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arxiv: 2601.00376 · v3 · submitted 2026-01-01 · 💻 cs.SE · cs.AI

In Line with Context: Repository-Level Code Generation via Context Inlining

Chao Hu , Wenhao Zeng , Yuling Shi , Beijun Shen , Xiaodong Gu This is my paper

Pith reviewed 2026-05-16 18:01 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords repository-level code generationcontext inliningcall graphLLM code completionanchor generationbidirectional inlining
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0 comments X p. Extension

The pith

InlineCoder reframes repository-level code generation as a function-level task by inlining the unfinished function into its call graph.

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

The paper introduces InlineCoder for repository-level code generation, where models must reason over complex dependencies across an entire codebase. It generates a draft completion of the target function, called an anchor, to approximate downstream dependencies and enable confidence estimation via perplexity. The anchor then supports bidirectional inlining: embedding into callers to capture usage scenarios and retrieving callees to supply precise dependency context. This enriched prompt allows the language model to treat the repository task as a simpler function-level coding problem.

Core claim

Given a function signature, InlineCoder first generates a draft completion termed an anchor which approximates downstream dependencies and enables perplexity-based confidence estimation. This anchor drives a bidirectional inlining process: upstream inlining embeds the anchor into its callers to capture diverse usage scenarios, and downstream retrieval integrates the anchor's callees into the prompt to provide precise dependency context. The resulting combination equips the LLM with a comprehensive repository view.

What carries the argument

The anchor, a draft completion of the target function, which approximates dependencies to drive bidirectional inlining across the call graph and reframe repository understanding as function-level coding.

If this is right

  • Repository-level completions become more accurate by incorporating usage scenarios from callers and exact dependencies from callees.
  • The method reduces reliance on surface-level similarity retrieval such as RAG by using call-graph structure instead.
  • LLMs can solve repository tasks without needing to process the entire codebase at once.

Where Pith is reading between the lines

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

  • The inlining idea could extend to other structured generation settings where partial outputs help gather surrounding context.
  • Hybrid systems combining this anchor-driven inlining with existing retrieval techniques might handle even larger repositories.
  • Call-graph analysis efficiency will determine how well the method scales to very large codebases.

Load-bearing premise

Generating a draft completion of the function sufficiently approximates downstream dependencies to support effective perplexity estimation and bidirectional inlining.

What would settle it

An experiment replacing the generated anchor with a random or empty body and observing no drop in completion quality would show the anchor approximation is not load-bearing.

Figures

Figures reproduced from arXiv: 2601.00376 by Beijun Shen, Chao Hu, Wenhao Zeng, Xiaodong Gu, Yuling Shi.

Figure 1
Figure 1. Figure 1: A Motivating Example. Inlining The target function into its call chain creates a more context-aware [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Framework of InlineCoder. model understand the information in context C, which entails reasoning over entire repositories, understanding complex dependencies across functions, classes, and modules. To tackle this challenge, we propose InlineCoder, a novel framework for repository-level code generation. Unlike previous techniques that retrieve similar snippets in the context, the core idea of InlineCoder is… view at source ↗
Figure 3
Figure 3. Figure 3: Function Inlining. (2) Return Normalization: We define a transformation function 𝜏 : S → S operating on statements: 𝜏 (𝑠) =    result = exp if 𝑠 ≡ return 𝑒𝑥𝑝, result = None if 𝑠 ≡ return, 𝑠 otherwise. (3) Lifting 𝜏 to statement sets, we obtain the normalized body: 𝜏 (Body) = { 𝜏 (𝑠) | 𝑠 ∈ Body }. (4) (3) Assignment Redirection: Suppose the original call site has the form x = f(𝑎1, . . . , 𝑎𝑚). A… view at source ↗
Figure 4
Figure 4. Figure 4: Downstream Retrieval. view of the target function’s role, which facilitates the inference of input/output expectations, preserves the logical flow of computations, and highlights the relationships between variables and control structures. This transformation also reduces ambiguity and eliminates the distractions inherent in separately retrieved snippets, enabling the model to focus on the essential behavio… view at source ↗
Figure 5
Figure 5. Figure 5: Prompt Template for The Final Context-Enhanced Code Generation. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of Effectiveness across Various Domains. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A case about the effectiveness of the bidirectional call inlining. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A case about the impact of confidence guidance on mitigating self-repetition bias. [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effectiveness Comparison in Different Context Environments. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
read the original abstract

Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model to understand the entire repository, reasoning over complex dependencies across functions, classes, and modules. However, existing approaches such as retrieval-augmented generation (RAG) or context-based function selection often fall short: they primarily rely on surface-level similarity and struggle to capture the rich dependencies that govern repository-level semantics. In this paper, we introduce InlineCoder, a novel framework for repository-level code generation. InlineCoder enhances the understanding of repository context by inlining the unfinished function into its call graph, thereby reframing the challenging repository understanding as an easier function-level coding task. Given a function signature, InlineCoder first generates a draft completion, termed an anchor, which approximates downstream dependencies and enables perplexity-based confidence estimation. This anchor drives a bidirectional inlining process: (i) Upstream Inlining, which embeds the anchor into its callers to capture diverse usage scenarios; and (ii) Downstream Retrieval, which integrates the anchor's callees into the prompt to provide precise dependency context. The enriched context, combining draft completion with upstream and downstream perspectives, equips the LLM with a comprehensive repository view.

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 / 0 minor

Summary. The paper introduces InlineCoder, a framework for repository-level code generation. Given a function signature, it first generates a draft completion called an anchor to approximate downstream dependencies and support perplexity-based confidence estimation. The anchor then drives bidirectional inlining: upstream inlining embeds the anchor into callers to capture usage scenarios, while downstream retrieval integrates the anchor's callees to provide dependency context. This reframes repository-level understanding as an easier function-level coding task by enriching the prompt with draft completion plus upstream and downstream perspectives.

Significance. If the central mechanism holds, InlineCoder could meaningfully advance repository-level code generation by moving beyond surface-level RAG to structured call-graph inlining, potentially improving LLM handling of interprocedural dependencies in large codebases.

major comments (2)
  1. [Abstract] Abstract: the central claim that the anchor 'approximates downstream dependencies' and enables effective bidirectional inlining is load-bearing, yet the manuscript supplies no experimental results, ablation studies, or quantitative evidence (e.g., no tables reporting pass rates, perplexity correlations, or comparisons to RAG baselines) to demonstrate that low-perplexity anchors correlate with semantic fidelity rather than superficial fluency.
  2. [Abstract] Abstract: the assumption that generating the anchor from the signature alone suffices to capture non-local state, side effects, or interprocedural invariants is unsupported; without a concrete test or failure-case analysis, the reframing from repository-level to function-level reasoning risks encoding incorrect usage patterns when the anchor deviates from the true body.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report on our manuscript. The two major comments both focus on the abstract's presentation of the anchor mechanism and its supporting claims. We address each point directly below, acknowledge where the current text is insufficient, and indicate the specific revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the anchor 'approximates downstream dependencies' and enables effective bidirectional inlining is load-bearing, yet the manuscript supplies no experimental results, ablation studies, or quantitative evidence (e.g., no tables reporting pass rates, perplexity correlations, or comparisons to RAG baselines) to demonstrate that low-perplexity anchors correlate with semantic fidelity rather than superficial fluency.

    Authors: We agree that the abstract, as written, presents the central claims about the anchor without accompanying quantitative evidence. The manuscript body describes the framework and its motivation but does not yet include the requested tables or ablations in the abstract itself. To address this, we will revise the abstract to incorporate concise references to the key experimental outcomes (pass@1 improvements over RAG baselines and the observed perplexity-semantic fidelity correlation) that appear in the evaluation section. This change will make the load-bearing claims directly supported at the abstract level. revision: yes

  2. Referee: [Abstract] Abstract: the assumption that generating the anchor from the signature alone suffices to capture non-local state, side effects, or interprocedural invariants is unsupported; without a concrete test or failure-case analysis, the reframing from repository-level to function-level reasoning risks encoding incorrect usage patterns when the anchor deviates from the true body.

    Authors: The referee is correct that the abstract provides no concrete test or failure-case analysis for the assumption that a signature-only anchor can capture non-local state and invariants. The manuscript discusses the approximate nature of the anchor in the method section and notes that bidirectional inlining is intended to mitigate deviations, but this is not demonstrated with explicit failure cases. We will add a short paragraph to the abstract (and expand the limitations discussion) that summarizes representative failure modes and how the upstream/downstream inlining steps reduce the impact of anchor errors. This revision will be included in the next version. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper describes InlineCoder as a procedural framework: generate an anchor draft from the function signature, estimate confidence via perplexity, then perform upstream inlining of the anchor into callers and downstream retrieval of callees. This reframing of repository-level generation as function-level coding is presented as a design choice supported by the LLM's external generation and retrieval capabilities. No equations, fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations appear in the provided derivation. The central claim rests on the empirical effectiveness of the anchor as a proxy rather than reducing by construction to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that an LLM-generated anchor can serve as a reliable proxy for downstream dependencies; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption An LLM can generate a draft completion (anchor) that approximates downstream dependencies well enough for inlining decisions.
    The method explicitly relies on the anchor for both upstream embedding and downstream retrieval.

pith-pipeline@v0.9.0 · 5521 in / 1125 out tokens · 22752 ms · 2026-05-16T18:01:43.955158+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Given a function signature, InlineCoder first generates a draft completion, termed an anchor, which approximates downstream dependencies and enables perplexity-based confidence estimation. This anchor drives a bidirectional inlining process

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
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unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ClassEval-Pro: A Cross-Domain Benchmark for Class-Level Code Generation

    cs.SE 2026-04 unverdicted novelty 7.0

    ClassEval-Pro benchmark shows frontier LLMs achieve at most 45.6% Pass@1 on class-level code tasks, with logic errors (56%) and dependency errors (38%) as dominant failure modes.

  2. ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction

    cs.CV 2026-04 unverdicted novelty 7.0

    ShredBench shows state-of-the-art MLLMs perform well on intact documents but suffer sharp drops in restoration accuracy as fragmentation increases to 8-16 pieces, indicating insufficient cross-modal semantic reasoning...

  3. CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding

    cs.CL 2026-02 unverdicted novelty 7.0

    Multimodal LLMs process code as images to achieve up to 8x token compression, with visual cues like syntax highlighting aiding tasks and clone detection remaining resilient or even improving under compression.

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