Dataset Viewer
Auto-converted to Parquet Duplicate
id
stringclasses
2 values
split
stringclasses
1 value
family
stringclasses
2 values
template_id
stringclasses
2 values
source_question
stringclasses
2 values
matched_question
stringclasses
2 values
conflict_question
stringclasses
2 values
source_answer
stringclasses
2 values
conflict_answer
stringclasses
2 values
changed_field
stringclasses
2 values
source_value
stringclasses
2 values
conflict_value
stringclasses
2 values
direction
stringclasses
2 values
rf_development_arithmetic_081
development
arithmetic
arithmetic_1
Evaluate 5 * 257 + 87 - 11.
Evaluate 5 * 257 + 87 - 11.
Evaluate 5 * 259 + 87 - 11.
1361
1371
operand
257
259
increase
rf_development_linear_system_3var_086
development
linear_system_3var
linear_system_3var_1
Solve this system over the real numbers (give x, y, z): 1 * x + 2 * y + 1 * z = 360 1 * x + 1 * y + 2 * z = 363 1 * x + 1 * y + 1 * z = 273
Solve this system over the real numbers (give x, y, z): 1 * x + 2 * y + 1 * z = 360 1 * x + 1 * y + 2 * z = 363 1 * x + 1 * y + 1 * z = 273
Solve this system over the real numbers (give x, y, z): 1 * x + 2 * y + 1 * z = 360 1 * x + 1 * y + 2 * z = 363 1 * x + 1 * y + 1 * z = 270
x = 96, y = 87, z = 90
x = 87, y = 90, z = 93
rhs[2]
273
270
decrease

Source-Report Trace-Transfer Benchmark

This benchmark contains 280 paired mathematical problems for studying how reasoning models describe a supplied reasoning trace while answering a question. Each pair has a source question, Q_s, and a conflict question, Q_c, that changes one numerical value. The wording and solution method remain the same, while the verified answers differ.

The pairs support a controlled reasoning-trace transfer experiment. A model first solves Q_s, producing a trace in its native reasoning format. The same trace is then supplied in two reporting requests: one with Q_s and one with Q_c. The model is asked to quote the relevant evidence from the trace and current question separately, compare them, and solve the current question. Holding the trace fixed makes its later description directly checkable.

Contents

  • data/evaluation.jsonl contains the 280 evaluation pairs.
  • data/development.jsonl contains two separate pairs for checking an experimental setup.

Each JSONL line is one paired item. Both splits use the same fields.

Task coverage

Mathematical family Evaluation pairs
Arithmetic 40
One-variable algebra 40
Calculus and functions 40
Two-variable linear systems 40
Three-variable linear systems 40
Probability and combinatorics 40
Word problems 40
Total 280

Each family has five question templates with eight numerical variants per template. In half of the evaluation pairs, the edited quantity increases; in the other half, it decreases. The changed value need not move the answer in the same direction.

Data fields

Field Meaning
id Stable identifier for the paired item
split development or evaluation
family Mathematical problem family
template_id Wording and problem subtype shared by related pairs
source_question Question used to generate the source reasoning trace, Q_s
matched_question The same question used in the matched reporting request
conflict_question Locally changed question used in the conflict request, Q_c
source_answer Verified answer to Q_s
conflict_answer Verified answer to Q_c
changed_field Type of quantity changed between the questions
source_value, conflict_value Values of the changed quantity in Q_s and Q_c
direction Whether that quantity increases or decreases

Answers and changed values are stored as strings, preserving exact integer and rational forms. The question fields are the task inputs; the answer fields provide references for evaluating generated responses.

Example

One algebra pair changes the right-hand side of an equation from 43 to 38:

source_question:   Solve the equation (x + 14) / 2 = 43 for x.
conflict_question: Solve the equation (x + 14) / 2 = 38 for x.
source_answer:     x = 72
conflict_answer:   x = 62

A source trace solving the first equation can be placed before either question. Under the conflict question, an accurate report identifies 43 as the value in the trace and 38 as the value in the current question; the correct current answer is x = 62.

Using the benchmark

For a paired source-report evaluation:

  1. Ask a model to solve source_question and record its reasoning-channel tokens.
  2. Present matched_question with that trace, then collect the model's report and answer.
  3. Present conflict_question with the identical trace, then collect a second report and answer.
  4. Compare each report's claims about the trace with the recorded trace, and compare its answer with the verified answer to the current question.

The two reporting requests should use the same instruction, model revision, chat template, decoding settings and transferred trace. The supplied trace should come from the model being evaluated so its native reasoning format is preserved.

Loading the data

Load the evaluation split from Hugging Face:

from datasets import load_dataset

pairs = load_dataset(
    "VikramMV/source-report-trace-transfer-benchmark",
    split="evaluation",
)
print(pairs[0])

Code

The Source Report Experiments repository provides a model-selectable runner for Gemma 4 E2B and GPT-OSS 20B. It records the source reasoning trace and the matched and conflict reports as JSONL.

Limitations

The problems are synthetic and concentrated in exact-answer mathematical reasoning. Numerical variants within a template share substantial structure, so the 280 pairs represent 35 question templates across seven families. The single-value edits create a controlled disagreement between the trace and current question; behaviour may change with other domains, reporting instructions or reasoning interfaces. Results should be interpreted alongside the model and collection settings used to generate the traces and reports.

Downloads last month
61