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arxiv: 2512.00127 · v3 · submitted 2025-11-28 · 💻 cs.SE · cs.AI· cs.PL

Generating Verifiable Chain of Thoughts from Exection-Traces

Shailja Thakur , Vaibhav Saxena , Rohan Kulkarni , Shivdeep Singh , Parameswaran Selvam , Hima Patel , Hiroshi Kanayama This is my paper

Pith reviewed 2026-05-17 05:13 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.PL
keywords chain-of-thoughtexecution tracescode generationverifiable reasoningfine-tuningprogram verificationsynthetic data generation
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0 comments X

The pith

Execution traces can be converted into verified chain-of-thought data that substantially improves code reasoning in language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a pipeline to create training data for language models that reason about code. It instruments programs to record execution traces, translates these traces into natural language explanations, and verifies that each explanation accurately reflects the trace. This produces 54,000 bi-directional rationales that allow reasoning from inputs to outputs and vice versa. When models are fine-tuned on this data, they show large gains on benchmarks like LiveCodeBench-Exec and HumanEval. The work argues that the quality of verification in the training data is what drives better reasoning and code generation.

Core claim

We build a pipeline that generates execution-trace-verified CoT rationales by instrumenting code to capture traces, narrating them into natural language, and cross-checking each narration against the original trace. We systematically create 54,000 verified, bi-directional rationales that teach models to reason both forward and backward. Models fine-tuned on our verified data achieve substantial improvements, with a peak gain of +26.6 on LiveCodeBench-Exec, +22.2 on CruxEval, and +19.5 on HumanEval, demonstrating that verification quality directly determines both reasoning and code generation capabilities.

What carries the argument

The synthesis pipeline that instruments code for trace capture, narrates traces to natural language, and cross-checks narrations for fidelity to produce verified bi-directional rationales.

If this is right

  • Models trained on verified rationales show improved performance on code execution and reasoning benchmarks.
  • Bi-directional training enables both forward prediction and backward reasoning from outputs.
  • Verification quality in training data is a key factor in model capabilities for reasoning and generation.
  • The open-source pipeline supports creation of similar datasets for other tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar verification approaches could improve training data in other areas like mathematical reasoning where step-by-step checks are possible.
  • Emphasizing data quality through verification might allow smaller models to achieve results comparable to larger ones trained on noisier data.
  • Extending the method to handle more complex programs or different programming languages could broaden its impact on software engineering tasks.

Load-bearing premise

Converting execution traces into natural-language narrations and cross-checking them produces accurate, complete, and pedagogically useful reasoning steps without introducing semantic drift or omitting critical program behavior.

What would settle it

Observing no improvement or even degradation in model performance on LiveCodeBench-Exec, CruxEval, or HumanEval after fine-tuning on the verified data compared to baselines would indicate the method does not deliver the claimed benefits.

Figures

Figures reproduced from arXiv: 2512.00127 by Hima Patel, Hiroshi Kanayama, Parameswaran Selvam, Rohan Kulkarni, Shailja Thakur, Shivdeep Singh, Vaibhav Saxena.

Figure 1
Figure 1. Figure 1: Comparison of hallucinated vs trace-grounded CoT. Our approach translates pysnooper [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of our three-stage data synthesis pipeline. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Bi-directional CoT data format examples. Blue tags denote prompt components (instruction, function, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Signature format templates with extracted metadata. Generated signatures specify function/class [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Test format requirements. Correct format (left) enables clean I/O extraction for trace generation with [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Heatmap showing percentage of problems achieving high consensus scores as a function of number of [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Performance comparison against SOTA baselines [ [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance boost from fine-tuning instruct models. Solid bars represent the baseline instruct models; [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Chain-of-Thought quality analysis. Top: CoT-to-outcome consistency vs reasoning length with regres [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
read the original abstract

Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, and not verifiable accounts of actual program behavior. Models trained on such data learn logically flawed reasoning patterns despite syntactic correctness. To address this, we build a pipeline that generates execution-trace-verified CoT rationales by instrumenting code to capture traces, narrating them into natural language, and cross-checking each narration against the original trace. We systematically create 54,000 verified, bi-directional rationales that teach models to reason both forward (input$\rightarrow$output) and backward (output$\rightarrow$input). Models fine-tuned on our verified data achieve substantial improvements, with a peak gain of +26.6 on LiveCodeBench-Exec, +22.2 on CruxEval, and +19.5 on HumanEval across our fine-tuned models, demonstrating that verification quality directly determines both reasoning and code generation capabilities. Complete synthesis pipeline is avilable as open-source: https://github.com/IBM/verified-code-cot/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents a pipeline to generate verifiable Chain-of-Thought (CoT) rationales for code reasoning tasks. Code is instrumented to capture execution traces, which are then narrated into natural language and cross-checked against the original traces for verification. This process yields 54,000 bi-directional verified rationales. Models fine-tuned on this data report performance gains of +26.6 on LiveCodeBench-Exec, +22.2 on CruxEval, and +19.5 on HumanEval, with the claim that verification quality directly improves reasoning and code generation. The synthesis pipeline is released as open source.

Significance. If the generated rationales prove faithful to execution traces, the work could provide a scalable source of verifiable training data that grounds LLM reasoning in actual program behavior rather than potentially flawed synthetic explanations. The open-source pipeline strengthens reproducibility and enables community extensions.

major comments (2)
  1. [Abstract] Abstract: the reported benchmark gains (+26.6 on LiveCodeBench-Exec, etc.) are presented without details on trace coverage, narration error rates, handling of verification failures, baseline comparisons, or statistical significance. This prevents determining whether improvements are attributable to verification quality.
  2. [Pipeline description] Pipeline description: the central claim rests on the narration-plus-cross-check step producing accurate, complete rationales without semantic drift or omitted behaviors (control flow, side effects, intermediate states). No quantitative fidelity metrics (human or automated) on a held-out sample are provided to support this.
minor comments (1)
  1. [Abstract] Abstract: 'avilable' is a typo and should read 'available'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below. Where the comments correctly identify gaps in the initial submission, we have revised the manuscript to incorporate additional details and metrics.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported benchmark gains (+26.6 on LiveCodeBench-Exec, etc.) are presented without details on trace coverage, narration error rates, handling of verification failures, baseline comparisons, or statistical significance. This prevents determining whether improvements are attributable to verification quality.

    Authors: We agree that the abstract is too concise to convey these details. The full manuscript already includes baseline comparisons in the experiments section and reports statistical significance via paired tests. In revision we have expanded the abstract to note trace coverage (instrumentation succeeded on the large majority of functions), narration error handling (verification failures were filtered prior to inclusion in the 54k set), and that reported gains are statistically significant relative to baselines. This makes the link to verification quality explicit. revision: yes

  2. Referee: [Pipeline description] Pipeline description: the central claim rests on the narration-plus-cross-check step producing accurate, complete rationales without semantic drift or omitted behaviors (control flow, side effects, intermediate states). No quantitative fidelity metrics (human or automated) on a held-out sample are provided to support this.

    Authors: The referee is correct that the original submission lacked explicit quantitative fidelity metrics on a held-out sample. The pipeline description explains the cross-check against execution traces to catch omissions in control flow and intermediate states, but we have now added a dedicated evaluation subsection. It reports automated fidelity metrics (trace-element coverage and consistency scores) together with a human review of a random held-out sample confirming low semantic drift. These results are included in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results rest on external benchmarks

full rationale

The paper describes a pipeline that instruments code, narrates execution traces into natural language, and cross-checks the narrations to produce verified bidirectional CoT data. Models are fine-tuned on the resulting 54k examples and evaluated on independent, established benchmarks (LiveCodeBench-Exec, CruxEval, HumanEval). The reported gains (+26.6, +22.2, +19.5) are measured on these external test sets rather than on any quantity defined from the generated data or pipeline outputs themselves. No equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation chain. The central claim that verification quality drives performance is therefore supported by measurements outside the synthesis process, rendering the reported results self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard assumptions about program semantics and LLM fine-tuning rather than new invented entities or heavily fitted parameters.

axioms (2)
  • domain assumption Execution traces faithfully record the observable behavior of a program under a given input.
    Invoked when the pipeline treats the captured trace as ground truth for verification.
  • domain assumption Natural-language narration of a trace can be made to match the trace semantics closely enough to serve as useful reasoning supervision.
    Central to the claim that the generated rationales are both verifiable and pedagogically effective.

pith-pipeline@v0.9.0 · 5528 in / 1438 out tokens · 66069 ms · 2026-05-17T05:13:21.241652+00:00 · methodology

discussion (0)

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Reference graph

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    This { difficulty } difficulty should create { c o m p l e x i t y _ d e s c r i p t i o n }

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    INSTRUCTIONS FOR PROBLEM DIVERSITY :

    Solutions should span approximately { expected_lines } of code with rich , intricate logic maximized for'hard'tasks . INSTRUCTIONS FOR PROBLEM DIVERSITY :

    work page

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    Create problems that are fundamentally different in : - Problem domain : Include mathematics ( e . g . , algebra for equations and transformations , timing & durations for scheduling or sequencing , probabilities for statistical analysis , geometry for spatial computations ) , finance , data processing , algorithms , text processing , or system design . F...

    work page

  25. [25]

    Before generating , analyze the concept's core principles and identify unique problem - solving strategies that leverage these principles , especially for mathematical domains in'hard'tasks to maximize complexity and clarity

    work page

  26. [26]

    Instructions may request either a standalone function named'solution'or a class named'Solution 'with methods ; indicate clearly if a class is required ( e . g . ,'implement a class') and specify the primary method name ( e . g . ,'compute') if applicable , otherwise assume'compute'as the default primary method for classes . Concept : { concept } Descripti...

    work page

  27. [27]

    , using the specified class name ( e

    Decide if the instruction requires a standalone function or a class : - Choose a CLASS if the instruction EXPLICITLY says'implement a class','create a class', or mentions methods like'constructor','build_tree', etc . , using the specified class name ( e . g . ,'HuffmanTree') . - Otherwise , default to a standalone FUNCTION named'solution'

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    For a FUNCTION : - Format EXACTLY as :'Function : name ( param1 : type1 , param2 : type2 ) -> return_type' - Include parameter names , types ( infer if not specified ) , and return type ( use'unknown'if unclear )

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  29. [29]

    - Include'__init__'with parameters if implied , followed by all required methods

    For a CLASS : - Format EXACTLY as :'Class : ClassName ; __init__ ( self , param1 : type1 ) -> return_type ; method1 ( self , param2 : type2 ) -> return_type ; ...' - Use semicolons (;) to separate class name and methods . - Include'__init__'with parameters if implied , followed by all required methods . - Specify the primary method ( named in instruction ...

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    Matrix

    RULES FOR FORMATTING : - Use ONLY spaces ( no tabs , newlines , or escaped characters like'\') . - Use EXACTLY the syntax shown ( e . g . ,'__init__','->', commas between params ) . - Do NOT add extra punctuation ( e . g . , colons after parentheses ) or quotes around simple types ( e . g . , use'Matrix', not'" Matrix "') . - Do NOT deviate from the templ...

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  31. [31]

    Matrix

    Base your analysis ONLY on the instruction text , inferring types and outputs logically . Instruction : { instruction } Return the signature skeleton INSIDE a code block , following the EXACT format below : ```text Function : solution ( input1 : type1 , input2 : type2 ) -> return_type ``` or ```text Class : ClassName ; __init__ ( self , param1 : type1 ) -...

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    Do NOT deviate from this signature

    ** HIGH PRIORITY **: Implement a standalone function with name'{ function_name }', inputs'{ input_params }', and return type'{ return_type }'EXACTLY as provided . Do NOT deviate from this signature

    work page

  33. [33]

    Write all logic directly within'{ function_name }'- - - do NOT define nested functions , even for multi - step problems ; use variables or steps instead

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  34. [34]

    The function MUST ALWAYS RETURN A VALUE matching'{ return_type }'

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    ** HIGH PRIORITY **: Generate EXACTLY FIVE distinct implementations , all strictly adhering to the provided signature : - Vary each implementation by : - Computational approach : Use distinct methods like iterative loops , recursion , dynamic programming , list comprehensions , or functional programming ( e . g . , map / filter / reduce ) , as appropriate...

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    Do NOT deviate from these signature details

    HIGH PRIORITY : Implement a class with name'{ class_name }'and methods as specified in'{ me th od _s ign at ur es }'( including inputs and return types ) EXACTLY as provided . Do NOT deviate from these signature details

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  37. [40]

    Otherwise , omit the constructor

    Include a constructor'{ c o n s t r u c t o r _ s i g n a t u r e }'ONLY if explicitly provided in the signature details or if the instruction requires initialization of instance variables for the class to function correctly . Otherwise , omit the constructor

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  38. [41]

    Define the class with all necessary methods as specified , avoiding a function template

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  39. [42]

    Each method must be self - contained ; each method MUST RETURN A VALUE matching its specified return type

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  40. [43]

    Ensure the code is fully modular , self - contained , and does not rely on external code or global variables

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  41. [44]

    Optimize for readability , following Python best practices , with clear variable names and comments where necessary

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    For hard difficulty , ensure the solution reflects the expected complexity : sophisticated long problems requiring complex algorithms and data structures (8 -10 difficulty ) , spanning approximately 50 -100+ lines with a difficulty score of 8 -10 on a scale of 1 -10

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    HIGH PRIORITY : Generate EXACTLY FIVE distinct implementations , all strictly adhering to the provided signature details : - Vary each implementation by : - Computational approach : Use distinct methods like iterative loops , recursion , dynamic programming , list comprehensions , or functional programming ( e . g . , map / filter / reduce ) , as appropri...

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    Each test function must contain EXACTLY ONE assert statement

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  45. [49]

    Every assert statement MUST DIRECTLY call the function with specific inputs and compare its result to an expected value using a direct comparison ( e . g . ,`==`,`is`,`in`,`!=`) : - The solution to the task is a standalone function named'{ function_name }', use`assert { function_name }(...) == ...`with all inputs packed into the call . - Do NOT : - Use va...

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    If fewer than 10 scenarios are provided , generate only that number

    Generate up to 10 test cases , each corresponding to one of the required test scenarios provided below , ensuring each test directly calls the function with inputs matching the signature , all within the assert . If fewer than 10 scenarios are provided , generate only that number

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    Verify that each test aligns with the task requirements , signature details , and the specified test scenario ; all inputs must match the provided signature

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  48. [52]

    Ensure every assert statement is complete , specifying a concrete expected output value ( e . g . , a number , list , or string ) and avoiding placeholders ( e . g . ,'...') . Calculate the exact expected result based on the task description and signature for each test case . Task Description : { instruction } 26 Thakur et al. Signature Details : ```pytho...

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  49. [53]

    Each test case must be a standalone Python function ( e . g . ,`def test_ ...() :`) , NOT defined within a class , to ensure easy parsing and execution

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  50. [54]

    Each test function must contain EXACTLY ONE assert statement , unless the solution is a class with multiple methods and multiple asserts are needed to call logically connected methods ( e . g . , setup methods ) before the primary method ; in such cases , separate each assert with a numbered comment like`# Test Case 1`,`# Test Case 2`, etc . , to distingu...

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    Every assert statement MUST DIRECTLY call the connected methods with specific inputs and compare its result to an expected value using a direct comparison ( e . g . ,`==`,`is`,`in`,`!=`) : - The solution to the task is a class named'{ class_name }'. The primary method to test is'{ primary_method }'. Instantiate it as`{ class_name }()`and call methods dire...

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  52. [56]

    If fewer than 10 scenarios are provided , generate only that number

    Generate up to 10 test cases , each corresponding to one of the required test scenarios provided below , ensuring each test directly calls the relevant method ( s ) with inputs matching their signature , all within the assert . If fewer than 10 scenarios are provided , generate only that number

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  53. [57]

    Generating Verifiable Chain of Thoughts from Exection-Traces 27

    Verify that each test aligns with the task requirements , signature details , and the specified test scenario ; all inputs must match the method signatures . Generating Verifiable Chain of Thoughts from Exection-Traces 27

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  54. [58]

    Ensure every assert statement is complete , specifying a concrete expected output value ( e . g . , a number , list , or string ) and avoiding placeholders ( e . g . ,'...') . Calculate the exact expected result based on the task description and signature for each test case . Task Description : { instruction } Signature Details : ```python Class : { class...

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  55. [59]

    Analyze the input and the initial operations ( sorting and extracting probabilities )

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  56. [60]

    Check if normalization is needed and perform it if necessary

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  57. [61]

    Calculate the PDF using the normalized probabilities

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  58. [62]

    Calculate the CDF by accumulating the PDF values

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  59. [63]

    ### Execute

    Verify the final output by considering the logical flow and ensuring all steps are consistent . ### Execute

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  60. [64]

    The sorting step sorts the events by the second element ( probability ) , but since both probabilities are equal , the order remains`('a', 0.5) , ('b', 0.5)`

    ** Input and Sorting **: The input is`events = [('a', 0.5) , ('b', 0.5) ]`. The sorting step sorts the events by the second element ( probability ) , but since both probabilities are equal , the order remains`('a', 0.5) , ('b', 0.5)`

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  61. [65]

    ** Extracting Probabilities **: The probabilities extracted are`[0.5 , 0.5]`

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  62. [66]

    Since the total is already 1 , no normalization is needed , and the probabilities remain `[0.5 , 0.5]`

    ** Normalization Check **: The sum of probabilities is`0.5 + 0.5 = 1.0`. Since the total is already 1 , no normalization is needed , and the probabilities remain `[0.5 , 0.5]`

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  63. [67]

    ** PDF Calculation **: The PDF is directly the list of probabilities ,`[0.5 , 0.5]`

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    - For the first probability`0.5`,`cumulative_prob = 0 + 0.5 = 0.5`

    ** CDF Calculation **: - Initialize`cumulative_prob = 0`. - For the first probability`0.5`,`cumulative_prob = 0 + 0.5 = 0.5`. Append`0.5` to`cdf`. - For the second probability`0.5`,`cumulative_prob = 0.5 + 0.5 = 1.0`. Append `1.0`to`cdf`. - The resulting CDF is`[0.5 , 1.0]`

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    ** Final Output **: The function returns the tuple`( cdf , pdf )`, which is`([0.5 , 1.0] , [0.5 , 0.5])`. ### Reflect The reasoning process involved analyzing each step of the function , ensuring that the probabilities were correctly extracted , checked for normalization , and used to compute the PDF and CDF . The calculations were straightforward , and t...

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    ** Initialization **: Understand the initial setup of variables like`start`,` remaining`, and`route`

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  67. [71]

    ** Loop Execution **: Analyze the loop's behavior , focusing on how it selects the next location based on distance and prioritization

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  68. [72]

    ** Return to Start **: Consider the final step where the route returns to the starting point

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  69. [73]

    ### Execute

    ** Verify **: Ensure the reasoning aligns with the expected behavior of the function . ### Execute

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  70. [74]

    This is not applicable here since there are four locations

    ** Initialization **: Generating Verifiable Chain of Thoughts from Exection-Traces 31 - The function begins by checking if there are fewer than two locations , in which case it returns the input as is . This is not applicable here since there are four locations . -`start`is set to the first location`(0 , 0)`. -`remaining`is initialized with the rest of th...

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  71. [75]

    -`route`starts with`[ start ]`, i

    ]`. -`route`starts with`[ start ]`, i . e . ,`[(0 , 0) ]`

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    - For each iteration , the current location is the last in`route`

    ** Loop Execution **: - The loop continues as long as there are locations in`remaining`. - For each iteration , the current location is the last in`route`. - The`remaining`locations are sorted based on whether they are within 10 units and then by distance . - The closest location is selected and added to`route`, then removed from` remaining`. ** Iteration...

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  73. [77]

    -`next_location`is`(1 , 1)`, added to`route`, and removed from`remaining`

    ]`. -`next_location`is`(1 , 1)`, added to`route`, and removed from`remaining`. - ** Second Iteration **: -`current`is`(1 , 1)`. - Distances to remaining locations : -`(2 , 2)`: \(\ sqrt {2}\) -`(3 , 3)`: \(\ sqrt {8}\) - Both are within 10 units , sorted by distance :`[(2 , 2) , (3 , 3) ]`. -`next_location`is`(2 , 2)`, added to`route`, and removed from`re...

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  74. [78]

    ** Return to Start **: - After the loop , the route returns to the starting point`(0 , 0)`by appending it to`route`

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    ### Reflect The reasoning process aligns with the function's logic , ensuring that each step is consistent with the code's behavior

    ** Final Route **: - The constructed route is`[(0 , 0) , (1 , 1) , (2 , 2) , (3 , 3) , (0 , 0) ]`. ### Reflect The reasoning process aligns with the function's logic , ensuring that each step is consistent with the code's behavior . The function correctly constructs a route based on proximity and returns to the start , confirming the expected output . The...

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  76. [80]

    ** Euclidean Distance Calculation **: A helper function`euclidean_distance`computes the distance between two points

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  77. [81]

    It also considers points near the dividing line to ensure the closest pair is found

    ** Recursive Closest Pair Function **: The`closest_pair`function recursively divides the list of points and finds the closest pair in each half . It also considers points near the dividing line to ensure the closest pair is found

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    ** Brute Force Method **: For small subsets of points (3 or fewer ) , the`brute_force` function is used to find the closest pair by checking all possible pairs

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  79. [83]

    The function's goal is to return the pair of points with the smallest Euclidean distance

    ** Sorting **: The input coordinates are sorted by their x - coordinates before processing . The function's goal is to return the pair of points with the smallest Euclidean distance . ### Plan To determine the output for the input`[(1 , 2) , (3 , 4) ]`, we will :

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  80. [84]

    ** Analyze Initialization **: Understand how the input is processed initially , particularly the sorting step

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Showing first 80 references.