arxiv: 2604.20917 · v1 · submitted 2026-04-22 · 💻 cs.LG · cs.AI· cs.CL· cs.PL· cs.SE

Recognition: unknown

The Path Not Taken: Duality in Reasoning about Program Execution

Eshgin Hasanov , Md Mahadi Hassan Sibat , Santu Karmaker , Aashish Yadavally

Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:41 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CLcs.PLcs.SE

keywords large language modelsprogram executioncode reasoningbenchmarkdual path reasoningdynamic analysisLLM evaluationinput mutation

0 comments

The pith

Dual reasoning paths—predicting program behavior and inferring input changes—together test whether language models truly understand code execution.

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

The paper claims that single-direction benchmarks for how LLMs handle code are insufficient because they only test what happens when a program runs on a given input. This leaves open the possibility that models succeed through surface patterns or memorized examples rather than grasping execution flow. To address this, the authors introduce two complementary tasks: one that asks a model to predict observed behavior from an input, and another that asks it to figure out what input would produce a target behavior. They create DexBench containing 445 paired instances of these tasks and run it on 13 models. The results indicate that strong performance across both directions serves as a clearer signal of genuine dynamic understanding.

Core claim

Dual-path reasoning through behavior prediction and input mutation inference serves as a robust and discriminative proxy for a model's causal understanding of program execution flow, as demonstrated by evaluations on the DexBench benchmark.

What carries the argument

The duality of forward behavior prediction for a given input and backward inference of an input that achieves a target behavior, which together require models to reason about execution in both directions.

If this is right

Single-task benchmarks for code properties are prone to overestimating understanding due to contamination or pattern matching.
Models that handle both forward prediction and backward mutation inference exhibit stronger evidence of grasping program dynamics.
The paired-task design in DexBench can be used to extend existing evaluation suites for dynamic code reasoning.
Dual-path testing distinguishes models that have learned execution mechanics from those that have learned correlations only.

Where Pith is reading between the lines

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

The same forward-and-backward pairing could be applied to other domains involving sequential processes, such as verifying hardware designs or simulating physical systems.
Models strong at input mutation inference may transfer well to tasks like generating test cases or localizing bugs without explicit traces.
Training objectives that explicitly optimize for both directions might produce LLMs with improved ability to follow and modify execution paths in generated code.

Load-bearing premise

Success on both predicting what a program does and finding inputs for desired outputs means the model understands causal execution flow rather than relying on surface patterns or memorized data.

What would settle it

An experiment showing that models scoring high on both DexBench tasks still fail to simulate execution correctly on a fresh set of programs with structures and control flows absent from any training data.

Figures

Figures reproduced from arXiv: 2604.20917 by Aashish Yadavally, Eshgin Hasanov, Md Mahadi Hassan Sibat, Santu Karmaker.

**Figure 1.** Figure 1: The program accepts multiple paths, including π (1) : 3→...→6→8→ 9 →...→6→ 8 →9→ 5 → 12 → [END], π (2) : 3 → ... → 5 → 6 → 7 → [END], π (3) : 3 → ... → 6 → 8 → ... → 11 → 5 → ... → [END], among others. Any pair ⟨π (i) , π(j) ⟩ reflects this multiality. For the test input “ua6hajq”, execution follows the control-flow path π (1). Here, we designate π (1) as the execution path πexec and select π (2) as the … view at source ↗

**Figure 2.** Figure 2: Distribution of different program complexity [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Strict performance comparison (pass@1, in %) in execution-only, counterfactual-only, and dual-path reasoning evaluation settings across three datasets. Jamba Reasoning 3B Nemotron Nano Llama-3.3-3B-Inst. Mistral Small 24B Magistral Small QwQ-32B Qwen2.5-32B Llama-3.3-70B-Inst. Qwen2.5-72B Gemini 2.5 Flash GPT-5 Mini Grok-4 Reasoning Claude Sonnet 4 0 20 40 60 80 100 pass@1 Strict (Exact Match) Relaxed (Jac… view at source ↗

**Figure 4.** Figure 4: Strict vs. relaxed performance comparison [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Robustness to prompt complexity. Average model performance for execution reasoning (pass@1, in %). Here, whiskers denote ±σ across three datasets. 4.3 Sensitivity Analysis In this section, we examine the robustness of model performance in both execution and counterfactual reasoning under different perturbations. Robustness to Prompt Complexity. To assess the robustness of execution reasoning within DEXBENC… view at source ↗

**Figure 6.** Figure 6: Prompt templates for: (top) execution, and (bottom) counterfactual reasoning in DEXBENCH [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 7.** Figure 7: Incorrect letter counting logic: Here, the model fails to correctly analyze string operations. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Incorrect splitlines() analysis: Here, the model misunderstands the edge cases in text processing [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

read the original abstract

Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of program execution rather than relying on surface-level patterns. Existing benchmarks primarily focus on predicting program properties tied to specific inputs (e.g., code coverage, program outputs). As a result, they provide a narrow view of dynamic code reasoning and are prone to data contamination. We argue that understanding program execution requires evaluating its inherent duality through two complementary reasoning tasks: (i) predicting a program's observed behavior for a given input, and (ii) inferring how the input must be mutated toward a specific behavioral objective. Both tasks jointly probe a model's causal understanding of execution flow. We instantiate this duality in DexBench, a benchmark comprising 445 paired instances, and evaluate 13 LLMs. Our results demonstrate that dual-path reasoning provides a robust and discriminative proxy for dynamic code understanding.

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.

Desk Editor's Note private letter to a colleague

DexBench pairs forward behavior prediction with backward input inference to test LLM code understanding, but the abstract gives no ablations or construction details to show the tasks actually require causal execution knowledge instead of surface patterns.

read the letter

The paper's main contribution is the duality framing itself: one task asks an LLM to predict program behavior from code plus input, while the other asks it to infer the input that would produce a target behavior. They turn this into DexBench with 445 paired instances and evaluate 13 models, claiming the joint performance is a better proxy for dynamic understanding than existing single-direction benchmarks that are vulnerable to contamination. That pairing idea is new relative to the priors mentioned in the abstract, and the motivation is stated plainly. It does a decent job explaining why narrow tests like output prediction or coverage leave gaps and how requiring both directions might close them. The conceptual move is straightforward and worth considering for anyone building code-reasoning evals. The soft spot is exactly the one the stress-test note flags. The central claim needs the two tasks to jointly probe causal grasp of execution flow rather than statistical shortcuts or memorized fragments. The abstract supplies no ablations, no description of how the pairs were built to block surface cues, and no quantitative results or comparisons. Without those, it is difficult to know whether models that do well are actually reasoning about control flow or just exploiting correlations that happen to appear in both directions. The evidence for the claim is therefore thin at present. This is for researchers who design or critique benchmarks for LLM coding tools and who care about contamination. A reader looking for a new angle on dynamic code evaluation will get something from the framing even if the current support is preliminary. It deserves peer review because the benchmark and the duality concept could be useful once the construction details and controls are added and checked; referees can determine whether the empirical results hold up or whether the interpretation needs to be tempered.

Referee Report

2 major / 0 minor

Summary. The paper argues that true dynamic code understanding in LLMs requires assessing the inherent duality of program execution via two complementary tasks: (i) predicting observed behavior given code and input, and (ii) inferring input mutations to achieve a target behavior. These tasks are instantiated in the DexBench benchmark (445 paired instances) and used to evaluate 13 LLMs, with the conclusion that dual-path reasoning serves as a robust, discriminative proxy less prone to data contamination than existing benchmarks focused on narrow properties like coverage or outputs.

Significance. If the empirical results hold under proper validation, the work could meaningfully improve evaluation of LLMs' causal grasp of execution flow, addressing key limitations in current code reasoning benchmarks. The emphasis on duality and paired tasks offers a potentially falsifiable framework for distinguishing surface patterns from deeper understanding.

major comments (2)

[Abstract and benchmark construction] The central claim that the two tasks 'jointly probe a model's causal understanding of execution flow' (Abstract) rests on an untested premise; no ablations, controls for surface correlations (e.g., code obfuscation or equivalent syntax variants), or contamination checks are described for the 445 DexBench instances, making it impossible to verify that success reflects execution semantics rather than statistical shortcuts.
[Evaluation] Quantitative results, exact metrics, instance construction details, and model performance breakdowns are absent from the provided manuscript text, undermining the assertion that dual-path reasoning is 'robust and discriminative' (Abstract); without these, the evaluation of 13 LLMs cannot support the proxy claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for strengthening the presentation of our dual-path reasoning framework and DexBench benchmark. We address each major comment below, providing clarifications based on the full manuscript and outlining targeted revisions to enhance empirical support and clarity.

read point-by-point responses

Referee: [Abstract and benchmark construction] The central claim that the two tasks 'jointly probe a model's causal understanding of execution flow' (Abstract) rests on an untested premise; no ablations, controls for surface correlations (e.g., code obfuscation or equivalent syntax variants), or contamination checks are described for the 445 DexBench instances, making it impossible to verify that success reflects execution semantics rather than statistical shortcuts.

Authors: We acknowledge that the manuscript text does not explicitly detail ablations or contamination analyses in the main body, which limits immediate verifiability of the causal claim. The benchmark design pairs forward behavior prediction with backward input mutation inference for each of the 445 instances, requiring models to reason about execution causality (e.g., how specific input changes produce targeted behavioral shifts) rather than isolated properties. To directly address the concern, the revised version will include a dedicated subsection with ablations on obfuscated and syntax-variant code, plus contamination checks comparing DexBench against public code datasets. These will empirically demonstrate that dual-task performance reflects semantic understanding beyond surface correlations. revision: yes
Referee: [Evaluation] Quantitative results, exact metrics, instance construction details, and model performance breakdowns are absent from the provided manuscript text, undermining the assertion that dual-path reasoning is 'robust and discriminative' (Abstract); without these, the evaluation of 13 LLMs cannot support the proxy claim.

Authors: The full manuscript provides these elements in Sections 3 and 4: instance construction details the generation of 445 paired tasks with concrete examples; metrics are defined as exact-match accuracy for forward prediction and mutation success rate for backward inference; and results include tables with full performance breakdowns across the 13 LLMs (e.g., distinguishing GPT-4's dual-path accuracy from smaller models). These show dual reasoning as more discriminative than single-task baselines. We will revise by moving key tables and breakdowns from the appendix into the main text for better visibility, ensuring the proxy claim is directly supported by the quantitative evidence. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark with independent evaluation

full rationale

The paper advances an argument that program execution understanding is best probed via a duality of behavior prediction and input mutation inference, then instantiates this in the DexBench dataset of 445 paired instances and reports LLM performance. No equations, derivations, fitted parameters, or self-citations appear in the provided text. The central claim is an empirical observation about model performance on the new benchmark rather than a reduction of any result to its own inputs by construction. The benchmark tasks are externally defined and falsifiable, with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility; the duality concept is a domain framing rather than a mathematical derivation.

axioms (1)

domain assumption Dual forward and backward reasoning tasks jointly probe causal understanding of program execution
Explicitly stated as the motivation for the benchmark in the abstract.

pith-pipeline@v0.9.0 · 5471 in / 1118 out tokens · 91276 ms · 2026-05-10T00:41:15.699661+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 3 canonical work pages · 2 internal anchors

[1]

Evaluating Large Language Models Trained on Code

Evaluating large language models trained on code. CoRR, abs/2107.03374. Nuo Chen, Hongguang Li, Baoyuan Wang, and Jia Li

work page internal anchor Pith review Pith/arXiv arXiv
[2]

CoRR, abs/2401.05384

From good to great: Improving math reason- ing with tool-augmented interleaf prompting. CoRR, abs/2401.05384. Hridya Dhulipala, Aashish Yadavally, Smit Soneshbhai Patel, and Tien N. Nguyen. 2025. CRISPE: semantic- guided execution planning and dynamic reasoning for enhancing code coverage prediction. Proc. ACM Softw. Eng., 2(FSE):2965–2986. Yangruibo Ding...

work page arXiv 2025
[3]

Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models

Assessing coherency and consistency of code execution reasoning by large language models. CoRR, abs/2510.15079. Changshu Liu, Shizhuo Dylan Zhang, and Reyhaneh Jabbarvand. 2024. Codemind: A framework to challenge large language models for code reasoning. CoRR, abs/2402.09664. Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu F...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[4]

In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, Findings of ACL, pages 17113–17126

Pythonsaga: Redefining the benchmark to evaluate code generating llms. In Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, Florida, USA, November 12-16, 2024, Findings of ACL, pages 17113–17126. Association for Computational Linguistics. Aashish Yadavally, Yi Li, and Tien N. Nguyen. 2024a. Predictive program slicing via execut...

2024
[5]

Put your reasoning inside <REASONING></REASONING> tags

First, analyze the execution paths and predict which lines would be executed. Put your reasoning inside <REASONING></REASONING> tags
[6]

Prompt Template for Counterfactual (Backward) Reasoning You are an expert in directed fuzzing and test-case generation

Then, provide the final answer inside [ANSWER] and [/ANSWER] tags as a single JSON object with one key: - executed_lines: JSON list of integers (sorted ascending) for all executable lines that would run. Prompt Template for Counterfactual (Backward) Reasoning You are an expert in directed fuzzing and test-case generation. You are given a Python program in...
[7]

Put your reasoning inside <REASONING></REASONING> tags

First, analyze the program and reason about what inputs would reach line{priority_line}. Put your reasoning inside <REASONING></REASONING> tags
[8]

Figure 6: Prompt templates for: (top) execution, and (bottom) counterfactual reasoning in DEXBENCH

Then, provide the final answer inside [ANSWER] and [/ANSWER] tags with **only** the comma-separated argument values (for example:`["a","bb"], 0.5`). Figure 6: Prompt templates for: (top) execution, and (bottom) counterfactual reasoning in DEXBENCH. 1import unittest 2 3def f(text, value): 4ls = list(text) 5if (ls.count(value)) % 2 == 0: 6while value in ls:...
[9]

Convert text to list
[10]

Check if count ofvalueis even
[11]

If even: enter while loop removing all instances
[12]

The loop runs only if the count of value is even, and value exists in the list

If odd: clear the list. The loop runs only if the count of value is even, and value exists in the list. In the given test case, ‘m’ appears 0 times (even), but is not in the list, so the loop does not execute. To trigger execution, choose a character with even count>0. From the string: •‘a’appears 2 times •‘b’appears 4 times •‘e’appears 2 times •‘u’appear...
[13]

The functionf(text)‘ takes a text parameter
[14]

It splits the text into lines usingtext.splitlines()
[15]

It iterates through each line in a for loop (line 5)
[16]

Line 6 is the conditional:if line == ‘’
[17]

If this condition is true (line is empty), it breaks out of the loop
[18]

To reach line 6, I need the function to be called with text that, when split into lines, produces at least one line to iterate over the for loop

Otherwise, it processes the line and appends to created. To reach line 6, I need the function to be called with text that, when split into lines, produces at least one line to iterate over the for loop. Looking at the current test case:f(‘A(hiccup)A’) •‘A(hiccup)A’.splitlines()returns‘A(hiccup)A’(a list with one element) • The for loop will iterate once w...