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REVIEW 5 major objections 5 minor 6 cited by

AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Fine-tuning on AgentAlign data raises refusal of harmful agentic requests by 35.8 to 79.5 percentage points while preserving helpfulness.

desk verdict A genuinely new data-synthesis pipeline for agentic safety alignment with large, consistent refusal gains, but a missing text-only control leaves the mechanism underdetermined. read the letter →

arxiv 2505.23020 v1 pith:26DI5GVG submitted 2025-05-29 cs.CR cs.AIcs.CL

Jinchuan Zhang , Lu Yin , Yan Zhou , Songlin Hu This is my paper
classification cs.CRcs.AIcs.CL
keywords agentsafetyalignmentLLMagentsdatasynthesisabstractbehaviorchainssimulatedenvironmentstooluserefusalrateHarm
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that the safety gap of LLM agents can be closed by aligning them on synthetic data that mirrors real agentic workflows: multi-step, tool-using tasks. It proposes AgentAlign, which turns abstract behavior chains into concrete executable instructions in a simulated environment with 7,485 tool instances, and pairs harmful instructions with a proportional set of benign ones from the same chains. Fine-tuning three open models on this data raised their refusal rate on the AgentHarm benchmark by 35.8 to 79.5 percentage points, with benign-task scores roughly stable or better. If true, this gives a low-cost, data-only route to agent safety alignment.

What carries the argument

The load-bearing device is the abstract behavior chain: a sequence of abstract tool capabilities (e.g., web_search, manage_files, send_sms) that describes a harmful activity pattern independently of any concrete API. AgentAlign builds 240 such chains from a harmful taxonomy, instantiates each chain in a simulated environment containing 86 tool types and thousands of concrete tool instances, and uses LLMs to generate natural-language instructions from the instantiated chains. Because the same chain is instantiated both harmfully and benignly, the resulting dataset teaches refusal of the harmful pattern without teaching refusal of the whole action class.

What would settle it

Run the three fine-tuned models on harmful agentic tasks that use the real tools behind the simulation (e.g., actual search engines, git, SSH, file operations) and measure refusal rates; if the gains shrink toward the base-model level, the alignment is an artifact of the sandbox. A cleaner test is to swap in an emulation of real APIs and check whether AgentHarm refusal rates hold.

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Extended reading notes

Core claim

On its own terms, the paper establishes that the willingness of instruction-tuned LLMs to execute harmful tool chains is not a fixed property of the base model: it can be moved dramatically by supervised fine-tuning on appropriately shaped agentic data. The central evidence is on AgentHarm: Ministral-8B-Instruct goes from a 0.0% refusal rate to 79.5%, Qwen-2.5-7B-Instruct from 21.6% to 85.8%, and Functionary-Small-v3.2 from 52.8% to 88.6%, while benign-task scores stay roughly flat or improve (Qwen from 53.4% to 64.2%, Functionary from 45.9% to 53.5%, Ministral from 69.1% to 63.3%). The paper argues this trade-off beats prompting-only interventions and matches or exceeds the safety-utility frontier of commercial models like Claude-3.5-Haiku with fewer false refusals.

Load-bearing premise

The whole result relies on the simulated sandbox tools being close enough to real-world tools that a refusal learned inside the sandbox still fires when the same harmful request is issued against actual APIs and systems.

Editorial extensions

If this is right

  • Fine-tuning on AgentAlign data can replace or complement prompting-based defenses that fail on models with low baseline safety awareness.
  • The approach transfers beyond AgentHarm: aligned models also refuse ToolSword malicious queries, with Ministral reaching 100% refusal.
  • Because the data is synthetic and open-sourced, the method can be re-run for different tool ecosystems and languages without manual annotation at scale.
  • Combining AgentAlign with refusal prompting or ReAct pushes the safety-utility Pareto frontier further.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the simulation-to-real gap is small, this style of alignment could generalize to agentic systems that use real web services and OS-level operations, where the refusal gains would be more consequential.
  • The use of one behavior chain for both harmful and benign instantiations suggests that safety alignment can be framed as learning to condition on intent rather than on surface tool names; this might be testable by probing with rephrased instructions.
  • A likely failure mode to probe: borderline benign instructions (e.g., security testing) may drift toward over-refusal as the benign/harmful ratio changes; the paper's reported false-refusal rates (2.8-12.5%) suggest this trade-off is nontrivial.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes AgentAlign, a framework for synthesizing safety-alignment data for LLM-based agents. The method constructs abstract behavior chains from a harmful taxonomy, instantiates them in a simulated tool environment with diverse concrete tools, filters the resulting instructions through semantic and execution validation, and generates refusal responses (via Claude-3.5-Haiku) for harmful instructions and multi-step trajectories (via Mistral-Large) for benign instructions. Fine-tuning three open-weight agent models (Ministral-8B-Instruct, Qwen-2.5-7B-Instruct, Functionary-Small-v3.2) on the resulting dataset raises refusal rates on AgentHarm by 35.8 to 79.5 percentage points while producing modest changes in benign AgentHarm scores. Ablations on Qwen indicate that benign samples, harmful samples, and third-party data each contribute to the safety-utility balance, and a supplementary evaluation on ToolSword shows improved refusal rates on malicious queries. The dataset and code are released.

Significance. If the central claim holds, AgentAlign would be a practically useful and scalable way to improve agentic safety alignment: it generates executable, multi-step tool instructions without manual annotation at scale, and it appears to transfer across three model families. The paper has several concrete strengths: it evaluates on external benchmarks (AgentHarm and ToolSword) rather than only on self-generated data; the LLM judge used for refusal detection is validated against 100 human-annotated outputs (Appendix B); ablations isolate the contribution of each dataset component; and the dataset and code are open-sourced. The human evaluation of instruction quality (93% majority-pass, Fleiss' kappa 0.738) is also a useful quality check. However, the strength of the central claim is currently tempered by the absence of data-centric baselines and transfer controls, and by selection details around the pilot-tuned data ratio and intermediate checkpoints. These are addressable with additional experiments rather than being fundamental flaws.

major comments (5)
  1. [§4.1, Table 1] The evaluation compares AgentAlign only against prompting baselines (CoT, ReAct, Refusal Prompt); there is no data-centric baseline such as SFT on an existing agentic safety dataset (e.g., ToolAlign), SFT on a text-only harmful/benign instruction set, or SFT on refusal-response pairs without tool grounding. Consequently, the contribution of the behavior-chain and simulated-environment design is not isolated from the contribution of simply training on refusal pairs. I request at least one control that removes the agentic/tool component while keeping instruction content and training scale comparable; the public release makes such a control runnable.
  2. [§3.2–3.3, §4.2] The training instructions from AgentAlign and the test instructions from AgentHarm share overlapping harm taxonomies and the same synthetic-tool idiom, so the large refusal-rate gains (35.8–79.5 percentage points in Table 1) could in principle reflect surface-level associations with category names, tool names, or the refusal style of Claude-3.5-Haiku rather than transferable recognition of harmful intent in novel agentic requests. I ask for a control that perturbs tool names or paraphrases AgentHarm instructions, or for an evaluation on a held-out set of behavior chains built from a disjoint taxonomy, in order to rule out this surface-matching explanation. Without such a control, the claim that AgentAlign teaches generalizable agentic refusal is not fully supported.
  3. [§3.5, Appendix C] The final harmful-to-benign data ratio is chosen 'based on pilot experiments,' and the Qwen training description says intermediate checkpoints were saved 'for comparison and evaluation.' The paper does not report the pilot evaluation set, the candidate ratios, the number of checkpoints inspected, or whether AgentHarm or ToolSword were consulted during this selection. Because the headline results are the main evidence for the method, the authors should specify these details and, ideally, demonstrate that the reported numbers survive evaluation on a split that was not used for selection.
  4. [§4.2, Table 1] The utility claim—'minimally impacting or even positively enhancing their helpfulness'—is supported only by AgentHarm's benign tool-use tasks, where benign Score changes range from -5.8 to +8.1 points depending on the model. This is a narrow basis for a general helpfulness claim, and it leaves open the possibility of capability regression outside the tool-use setting. I recommend either adding standard helpfulness or general-capability evaluations (e.g., instruction following, general knowledge, or a separate tool-use benchmark) or narrowing the claim to benign performance on AgentHarm-style agentic tasks.
  5. [Appendix F, Table 4] The generalization evaluation on ToolSword reports only refusal rates on the Malicious Queries split and does not report benign-task scores or non-refusal scores. The authors themselves note that ToolSword's queries are predominantly tool-augmented harmful information-seeking requests rather than multi-step agentic write operations, so this result is limited evidence for transfer to other agentic benchmarks. At minimum, the authors should report the full ToolSword metrics and frame the result as a partial generalization check rather than a general demonstration of transfer.
minor comments (5)
  1. [Abstract and §4.2] The phrase '35.8% to 79.5% improvement' is ambiguous; these are percentage-point increases in refusal rate, and the text should say so explicitly.
  2. [Table 2] The 'Refusal' column for benign requests is a false-refusal rate; renaming it 'False Refusal Rate' would avoid confusion with the harmful-request refusal column.
  3. [Appendix C] In the Qwen-2.5-7B-Instruct paragraph, 'one complete epocht' contains a typo and should read 'one complete epoch.'
  4. [Tables 11 and 12] The headings read 'Smantic Validation Prompt'; this should be 'Semantic Validation Prompt.'
  5. [§4.4] The section title 'Orthogonal Results' is nonstandard; a title such as 'Combination with Prompting Methods' would more accurately describe the content.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: central safety improvements are measured on external benchmarks (AgentHarm, ToolSword); only a minor, non-load-bearing self-citation is present.

full rationale

The paper's central claim is that fine-tuning on AgentAlign data improves refusal of harmful agentic requests, and this is evaluated on AgentHarm and ToolSword, both externally constructed benchmarks with independent instructions, tools, and scoring rubrics. The training pipeline (abstract behavior chains, simulated tools, semantic validation, response generation) produces labels and trajectories, but none of these outputs is fed back as the evaluation metric: the refusal judge was validated against 100 human-annotated outputs (Appendix B), and the reported gains are measured on benchmark tasks, not on the synthesized training set. The only self-citation in the construction is the harmful taxonomy from Zhang et al. (2024a), used as an input for generating training instructions; this is not load-bearing because the evaluation benchmarks were not built from that taxonomy and the results do not reduce to it. The pilot-selected data ratio and intermediate-checkpoint selection are model-selection choices that can affect the magnitude of results, but they are not fitted parameters renamed as predictions, and no equation in the paper makes the evaluation score equal to the training objective. The use of LLM judges in both data filtering and evaluation does not create a self-referential loop, since the evaluation judge was chosen through human annotation and is not the model being trained. Accordingly, no circular step is present; the score reflects only the presence of a minor, non-load-bearing self-citation.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central result rests on several domain assumptions about the fidelity of simulated environments and benchmarks, and on a data-ratio tuning choice; no new physical or conceptual entities are introduced beyond the framework itself.

free parameters (1)
  • harmful:benign data ratio = not specified numerically; chosen via pilot experiments
    The proportion of harmful to benign training examples was tuned via pilot experiments to balance safety and utility (Section 3.5); this ratio directly affects the reported safety-utility trade-off.
assumptions (5)
  • domain assumption Abstract behavior chains over a fixed action space capture common patterns of harmful agentic activities.
    The framework relies on 240 curated chains as sufficient coverage for aligning agents against real-world misuse (Section 3.2).
  • domain assumption Simulated tool implementations behave like real-world APIs for safety alignment purposes.
    A custom sandbox was built because RapidAPI data was low quality; fidelity to real tools is asserted, with discrepancies acknowledged in Limitations.
  • domain assumption AgentHarm and ToolSword are valid proxies for real-world agentic harm.
    Safety is measured on these synthetic-tool benchmarks; generalizability to other environments is assumed (Section 4.1, Appendix F).
  • domain assumption LLM-based semantic validation and refusal detection are sufficiently accurate.
    Qwen-2.5-72B-Instruct reached 0.993 accuracy on 100 human-annotated outputs, a small validation sample (Appendix B).
  • domain assumption Claude-3.5-Haiku refusal responses are appropriate training targets.
    Harmful instructions are paired with Claude-generated refusals, encoding a particular refusal style (Section 3.5).

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Cite this review

Pith. "Pith review of AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models." pith.science (2026). https://pith.science/paper/26DI5GVG

@misc{pith2026250523020,
  author       = {Pith},
  title        = {Pith review of: AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/26DI5GVG}},
  note         = {Machine review of arXiv:2505.23020}
}
read the original abstract

The acquisition of agentic capabilities has transformed LLMs from "knowledge providers" to "action executors", a trend that while expanding LLMs' capability boundaries, significantly increases their susceptibility to malicious use. Previous work has shown that current LLM-based agents execute numerous malicious tasks even without being attacked, indicating a deficiency in agentic use safety alignment during the post-training phase. To address this gap, we propose AgentAlign, a novel framework that leverages abstract behavior chains as a medium for safety alignment data synthesis. By instantiating these behavior chains in simulated environments with diverse tool instances, our framework enables the generation of highly authentic and executable instructions while capturing complex multi-step dynamics. The framework further ensures model utility by proportionally synthesizing benign instructions through non-malicious interpretations of behavior chains, precisely calibrating the boundary between helpfulness and harmlessness. Evaluation results on AgentHarm demonstrate that fine-tuning three families of open-source models using our method substantially improves their safety (35.8% to 79.5% improvement) while minimally impacting or even positively enhancing their helpfulness, outperforming various prompting methods. The dataset and code have both been open-sourced.

Figures

Figures reproduced from arXiv: 2505.23020 by the authors.

Figure 1
Figure 1. An actual multi-step execution trajectory by [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Visualization of the distribution of two [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the AgentAlign framework illustrating the workflow of high-quality alignment data generation, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Distribution of harmful request categories (left) and statistics of the AgentAlign dataset (right). [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Ablation analysis of different training data components’ impact on model performance in harmful and [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Human evaluation results on sampled instruc [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Examples of synthesized abstract behavior chains, consisting of action sequences (steps) composed of [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Example tool schema in our simulated environment, including clear tool descriptions, parameter specifica [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Example of tool implementation within the simulated environment, comprehensively mimicking real-world [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Comparison of harmful instructions and tools between ToolAlign and AgentAlign reveals their distinct [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]
Figure 11
Figure 11. Figure 11: An example of multi-step execution trajectory for harmful tasks on AgentHarm by Ministral-8B-Instruct [PITH_FULL_IMAGE:figures/full_fig_p027_11.png]
Figure 12
Figure 12. Figure 12: An example of correct identification and refusal of harmful tasks on AgentHarm by Ministral-8B-Instruct [PITH_FULL_IMAGE:figures/full_fig_p028_12.png]
Figure 13
Figure 13. Figure 13: The refusal rate of different models and methods (Base Model, Refusal Prompt, and AgentAlign) across [PITH_FULL_IMAGE:figures/full_fig_p029_13.png]

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Forward citations

Cited by 6 Pith papers

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

  1. Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Schema-formatted tool specifications degrade refusal in LLM agents; using flattened textual specifications during safety judgment substantially restores refusal and blocks prompt injection.

  2. $S^3$: Improving Agent Safety through Multi-Stage Defense

    cs.CR 2026-08 conditional novelty 6.0 of 10

    S3 composes stage-specific safety skills through a guard agent, achieving near-zero attack success on six risk types in its own benchmark while preserving benign task completion.

  3. SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety

    cs.CR 2026-05 unverdicted novelty 6.0 of 10

    SafeHarbor uses hierarchical memory with adversarial rule extraction and entropy-driven self-evolution to achieve over 93% refusal on harmful requests while reaching 63.6% benign utility on GPT-4o.

  4. S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner

    cs.AI 2025-08 conditional novelty 5.0 of 10

    S3LoRA prunes LoRA layers with the sharpest spectral update concentration to improve safety in fine-tuned LLM agents without needing base models or extra data.

  5. Agent Safety Alignment via Reinforcement Learning

    cs.AI 2025-07 reject novelty 5.0 of 10

    RL-based safety alignment with an execute-refuse-verify policy improves reported threat resistance for tool-using agents, but utility preservation is not consistently demonstrated.

  6. A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents

    cs.AI 2025-06 conditional novelty 4.0 of 10

    The paper surveys security risks of LLM agents, organizes them into a five-level autonomy taxonomy, and proposes an untested CMDP-based architecture called R2A2.

Reference graph

Works this paper leans on

22 extracted references · 20 canonical work pages · cited by 6 Pith papers

  1. [1]

    Goal & Requirements - What is the ultimate harmful objective? - What capabilities and resources are needed?

    work page

  2. [2]

    Tool Selection & Flow - Which tools can provide these capabilities? - How should they be sequenced for maximum effect? - What dependencies exist between steps?

    work page

  3. [3]

    Technical Validation - Is the pattern complete and feasible? - Are all tool combinations logical? - Does the data/information flow make sense? [Requirements]

    work page

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    Answer: HARMFUL

    What specific aspects make this harmful or benign? Then provide your final answer as "Answer: HARMFUL" or "Answer: BENIGN". <instruction>{instruction}</instruction> Table 11: Prompt template for semantic validation of benign instructions, where the instruction to be analyzed is provided within XML tags. Smantic Validation Prompt for Harmful Instructions A...

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    For each subcategory: - Generate 2-3 distinct tool chains that could be used for harmful purposes - Each chain should contain 1-5 tools that logically work together - Tool chains can be reused across different subcategories if they serve similar purposes - Only use tools from the provided list, and maintain consistent tool naming - Include a clear explana...

    work page

  6. [7]

    Consider: - Sequential dependencies between tools - Data/information flow between steps - Technical feasibility of each step - Real-world applicability - Common patterns in similar harmful activities

    work page

  7. [8]

    misinformation

    Output Format: {output_format} [Examples] Here are some example patterns for different categories: {examples} Now, analyze the provided category and generate a comprehensive set of patterns following this format. Table 7: Prompt template for abstract behavior chain construction, where the variables (shown initalics) are replaced with specific tool capabil...

    work page

  8. [9]

    Natural Language and Execution Requirements: - Tool Coverage: All available tools are naturally incorporated - Parameter Completeness: All required parameters of the tools can be inferred from the instruction - Logical Flow: Steps proceed in a coherent sequence - Natural Language: Instructions sound like genuine user requests

    work page

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    instructions

    Diversity Requirements: Generated instructions should vary in: - Complexity: From straightforward to sophisticated approaches - Language style: Different tones and formality levels - Parameter usage: Various valid parameter combinations - Context: Different scenarios while mai...

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    Legitimacy Requirements: - Ethical Use: Instructions must respect privacy, copyright, and other ethical boundaries - Legal Compliance: All generated instructions must describe legal activities

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    instructions

    Diversity Requirements: Generated instructions should vary in: - Application domains: Business, education, personal use, professional development, entertainment, etc. - Complexity: From straightforward to sophisticated approaches - Language style: Different tones and formality...

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    Natural Language and Execution Requirements: - Tool Coverage: All available tools are naturally incorporated - Parameter Completeness: All required parameters of the tools can be inferred from the instruction - Parameter Validity: All parameter values must be realistic and com...

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    Core Requirements: - Contains potentially sensitive elements but maintains clear legitimate purpose - Requests professional knowledge or careful handling - Operates within regulated domains but complies with all rules - Demonstrates clear value creation or problem-solving inte...

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    instructions

    Diversity Requirements: Generated instructions should vary in: - Professional domains: Cover different regulated or sensitive fields - Expertise levels: From general public needs to professional applications - Tool usage patterns: Various legitimate combinations of sensitive o...

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    What is the core intent of the instruction?

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    What would be the immediate and potential downstream impacts?

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    Could this instruction be reasonably interpreted as part of legitimate use?

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    What is the core intention behind this request?

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    Are there any explicit harmful elements in the request?

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    I’m sorry, I can’t assist with that

    What would be the natural outcome of fulfilling this request? Then provide your final answer as either: Answer: HARMFUL (only if clear and explicit harmful intent is demonstrated) Answer: BENIGN (default for normal activities and ambiguous cases) <instruction>{instruction}</in...

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    Preprint, arXiv:2309.07864

    The rise and potential of large language model based agents: A survey. Preprint, arXiv:2309.07864. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhou- jun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Sil...

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    OpenCodeInterpreter: Integrating code gener- ation with execution and refinement. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12834–12859, Bangkok, Thailand. As- sociation for Computational Linguistics. Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui...

    2024 arXiv

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