ARXIV:2603.09951 · NEURAL DEBUGGING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Towards a Neural Debugger for Python

arXiv

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction. However, developers rarely execute programs step by step; instead, they use debuggers to stop execution at certain breakpoints and step…

METHOD

Full abstract

Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreters (FAIR CodeGen Team et al., 2025). However, developers rarely execute programs step by step; instead, they use debuggers to stop execution at certain breakpoints and step through relevant portions only while inspecting or modifying program variables. Existing neural interpreter approaches lack such interactive control. To address this limitation, we introduce neural debuggers: language models that emulate traditional debuggers, supporting operations such as stepping into, over, or out of functions, as well as setting breakpoints at specific source lines. We show that neural debuggers -- obtained via fine-tuning large LLMs or pre-training smaller models from scratch -- can reliably model both forward execution (predicting future states and outputs) and inverse execution (inferring prior states or inputs) conditioned on debugger actions. Evaluated on CruxEval, our models achieve strong performance on both output and input prediction tasks, demonstrating robust conditional execution modeling. Our work takes first steps towards future agentic coding systems in which neural debuggers serve as a world model for simulated debugging environments, providing execution feedback or enabling agents to interact with real debugging tools. This capability lays the foundation for more powerful code generation, program understanding, and automated debugging.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs,…

WHY NOW

Neural Debugging moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainIntroducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.09951 · NEURAL DEBUGGING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Towards a Neural Debugger for Python

arXiv

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Neural Debugging moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainIntroducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.09951

Towards a Neural Debugger for Python

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback29 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Neural Debugging moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction. However, developers rarely execute programs step by step; instead, they use debuggers to stop execution at certain breakpoints and step through relevant portions only while inspecting or modifying program variables.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreters (FAIR CodeGen Team et al., 2025). However, developers rarely execute programs step by step; instead, they use debuggers to stop execution at certain breakpoints and step through relevant portions only while inspecting or modifying program variables.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them into neural interpreters (FAIR CodeGen Team et al., 2025).
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Neural Debugging moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Neural Debugging

Competitors

not indexed

Competitive landscape

Introducing neural debuggers that emulate traditional debugging tools for enhanced code execution and prediction.

Segment

Neural Debugging

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.09951 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(29)

Cyber-Zero: Training Cybersecurity Agents without Runtime

2025Terry Yue Zhuo, Dingmin Wang et al.

SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

2025Yuxiang Wei, Olivier Duchenne et al.

Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations

2025Kunal Handa, Alex Tamkin et al.

What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces

2025Jordi Armengol-Estap'e, Quentin Carbonneaux et al.

Debugging functional programs by interpretation

2024John G. Whitington

What Makes Large Language Models Reason in (Multi-Turn) Code Generation?

2024Kunhao Zheng, Juliette Decugis et al.

RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

2024Jonas Gehring, Kunhao Zheng et al.

Qwen2.5-Coder Technical Report

2024Binyuan Hui, Jian Yang et al.

CodeGemma: Open Code Models Based on Gemma

2024CodeGemma Team Heri Zhao, Jeffrey Hui et al.

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

2024John Yang, Carlos E. Jimenez et al.

NExT: Teaching Large Language Models to Reason about Code Execution

2024Ansong Ni, Miltiadis Allamanis et al.

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

2024Zhihong Shao, Peiyi Wang et al.

DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence

2024Daya Guo, Qihao Zhu et al.

CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

2024Alex Gu, Baptiste Rozière et al.

Code Llama: Open Foundation Models for Code

2023Baptiste Rozière, Jonas Gehring et al.

Llama 2: Open Foundation and Fine-Tuned Chat Models

2023Hugo Touvron, Louis Martin et al.

Code Execution with Pre-trained Language Models

2023Chenxiao Liu, Shuai Lu et al.

Teaching Large Language Models to Self-Debug

2023Xinyun Chen, Maxwell Lin et al.

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions

2022David Bieber, Rishab Goel et al.

Show Your Work: Scratchpads for Intermediate Computation with Language Models

2021Maxwell Nye, Anders Andreassen et al.

Showing 20 of 29 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkDebugLM: Learning Traceable Training Data Provenance for LLMs

7.0

Prior WorkLLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

7.0

Prior WorkA Decompilation-Driven Framework for Malware Detection with Large Language Models

7.0

Prior WorkDryRUN: On the Role of Public Tests in LLM-Driven Code Generation

7.0

Prior WorkExploring different approaches to customize language models for domain-specific text-to-code generation

7.0

Prior WorkIn-Context Reinforcement Learning for Tool Use in Large Language Models

7.0

Extension

Builds On ThisGradient-Based Program Synthesis with Neurally Interpreted Languages

4.0

Builds On ThisRecursive language models for jailbreak detection: a procedural defense for tool-augmented agents

3.0

Builds On ThisLarge Byte Model: Teaching Language Models About Compiled Code

4.0

Commercially relevant

Higher ViabilityReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

9.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.09951
Route: /paper/towards-a-neural-debugger-for-python
Active tab: read
Artifact: towards-a-neural-debugger-for-python

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/towards-a-neural-debugger-for-python/paper-pack
REST build passport API/api/v1/paper/towards-a-neural-debugger-for-python/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/towards-a-neural-debugger-for-python

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Towards a Neural Debugger for Python

Canonical ID towards-a-neural-debugger-for-python | Route /paper/towards-a-neural-debugger-for-python

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/towards-a-neural-debugger-for-python

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Towards a Neural Debugger for Python

/buildability/towards-a-neural-debugger-for-python

Watchwatch

Subject: Towards a Neural Debugger for Python

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/towards-a-neural-debugger-for-python

Paper ref

towards-a-neural-debugger-for-python

arXiv id

2603.09951

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

8f4f84469f183c6863c796f6cb742407237f9b1791c0f489f05b000dc4c1d082

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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          "item": "https://sciencetostartup.com/paper/towards-a-neural-debugger-for-python"
        }
      ]
    }
  ]
}

Towards a Neural Debugger for Python

Towards a Neural Debugger for Python

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