot(abs(m$data_summary$share_resolved - m$data_summary$n_resolved/6000) < 1e-12)
stopifnot(length(m$lgd_by_grade) == 7)
stopifnot(all(c("A","B","C","D","E","F","G") %in% names(m$lgd_by_grade)))
stopifnot(length(m$lgd_by_purpose) == 6)
stopifnot(m$bootstrap_ci$ci_low < m$corrected_lgd$value,
m$bootstrap_ci$ci_high > m$corrected_lgd$value)
stopifnot(m$bias_assessment$direction == "materially_overestimates")
stopifnot(abs(m$bias_assessment$naive_minus_corrected -
(m$naive_lgd$value - m$corrected_lgd$value)) < 1e-9)
p <- read.csv("/workspace/output/lgd_panel.csv")
stopifnot(all(c("grade","purpose","n_total","n_resolved","naive_lgd","corrected_lgd") %in% names(p)))
stopifnot(all(p$n_resolved > 0))
stopifnot(!any(is.na(p$corrected_lgd)))
stopifnot(nrow(p) == 7*6)
pc <- jsonlite::fromJSON("/workspace/output/plot_contract.json", simplifyVector = FALSE)
stopifnot(is.list(pc$figures), length(pc$figures) == 2)
stopifnot(pc$figures[[1]]$name == "fig1_lgd_by_grade",
length(pc$figures[[1]]$series) > 0)
stopifnot(pc$figures[[2]]$name == "fig2_naive_vs_corrected",
length(pc$figures[[2]]$series) > 0)
cat("ALL CHECKS PASS\n")
'
[stderr] Error: abs(m$data_summary$share_resolved - m$data_summary$n_resolved/6000) < .... is not TRUE Execution halted
Rscript -e 'm <- jsonlite::fromJSON("/workspace/output/metrics.json"); cat(m$data_summary$share_resolved, "vs", m$data_summary$n_resolved/6000, "\n"); cat("diff:", m$data_summary$share_resolved - m$data_summary$n_resolved/6000, "\n")'0.5308333 vs 0.5308333 diff: -3.333333e-09
Rscript -e '
m <- jsonlite::fromJSON("/workspace/output/metrics.json")
stopifnot(m$data_summary$n_total == 6000)
stopifnot(m$data_summary$n_resolved + m$data_summary$n_censored == 6000)
stopifnot(abs(m$data_summary$share_resolved - m$data_summary$n_resolved/6000) < 1e-6)
stopifnot(length(m$lgd_by_grade) == 7)
stopifnot(all(c("A","B","C","D","E","F","G") %in% names(m$lgd_by_grade)))
stopifnot(length(m$lgd_by_purpose) == 6)
stopifnot(m$bootstrap_ci$ci_low < m$corrected_lgd$value,
m$bootstrap_ci$ci_high > m$corrected_lgd$value)
stopifnot(m$bias_assessment$direction == "materially_overestimates")
p <- read.csv("/workspace/output/lgd_panel.csv")
stopifnot(all(c("grade","purpose","n_total","n_resolved","naive_lgd","corrected_lgd") %in% names(p)))
stopifnot(all(p$n_resolved > 0))
stopifnot(!any(is.na(p$corrected_lgd)))
stopifnot(nrow(p) == 7*6)
pc <- jsonlite::fromJSON("/workspace/output/plot_contract.json", simplifyVector = FALSE)
stopifnot(is.list(pc$figures), length(pc$figures) == 2)
stopifnot(pc$figures[[1]]$name == "fig1_lgd_by_grade", length(pc$figures[[1]]$series) > 0)
stopifnot(pc$figures[[2]]$name == "fig2_naive_vs_corrected", length(pc$figures[[2]]$series) > 0)
cat("ALL CHECKS PASS\n")
'
ALL CHECKS PASS
rm -rf /workspace/output && mkdir -p /workspace/output cd /workspace LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript analysis.R md5sum /workspace/output/metrics.json /workspace/output/lgd_panel.csv /workspace/output/plot_contract.json echo "--- 2nd run ---" LGD_PATH=/workspace/app/lgd_workouts_source.csv LGD_OUTPUT_DIR=/workspace/output Rscript analysis.R md5sum /workspace/output/metrics.json /workspace/output/lgd_panel.csv /workspace/output/plot_contract.json
n_total=6000 n_resolved=3185 naive=0.6771 corrected=0.5651 ci=[0.5577,0.5727] dir=materially_overestimates c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv af9f8f17c6bb3f986f7c0bde78dfcfbe /workspace/output/plot_contract.json --- 2nd run --- n_total=6000 n_resolved=3185 naive=0.6771 corrected=0.5651 ci=[0.5577,0.5727] dir=materially_overestimates c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv af9f8f17c6bb3f986f7c0bde78dfcfbe /workspace/output/plot_contract.json
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: lending-club-lgd-bias-correction-r # attempt: 6 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). ---------------------------------------------------------------------- n_total=6000 n_resolved=3185 naive=0.6771 corrected=0.5651 ci=[0.5577,0.5727] dir=materially_overestimates c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv af9f8f17c6bb3f986f7c0bde78dfcfbe /workspace/output/plot_contract.json --- 2nd run --- n_total=6000 n_resolved=3185 naive=0.6771 corrected=0.5651 ci=[0.5577,0.5727] dir=materially_overestimates c2a023a9edca7a3a276de4699d328a3e /workspace/output/metrics.json 633a186d045f6490875af4c0ef0e46ad /workspace/output/lgd_panel.csv af9f8f17c6bb3f986f7c0bde78dfcfbe /workspace/output/plot_contract.json
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_70d657cae4d742bd. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_70d657cae4d742bd · verifier authoritative; classifier explanatory.