SyncVals · Trajectory

lending-club-lgd-bias-correction-r

claude-code claude-opus-4-8 ✗ failed BAD_FAILURE ↑ 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

BAD_FAILUREThe task is at fault, underspecified/contradictory instruction, brittle/flaky tests, or tests demanding undiscoverable behavior.

SubtypeTest Infrastructure Problem - Agent Code Overwritten

EvidenceThe test harness in test.sh (line 85) runs `bash /solution/solve.sh` BEFORE executing the agent's analysis.R. The solve.sh script (line 3) unconditionally copies /solution/analysis.R over /workspace/analysis.R, completely overwriting any code the agent created. The agent successfully wrote a correct analysis.R in /workspace (demonstrated in trajectory steps 19-45), but it is unconditionally deleted before the tests run. The agent's work never actually executes in the grader, making it impossible for the agent to pass regardless of solution quality. Test.sh lines 85-93 show the sequence: solve.sh runs first (overwriting agent code), then Rscript runs on the now-overwritten file, then pytest validates the reference solution's output, not the agent's.

Root causeThe test.sh harness unconditionally installs the reference solution from /solution/solve.sh before running tests, which overwrites /workspace/analysis.R. This design prevents the agent from having their code evaluated - the reference solution always runs instead. This is an infrastructure design flaw, not a problem with the agent or the task specification.

RecommendationRemove the `if [ -f /solution/solve.sh ]; then bash /solution/solve.sh; fi` line from test.sh (or make it conditional - only run for oracle/baseline validation, not for agent trials). The test should evaluate the agent's /workspace/analysis.R directly, not replace it with the reference solution. Alternatively, use a separate solve.sh for oracle validation that doesn't run during agent grading. The solve.sh approach is valid for oracle/nop baselines but is incompatible with agent code evaluation."

Trajectory

Tool-by-tool agent trajectory

51 tool calls · 3 tool types · 51 steps

Hi , I want a defensible portfolio Loss Given Default (LGD) read on this Lending Club defaulted-loans workout panel. Source CSV at `/workspace/app/lgd_workouts_source.csv`; schema at `/workspace/app/dataset_manifest.json`. A previous draft at `/workspace/analysis.R` runs but its specification is wrong. Audit and finish. ## Entry point `Rscript /workspace/analysis.R` reading `LGD_PATH` and writing to `LGD_OUTPUT_DIR`. Grader runs the script twice (public + hidden). ## Output contract Into `LGD_OUTPUT_DIR`: - **`metrics.json`** , Sections: - `data_summary`: n_total, n_resolved, n_censored, share_resolved. - `naive_lgd`: `{"value": <float>}` (or bare scalar) , mean of `1 - recovered_amount / principal_at_default` over **all** rows (the biased baseline). - `corrected_lgd`: `{"value": <float>}` (or bare scalar) , same mean but **restricted to is_resolved == 1**. - `bootstrap_ci`: `{"ci_low": <float>, "ci_high": <float>}` , 95% CI for `corrected_lgd` from a paired bootstrap (B = 1000) over resolved loans only. - `lgd_by_grade`: corrected LGD per grade A-G. - `lgd_by_purpose`: corrected LGD per purpose. - `bias_assessment`: keys `naive_minus_corrected` and `direction` ∈ `"materially_underestimates"`, `"close"`, `"materially_overestimates"`. - **`lgd_panel.csv`** , columns `grade, purpose, n_total, n_resolved, naive_lgd, corrected_lgd` (order not enforced). - **`insights.md`** , short paragraph naming the direction and magnitude. - **`plot_contract.json`** , `{"figures": [{"name": "fig1_lgd_by_grade", "series": [...]}, {"name": "fig2_naive_vs_corrected", "series": [...]}]}`. `figures` must be a JSON **array** of objects; each object must have a `"name"` string key matching the PNG filename (without `.png`) and a non-empty `"series"` list. - Two PNGs ≥ 200 × 150: - `fig1_lgd_by_grade.png` , corrected LGD by grade. - `fig2_naive_vs_corrected.png` , paired bars. ## Specification - **Naive LGD**: `mean(1 - recovered_amount / principal_at_default)` over the full dataset. Document this as biased. - **Corrected LGD**: same mean **only over `is_resolved == 1`** rows. - **Bootstrap CI**: 1000 resamples (with replacement) of the resolved rows; record the mean LGD per resample; report 2.5% / 97.5% percentile bounds. Use `set.seed(20260606)` immediately before the bootstrap loop. - **Per grade / per purpose**: use the corrected (resolved-only) population. - **`corrected_lgd` in the panel**: for every `grade × purpose` group that has at least one resolved loan (`n_resolved > 0`), `corrected_lgd` must be the resolved-only mean , **not NaN**. - **Direction** (relative to corrected): - `"materially_underestimates"` if `naive_lgd < corrected_lgd - 0.05`; - `"materially_overestimates"` if `naive_lgd > corrected_lgd + 0.05`; - `"close"` otherwise. ## Constraints `analysis.R` must not use: - **Filesystem manipulation**: `file.copy`, `file.rename`, `file.link`, `file.symlink`, `file.append` - **Shell / subprocess dispatch**: `system()`, `system2()`, `shell()`, `pipe()` - **Binary I/O**: `saveRDS()`, `writeBin()`, `as.raw()`, `sink()`, `readRDS()`, `readBin()`, `load()` Preferred write path: `jsonlite::write_json` for `metrics.json`; `readr::write_csv` or `write.csv` for `lgd_panel.csv`; `writeLines`, `cat`, or any text writer for `insights.md`; `ggsave` / `png` for plots.

Let me start by exploring the workspace to understand the structure and the existing code.

bash

ls -la /workspace/app/ && ls -la /workspace/

output

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

bash

cat /workspace/app/dataset_manifest.json

output

{
  "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."
  }
}

bash

cat /workspace/analysis.R

output

#!/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()

bash

head -3 /workspace/app/lgd_workouts_source.csv

output

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

bash

wc -l /workspace/app/lgd_workouts_source.csv

output

6001 /workspace/app/lgd_workouts_source.csv

bash

python3 -c "
import csv
with open('/workspace/app/lgd_workouts_source.csv') as f:
  r = list(csv.DictReader(f))
print('N rows:', len(r))
print('grades:', sorted(set(x['grade'] for x in r)))
print('purposes:', sorted(set(x['purpose'] for x in r)))
resolved = [x for x in r if x['is_resolved']=='1']
print('n resolved:', len(resolved))
print('n unresolved:', len(r)-len(resolved))
import statistics
naive = [1 - float(x['recovered_amount'])/float(x['principal_at_default']) for x in r]
corrected = [1 - float(x['recovered_amount'])/float(x['principal_at_default']) for x in resolved]
print('naive mean:', statistics.mean(naive))
print('corrected mean:', statistics.mean(corrected))
"

output

N rows: 6000
grades: ['A', 'B', 'C', 'D', 'E', 'F', 'G']
purposes: ['credit_card', 'debt_consolidation', 'home_improvement', 'major_purchase', 'other', 'small_business']
n resolved: 3185
n unresolved: 2815
naive mean: 0.6770569995859103
corrected mean: 0.5651258837747273

Great, so: - Naive LGD is ~0.677 (biased upward because censored rows have partial recovery) - Corrected LGD (resolved only) is ~0.565 - Difference is ~0.112, so naive materially overestimates LGD Let me now verify a group like grade × purpose where all rows are censored - we need to only use resolved rows for corrected_lgd, but only emit corrected_lgd NaN if n_resolved == 0. Wait, the spec says: "for every `grade × purpose` group that has at least one resolved loan (`n_resolved > 0`), `corrected_lgd` must be the resolved-only mean , **not NaN**." So for groups with n_resolved == 0, it can be NaN or similar. Let me check if there's any such group.

bash

python3 -c "
import csv
from collections import defaultdict
with open('/workspace/app/lgd_workouts_source.csv') as f:
  r = list(csv.DictReader(f))
groups = defaultdict(list)
for x in r:
  groups[(x['grade'], x['purpose'])].append(x)
for (g,p), rows in sorted(groups.items()):
  n_total = len(rows)
  n_resolved = sum(1 for x in rows if x['is_resolved']=='1')
  print(f'grade={g} purpose={p} n_total={n_total} n_resolved={n_resolved}')
"

output

grade=A purpose=credit_card n_total=219 n_resolved=136
grade=A purpose=debt_consolidation n_total=564 n_resolved=297
grade=A purpose=home_improvement n_total=114 n_resolved=64
grade=A purpose=major_purchase n_total=67 n_resolved=35
grade=A purpose=other n_total=90 n_resolved=52
grade=A purpose=small_business n_total=86 n_resolved=44
grade=B purpose=credit_card n_total=321 n_resolved=165
grade=B purpose=debt_consolidation n_total=796 n_resolved=431
grade=B purpose=home_improvement n_total=144 n_resolved=72
grade=B purpose=major_purchase n_total=92 n_resolved=47
grade=B purpose=other n_total=147 n_resolved=76
grade=B purpose=small_business n_total=107 n_resolved=62
grade=C purpose=credit_card n_total=278 n_resolved=137
grade=C purpose=debt_consolidation n_total=689 n_resolved=376
grade=C purpose=home_improvement n_total=140 n_resolved=81
grade=C purpose=major_purchase n_total=86 n_resolved=38
grade=C purpose=other n_total=108 n_resolved=55
grade=C purpose=small_business n_total=88 n_resolved=46
grade=D purpose=credit_card n_total=215 n_resolved=115
grade=D purpose=debt_consolidation n_total=448 n_resolved=228
grade=D purpose=home_improvement n_total=120 n_resolved=67
grade=D purpose=major_purchase n_total=42 n_resolved=19
grade=D purpose=other n_total=87 n_resolved=51
grade=D purpose=small_business n_total=80 n_resolved=41
grade=E purpose=credit_card n_total=90 n_resolved=46
grade=E purpose=debt_consolidation n_total=256 n_resolved=132
grade=E purpose=home_improvement n_total=43 n_resolved=25
grade=E purpose=major_purchase n_total=33 n_resolved=18
grade=E purpose=other n_total=38 n_resolved=22
grade=E purpose=small_business n_total=34 n_resolved=21
grade=F purpose=credit_card n_total=56 n_resolved=29
grade=F purpose=debt_consolidation n_total=117 n_resolved=54
grade=F purpose=home_improvement n_total=22 n_resolved=7
grade=F purpose=major_purchase n_total=12 n_resolved=10
grade=F purpose=other n_total=25 n_resolved=10
grade=F purpose=small_business n_total=10 n_resolved=7
grade=G purpose=credit_card n_total=31 n_resolved=12
grade=G purpose=debt_consolidation n_total=56 n_resolved=32
grade=G purpose=home_improvement n_total=18 n_resolved=8
grade=G purpose=major_purchase n_total=9 n_resolved=6
grade=G purpose=other n_total=11 n_resolved=7
grade=G purpose=small_business n_total=11 n_resolved=4

Good, every group has n_resolved > 0. Now let me write the correct analysis.R. I need to make sure: 1. naive_lgd uses all rows 2. corrected_lgd uses only is_resolved == 1 rows 3. Bootstrap with B=1000, set.seed(20260606) immediately before loop 4. panel with grade × purpose (and corrected_lgd from resolved-only subset) 5. metrics.json structure 6. Two plots: fig1_lgd_by_grade.png (corrected LGD by grade), fig2_naive_vs_corrected.png (paired bars) 7. plot_contract.json with "figures" array of objects with "name" and "series" keys 8. insights.md short paragraph naming direction and magnitude 9. No forbidden functions Note about the paired bootstrap - "paired bootstrap (B = 1000) over resolved loans only" - simpler bootstrap where we sample rows (each row is a pair of recovered_amount and principal_at_default). We then compute the mean LGD per resample. Let me think: "paired" means we sample the pair (recovered, principal), not separately. That's what sampling rows (with replacement) does naturally when you compute 1 - rec/prin. I think the "paired" nature refers to keeping the pair together - so sampling resolved row indices and computing the mean. Let me write a clean analysis.R. I need to make sure I don't use any forbidden functions. Forbidden: - file.copy, file.rename, file.link, file.symlink, file.append - system(), system2(), shell(), pipe() - saveRDS(), writeBin(), as.raw(), sink(), readRDS(), readBin(), load() I should use readr::read_csv or read.csv, write_csv or write.csv, write_json, writeLines/cat, ggsave/png. Let me verify the metrics structure: - data_summary: n_total, n_resolved, n_censored, share_resolved - naive_lgd: {"value": X} or bare scalar - corrected_lgd: {"value": X} or bare scalar - bootstrap_ci: {"ci_low": X, "ci_high": X} - lgd_by_grade: corrected LGD per grade A-G - lgd_by_purpose: corrected LGD per purpose - bias_assessment: naive_minus_corrected, direction Let me write it:

Now I understand the data. Let me write the corrected analysis.R:

str_replace_based_edit_tool

command: create
path: /workspace/analysis.R
file_text: #!/usr/bin/env Rscript
# LGD analysis , defensible read on Lending Club defaulted-loans workout panel.
# ----------------------------------------------------------------------------
# Specification (see /workspace/app/dataset_manifest.json):
#   Raw LGD = 1 - recovered_amount / principal_at_default,
#   but this ratio is only *fully* observed for rows with is_resolved == 1.
#   Censored rows (is_resolved == 0) have partial recovery, so their
#   recovered_amount understates terminal recovery and their "observed" LGD
#   overstates true LGD. A naive mean over all rows is therefore biased
#   upward; the defensible point estimate restricts to resolved workouts.
# ----------------------------------------------------------------------------

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)

# Row-level observed LGD (defined for every row, but only *valid* for resolved)
df <- df %>%
  mutate(observed_lgd = 1 - recovered_amount / principal_at_default)

resolved <- df %>% filter(is_resolved == 1)

# --- Point estimates ---------------------------------------------------------
# Naive: biased baseline across the full panel (includes censored rows).
naive_lgd     <- mean(df$observed_lgd)
# Corrected: restrict to fully-resolved workouts where LGD is observed.
corrected_lgd <- mean(resolved$observed_lgd)

# --- Paired bootstrap over resolved loans only (B = 1000) --------------------
# "Paired" = resample rows (pairs of recovered / principal), not the two
# columns independently.
n_res <- nrow(resolved)
res_lgd <- resolved$observed_lgd
B <- 1000L
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(res_lgd[idx])
}
ci_low  <- unname(quantile(boot_means, 0.025))
ci_high <- unname(quantile(boot_means, 0.975))

# --- Sub-population LGDs (corrected / resolved-only) -------------------------
lgd_by_grade_df <- resolved %>%
  group_by(grade) %>%
  summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
  arrange(grade)

lgd_by_purpose_df <- resolved %>%
  group_by(purpose) %>%
  summarise(corrected_lgd = mean(observed_lgd), .groups = "drop") %>%
  arrange(purpose)

# --- Grade x Purpose panel ---------------------------------------------------
totals_panel <- df %>%
  group_by(grade, purpose) %>%
  summarise(
    n_total    = dplyr::n(),
    n_resolved = sum(is_resolved),
    naive_lgd  = mean(observed_lgd),
    .groups    = "drop"
  )

resolved_panel <- resolved %>%
  group_by(grade, purpose) %>%
  summarise(corrected_lgd = mean(observed_lgd), .groups = "drop")

panel <- totals_panel %>%
  left_join(resolved_panel, 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 assessment ---------------------------------------------------------
naive_minus_corrected <- 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     = as.integer(sum(df$is_resolved)),
    n_censored     = as.integer(nrow(df) - sum(df$is_resolved)),
    share_resolved = mean(df$is_resolved)
  ),
  naive_lgd     = list(value = naive_lgd),
  corrected_lgd = list(value = correcte
…[truncated]

output

Existing file was successfully overwritten at /workspace/analysis.R

bash

cd /workspace && rm -rf /workspace/output/* && Rscript analysis.R 2>&1

output

Naive LGD: 0.6771
Corrected LGD: 0.5651
95% CI: [0.5577, 0.5727]
Direction: materially_overestimates

bash

ls -la /workspace/output/

output

total 64
drwxrws--- 2 h2tagent h2tagent  4096 Jun  1 09:34 .
drwxrws--- 4 h2tagent h2tagent  4096 Jun  1 09:34 ..
-rw-r--r-- 1 h2tagent h2tagent 27387 Jun  1 09:34 fig1_lgd_by_grade.png
-rw-r--r-- 1 h2tagent h2tagent 22803 Jun  1 09:34 fig2_naive_vs_corrected.png
-rw-r--r-- 1 h2tagent h2tagent   611 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   773 Jun  1 09:34 metrics.json
-rw-r--r-- 1 h2tagent h2tagent   907 Jun  1 09:34 plot_contract.json

bash

cat /workspace/output/metrics.json

output

{
  "data_summary": {
    "n_total": 6000,
    "n_resolved": 3185,
    "n_censored": 2815,
    "share_resolved": 0.530833
  },
  "naive_lgd": {
    "value": 0.677057
  },
  "corrected_lgd": {
    "value": 0.565126
  },
  "bootstrap_ci": {
    "ci_low": 0.557675,
    "ci_high": 0.572659
  },
  "lgd_by_grade": {
    "A": 0.435298,
    "B": 0.499192,
    "C": 0.571508,
    "D": 0.652621,
    "E": 0.719296,
    "F": 0.797467,
    "G": 0.849548
  },
  "lgd_by_purpose": {
    "credit_card": 0.591114,
    "debt_consolidation": 0.548877,
    "home_improvement": 0.542071,
    "major_purchase": 0.610129,
    "other": 0.545481,
    "small_business": 0.625576
  },
  "bias_assessment": {
    "naive_minus_corrected": 0.111931,
    "direction": "materially_overestimates"
  }
}

bash

cat /workspace/output/lgd_panel.csv

output

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
A,other,90,52,0.5730028339585355,0.4161251086238681
A,small_business,86,44,0.6524850156856171,0.501025517461768
B,credit_card,321,165,0.6650274867660854,0.5529570202359294
B,debt_consolidation,796,431,0.6046227193564916,0.4831736900891306
B,home_improvement,144,72,0.5957294070774534,0.4692244006760461
B,major_purchase,92,47,0.6660533581869653,0.5058966135004026
B,other,147,76,0.6134946055553012,0.4707940973640627
B,small_business,107,62,0.6406465639242245,0.5319930675458743
C,credit_card,278,137,0.6975732212004435,0.5846765821152625
C,debt_consolidation,689,376,0.6700856143560795,0.5627652371775993
C,home_improvement,140,81,0.6725210332547668,0.5523445244469769
C,major_purchase,86,38,0.7189961393361699,0.6135765359011776
C,other,108,55,0.6702128871263167,0.5494833684419389
C,small_business,88,46,0.7149621821285365,0.6290844602873757
D,credit_card,215,115,0.7655060220713419,0.6766756615218997
D,debt_consolidation,448,228,0.7371918264998432,0.6344644902504021
D,home_improvement,120,67,0.706739229484161,0.6234518380025803
D,major_purchase,42,19,0.7894081280303623,0.7084756218052387
D,other,87,51,0.6939151170291316,0.6058828728339123
D,small_business,80,41,0.8274598087266876,0.7660441640751415
E,credit_card,90,46,0.8077462377640657,0.7330477412383156
E,debt_consolidation,256,132,0.7862663620497375,0.7003007267441975
E,home_improvement,43,25,0.7541782779865313,0.6981705904655177
E,major_purchase,33,18,0.8385062029526982,0.7830152386610237
E,other,38,22,0.7946576919784877,0.73635573221014206
E,small_business,34,21,0.825301153364123,0.7612354523626295
F,credit_card,56,29,0.8654033610376749,0.8089172904120583
F,debt_consolidation,117,54,0.8459071231536545,0.7861061615750851
F,home_improvement,22,7,0.8708448206943532,0.7572663993839904
F,major_purchase,12,10,0.8568444820470749,0.8427265280385001
F,other,25,10,0.8579346748386366,0.8181945014303231
F,small_business,10,7,0.8268334441356919,0.7836012420916083
G,credit_card,31,12,0.9313126372886666,0.8967948452590078
G,debt_consolidation,56,32,0.8736372909633625,0.8295847885354636
G,home_improvement,18,8,0.8599317504304652,0.8133579562599889
G,major_purchase,9,6,0.8694264325104171,0.8167843285444563
G,other,11,7,0.871591124141334,0.8562888208527432
G,small_business,11,4,0.98019042346479,0.9772421282080737

bash

cat /workspace/output/plot_contract.json

output

{
  "figures": [
    {
      "name": "fig1_lgd_by_grade",
      "series": [
        {
          "grade": "A",
          "corrected_lgd": 0.435298
        },
        {
          "grade": "B",
          "corrected_lgd": 0.499192
        },
        {
          "grade": "C",
          "corrected_lgd": 0.571508
        },
        {
          "grade": "D",
          "corrected_lgd": 0.652621
        },
        {
          "grade": "E",
          "corrected_lgd": 0.719296
        },
        {
          "grade": "F",
          "corrected_lgd": 0.797467
        },
        {
          "grade": "G",
          "corrected_lgd": 0.849548
        }
      ]
    },
    {
      "name": "fig2_naive_vs_corrected",
      "series": [
        {
          "estimator": "naive",
          "value": 0.677057
        },
        {
          "estimator": "corrected",
          "value": 0.565126
        }
      ]
    }
  ]
}

bash

cat /workspace/output/insights.md

output

# Portfolio LGD , defensible read

Across 6000 defaulted accounts (3185 resolved, 2815 right-censored; share resolved = 53.1%), the naive mean of 1 - recovered_amount / principal_at_default is 0.677, while the corrected (resolved-only) mean is 0.565 with a 95% paired-bootstrap CI of [0.558, 0.573]. The naive estimate **materially overestimates** the true LGD by 0.112 (naive - corrected = +0.112). Direction: `materially_overestimates`. Because censored workouts have partial recoveries, their partially-observed LGD overstates the terminal loss; restricting to `is_resolved == 1` removes that upward bias.

bash

python3 -c "
from PIL import Image
for p in ['/workspace/output/fig1_lgd_by_grade.png','/workspace/output/fig2_naive_vs_corrected.png']:
  im = Image.open(p)
  print(p, im.size)
"

output

/workspace/output/fig1_lgd_by_grade.png (720, 480)
/workspace/output/fig2_naive_vs_corrected.png (720, 480)

Good, both images are 720x480, well above the 200x150 minimum. Let me verify the constraints:

bash

grep -nE 'file\.copy|file\.rename|file\.link|file\.symlink|file\.append|system\(|system2\(|shell\(|pipe\(|saveRDS\(|writeBin\(|as\.raw\(|sink\(|readRDS\(|readBin\(|load\(' /workspace/analysis.R || echo "No forbidden functions found"

output

No forbidden functions found

bash

# Test that running twice works (no side effects issues)
cd /workspace && rm -rf /workspace/output/* && Rscript analysis.R 2>&1 && echo "---- second run ----" && Rscript analysis.R 2>&1

output

Naive LGD: 0.6771
Corrected LGD: 0.5651
95% CI: [0.5577, 0.5727]
Direction: materially_overestimates
---- second run ----
Naive LGD: 0.6771
Corrected LGD: 0.5651
95% CI: [0.5577, 0.5727]
Direction: materially_overestimates

Reproducible across runs, as expected thanks to the fixed seed. Let me also verify with a different LGD_OUTPUT_DIR (simulating the hidden grader):

bash

mkdir -p /tmp/hidden_output && LGD_OUTPUT_DIR=/tmp/hidden_output Rscript /workspace/analysis.R 2>&1 && ls /tmp/hidden_output/

output

Naive LGD: 0.6771
Corrected LGD: 0.5651
95% CI: [0.5577, 0.5727]
Direction: materially_overestimates
fig1_lgd_by_grade.png
fig2_naive_vs_corrected.png
insights.md
lgd_panel.csv
metrics.json
plot_contract.json

bash

# Final verification - check corrected_lgd is NOT NaN for any group with n_resolved > 0
python3 -c "
import csv, math
with open('/workspace/output/lgd_panel.csv') as f:
  r = list(csv.DictReader(f))
bad = [row for row in r if int(row['n_resolved']) > 0 and (row['corrected_lgd'].upper() in ('NA','NAN','') or math.isnan(float(row['corrected_lgd'])))]
print('bad rows (n_resolved>0 but corrected NaN):', len(bad))
print('total rows:', len(r))
print('rows with n_resolved == 0:', sum(1 for row in r if int(row['n_resolved'])==0))
"

output

bad rows (n_resolved>0 but corrected NaN): 0
total rows: 42
rows with n_resolved == 0: 0

All 42 `grade × purpose` groups have `n_resolved > 0` and valid `corrected_lgd`. Let me do a final sanity check:

str_replace_based_edit_tool

command: view
path: /workspace/output/fig1_lgd_by_grade.png

output

<output_image>
…[truncated]

str_replace_based_edit_tool

command: view
path: /workspace/output/fig2_naive_vs_corrected.png

output

<output_image>
…[truncated]

Both figures render correctly. Summary of the audit and fix: **What was wrong in the draft:** 1. `corrected_lgd` was computed by **trimming** the full dataset's LGDs to (0, 1) instead of restricting to `is_resolved == 1` rows. 2. Bootstrap used the wrong (full) dataset and only 200 reps instead of 1000 on resolved loans. 3. `lgd_by_grade`, `lgd_by_purpose`, and the panel's `corrected_lgd` all averaged every row regardless of censoring. 4. The panel's `corrected_lgd` was identical to `naive_lgd`. 5. `plot_contract.json` had an empty `figures` array and the PNGs were blank (`plot.new()` only). 6. `insights.md` was a stub rather than a paragraph naming direction/magnitude. **What I did:** - Computed `naive_lgd` over every row and `corrected_lgd` strictly over `is_resolved == 1`. - Ran a paired bootstrap (B = 1000, sampling resolved row indices so the recovered/principal pair stays together), seeded with `set.seed(20260606)` immediately before the loop. - Built `lgd_by_grade`, `lgd_by_purpose`, and the panel's `corrected_lgd` on the resolved sub-population (resolved-mean joined back onto the full grade×purpose totals so every group with `n_resolved > 0` gets a non-NaN corrected LGD , confirmed for all 42 groups). - Applied the ±0.05 direction rule → `materially_overestimates` (naive 0.677 vs corrected 0.565, gap +0.112). - Wrote real PNGs via `ggsave` (720×480) and a `plot_contract.json` whose `figures` is an array of objects, each with `name` matching the PNG filename (no extension) and a non-empty `series`. - Wrote a one-paragraph `insights.md` naming the direction and magnitude. - Kept the script free of all forbidden functions (verified by grep). Results (reproducible across runs, confirmed twice): - Naive LGD = 0.6771, Corrected LGD = 0.5651, 95% CI [0.558, 0.573] - Direction = `materially_overestimates` (bias = +0.112)

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: lending-club-lgd-bias-correction-r
# attempt: 7  status: completed  raw_score: 0.0
# reward rule: binary: score 0.0 != 1.0
# => reward = 0.0  (FAIL)
#
# 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).
----------------------------------------------------------------------
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…[truncated]

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_922f356c7ba94a9a. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_922f356c7ba94a9a · verifier authoritative; classifier explanatory.