Datasets:

beatsprom
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ai-code-generation-swe-agents-2026

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2605.18401v2	SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution	2026-05-18T13:44:19Z	[ "cs.CL", "cs.AI" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes Agent Skills offer a structured artifact for combining procedural guidance, executable res... to enhance autonomous code synthesis, achieving Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improve....	Hongyi Liu	7	[ "Hongyi Liu", "Haoyan Yang", "Tao Jiang", "Bo Tang", "Feiyu Xiong", "Yuyu Luo", "Zhiyu Li" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2605.18401v2	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	140.86	Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries. Yet open skill ecosystems contain redundant, uneven, e...	[ -0.05816999822854996, -0.06106400117278099, -0.08495599776506424, 0.004093999974429607, 0.0069519998505711555, 0.04622599855065346, -0.02043999917805195, -0.03943600133061409, -0.045768000185489655, 0.00534099992364645, -0.008522000163793564, 0.04272200167179108, -0.005563999991863966, 0.1...	[ -0.020181000232696533, -0.08380500227212906, -0.040803998708724976, 0.021090000867843628, 0.12466099858283997, -0.03423300012946129, -0.038763001561164856, -0.03721199929714203, -0.01526699960231781, -0.024966999888420105, -0.05469899997115135, -0.04503199830651283, 0.025644000619649887, 0...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Autonomous Repo-Level SWE Agent (SWE-bench)	[ "Python" ]	GPT-4o / Codex Specialized Coding LLM	[ "SWE-bench (Autonomous Software Engineering)" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "SWE-bench (Autonomous Software Engineering)", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2605.25430v1", "title": "CODESKILL: Learning Self-Evolving Skills for Coding Agents", "cosine_sim": 0.685 }, { "paper_id": "2506.11425v2", "title": "Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Re", "cosine_sim": 0.6537 }, { "paper_...	No public code repository attached. Paper provides formal program synthesis specification.	Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern.	Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries.	Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improves agent performance on challenging agentic coding benchmarks.	1	Autonomous Repo-Level Software Engineering Agents (SWE-bench)	Explosive (>50/mo)	232	2026-08-21T14:36:55.066395
2604.05278v1	Spec Kit Agents: Context-Grounded Agentic Workflows	2026-04-07T00:26:49Z	[ "cs.SE", "cs.AI", "cs.MA" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-... to enhance autonomous code synthesis, achieving We further evaluate the framework on SWE-bench Lite, where augmentation hooks im....	Pardis Taghavi	2	[ "Pardis Taghavi", "Santosh Bhavani" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2604.05278v1	VERIFIED_LIVE	https://github.com/github/spec-kit	[ "https://github.com/github/spec-kit" ]	131,000	0	2026-08-21	Unspecified	136.91	Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-lev...	[ -0.017906000837683678, -0.0004659999976865947, -0.03569300100207329, -0.062470000237226486, 0.0623060017824173, -0.09127400070428848, 0.07774300128221512, 0.019391000270843506, -0.06134599819779396, -0.023907000198960304, -0.034935999661684036, -0.10392600297927856, -0.0004290000069886446, ...	[ -0.040824998170137405, -0.03677999973297119, -0.015138999558985233, -0.002188999904319644, 0.037714000791311264, -0.09035100042819977, -0.03119100071489811, 0.004739999771118164, -0.03911599889397621, -0.02028699964284897, -0.1005370020866394, -0.1059970036149025, 0.01862500049173832, 0.01...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Autonomous Repo-Level SWE Agent (SWE-bench)	[ "Multi-Language / Polyglot" ]	Specialized Autoregressive Code Transformer	[ "SWE-bench (Autonomous Software Engineering)" ]	[ "achieving 58.2" ]	[ { "benchmark_name": "SWE-bench (Autonomous Software Engineering)", "metric": "pass@1 / resolved", "score": "achieving 58.2" } ]	[ { "paper_id": "2605.15226v1", "title": "Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoeni", "cosine_sim": 0.7629 }, { "paper_id": "2510.23761v2", "title": "TDFlow: Agentic Workflows for Test Driven Development", "cosine_sim": 0.751 }, { "paper_id": ...	git clone https://github.com/github/spec-kit && cd spec-kit && (pip install -e . \|\| pip install -r requirements.txt)	Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated APIs and architectural violations.	We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks.	We further evaluate the framework on SWE-bench Lite, where augmentation hooks improve baseline by 1.7 percent, achieving 58.2 percent Pass@1.	8	Code Foundation Models & Specialized Instruction Distillation	Explosive (>50/mo)	232	2026-08-21T14:37:51.079701
2607.01929v1	Beyond Textual Repository Exploration: Dual-Modal Structural Reasoning for Agentic Issue Resolution	2026-07-02T09:23:48Z	[ "cs.SE" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	80	Enterprise Safe (Commercial Training & Deployment Allowed)	ArXiv Standard Distribution; Permissive Open-Source Software (MIT)	1	Proposes As issue-resolution agents traverse repositories through fragmented textual observations,... to enhance autonomous code synthesis, achieving Further ablation studies demonstrate that the gains arise not only from textual....	Jiayi Zhang	4	[ "Jiayi Zhang", "Kai Huang", "Yang Liu", "Chunyang Chen" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2607.01929v1	VERIFIED_LIVE	https://github.com/colbymchenry/codegraph	[ "https://github.com/colbymchenry/codegraph" ]	67,528	4,285	2026-08-20	MIT	134.59	Recent advances in agentic program repair have significantly improved issue resolution by enabling iterative repository exploration. However, existing approaches predominantly rely on sequential, text-based code navigation, which fundamentally limits their ability to reason over large-scale long-horizon repositories wi...	[ 0.012072999961674213, -0.0015310000162571669, 0.010522999800741673, 0.036600999534130096, 0.0860380008816719, 0.00418699998408556, 0.030119000002741814, 0.017093999311327934, 0.027442000806331635, 0.05183300003409386, -0.017961999401450157, -0.03453800082206726, 0.039413001388311386, 0.104...	[ -0.048778001219034195, -0.06391800194978714, -0.01829499937593937, 0.028967000544071198, 0.04979899898171425, -0.05796299874782562, -0.023615999147295952, 0.00711899995803833, -0.015812000259757042, 0.012107999995350838, -0.043776001781225204, -0.005764000117778778, 0.035597000271081924, 0...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Autonomous Repo-Level SWE Agent (SWE-bench)	[ "Multi-Language / Polyglot" ]	Claude-3.5-Sonnet Agentic SWE Backbone	[ "SWE-bench (Autonomous Software Engineering)" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "SWE-bench (Autonomous Software Engineering)", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2605.03117v2", "title": "ARISE: A Repository-level Graph Representation and Toolset for Agentic Program R", "cosine_sim": 0.7671 }, { "paper_id": "2605.16352v1", "title": "LARGER: Lexically Anchored Repository Graph Exploration and Retrieval", "cosine_sim": 0.7536 }, {...	git clone https://github.com/colbymchenry/codegraph && cd codegraph && (pip install -e . \|\| pip install -r requirements.txt)	Recent advances in agentic program repair have significantly improved issue resolution by enabling iterative repository exploration.	As issue-resolution agents traverse repositories through fragmented textual observations, structural information such as module organization, call relationships, and dependency chains must be repeatedly reconstructed across interaction steps, often leading to exploration drift and incomplete localization.	Further ablation studies demonstrate that the gains arise not only from textual structural information but also from visual externalization of repository dependencies, which better supports long-horizon repository exploration.	8	Code Foundation Models & Specialized Instruction Distillation	Explosive (>50/mo)	232	2026-08-21T14:36:02.079528
2602.22764v1	Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents	2026-02-26T08:54:09Z	[ "cs.SE" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes Recently, LLM-powered code agents have shown remarkable success in resolving complex softw... to enhance autonomous code synthesis, achieving The evaluation shows that RUSTFORGER using Claude-Sonnet-3.7 significantly outpe....	Jiahong Xiang	5	[ "Jiahong Xiang", "Wenxiao He", "Xihua Wang", "Hongliang Tian", "Yuqun Zhang" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2602.22764v1	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	134.25	The Rust programming language presents a steep learning curve and significant coding challenges, making the automation of issue resolution essential for its broader adoption. Recently, LLM-powered code agents have shown remarkable success in resolving complex software engineering tasks, yet their application to Rust ha...	[ -0.05432000011205673, -0.019053999334573746, 0.03193800151348114, 0.03148199990391731, 0.05669400095939636, -0.056724000722169876, -0.05136200040578842, -0.0025319999549537897, -0.044700998812913895, 0.0015030000358819962, -0.04782399907708168, -0.010869000107049942, 0.09177800267934799, -...	[ -0.08384200185537338, -0.029462000355124474, 0.007918000221252441, 0.0595409981906414, 0.05516500025987625, -0.08093400299549103, -0.044084999710321426, 0.026537999510765076, -0.028132999315857887, -0.02605300024151802, -0.10503000020980835, -0.07940799742937088, 0.0939669981598854, -0.000...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Autonomous Repo-Level SWE Agent (SWE-bench)	[ "Rust" ]	Claude-3.5-Sonnet Agentic SWE Backbone	[ "SWE-bench (Autonomous Software Engineering)" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "SWE-bench (Autonomous Software Engineering)", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2606.07297v1", "title": "SWE-Explore: Benchmarking How Coding Agents Explore Repositories", "cosine_sim": 0.755 }, { "paper_id": "2604.11518v1", "title": "From Translation to Superset: Benchmark-Driven Evolution of a Production AI Agen", "cosine_sim": 0.755 }, { "p...	No public code repository attached. Paper provides formal program synthesis specification.	The Rust programming language presents a steep learning curve and significant coding challenges, making the automation of issue resolution essential for its broader adoption.	Recently, LLM-powered code agents have shown remarkable success in resolving complex software engineering tasks, yet their application to Rust has been limited by the absence of a large-scale, repository-level benchmark.	The evaluation shows that RUSTFORGER using Claude-Sonnet-3.7 significantly outperforms all baselines, resolving 28.6% of tasks on Rust-SWE-bench, i.e., a 34.9% improvement over the strongest baseline, and, in aggregate, uniquely solves 46 tasks that no other agent could solve across all adopted advanced LLMs.	8	Code Foundation Models & Specialized Instruction Distillation	Explosive (>50/mo)	232	2026-08-21T14:38:31.236278
2605.09051v1	ParityFuzz: Finding Inconsistencies across Solidity Compilers via Fine-Grained Mutation and Differential Analysis	2026-05-09T16:53:45Z	[ "cs.SE" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes These inconsistencies hinder contract migration, mislead developers during debugging, and... to enhance autonomous code synthesis, achieving It achieves up to 18x higher compilation success rate and 1.8x higher code cover....	Bowei Su	5	[ "Bowei Su", "Mingxi Ye", "Yuhong Na", "Peilin Zheng", "Zibin Zheng" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2605.09051v1	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	124.04	The Solidity smart contract ecosystem has rapidly grown, leading to multiple compilers targeting different blockchain platforms or improving compilation efficiency. Although many compilers aim to be compatible with the primary Solidity compiler (Solc), significant inconsistencies in compilation and execution remain. Th...	[ -0.11066000163555145, -0.0005329999839887023, 0.029309000819921494, 0.005584000144153833, -0.022265000268816948, -0.11751700192689896, -0.04494199901819229, -0.018334999680519104, -0.07810500264167786, -0.05143500119447708, -0.02896299958229065, -0.07300999760627747, -0.008156999945640564, ...	[ -0.08967100083827972, 0.012349000200629234, 0.0017399999778717756, 0.009242000058293343, -0.005783999804407358, -0.09761399775743484, -0.05167999863624573, -0.00254100002348423, -0.08862199634313583, -0.040525998920202255, -0.048374999314546585, -0.11144399642944336, 0.0034680000972002745, ...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Code Translation & Transpilation Agent	[ "Solidity / Smart Contracts" ]	CodeQwen / Qwen2.5-Coder Foundation	[ "HumanEval & SWE-bench Functional Synthesis Benchmark" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "HumanEval & SWE-bench Functional Synthesis Benchmark", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2607.07217v1", "title": "Finding and Understanding Miscompilation Bugs in the Solidity Compiler", "cosine_sim": 0.842 }, { "paper_id": "2607.15762v1", "title": "GapForge: Directed Compiler Fuzzing via Coverage-Gap Analysis", "cosine_sim": 0.6979 }, { "paper_id": "2...	No public code repository attached. Paper provides formal program synthesis specification.	The Solidity smart contract ecosystem has rapidly grown, leading to multiple compilers targeting different blockchain platforms or improving compilation efficiency.	These inconsistencies hinder contract migration, mislead developers during debugging, and may introduce exploitable vulnerabilities, causing financial losses.	It achieves up to 18x higher compilation success rate and 1.8x higher code coverage than state-of-the-art fuzzers.	7	Syntax-Guided AST Modeling & Semantic Code Search	Explosive (>50/mo)	232	2026-08-21T14:37:09.664025
2511.13761v1	What happens when nanochat meets DiLoCo?	2025-11-14T10:30:04Z	[ "cs.DC", "cs.AI", "cs.LG" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes The model trade-offs introduced by this shift remain underexplored, and our goal is to stu... to enhance autonomous code synthesis, achieving DiLoCo achieves stable convergence and competitive loss in pretraining but yield....	Alexander Acker	6	[ "Alexander Acker", "Soeren Becker", "Sasho Nedelkoski", "Dominik Scheinert", "Odej Kao", "Philipp Wiesner" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2511.13761v1	VERIFIED_LIVE	https://github.com/karpathy/nanochat	[ "https://github.com/karpathy/nanochat" ]	57,400	0	2026-08-21	Unspecified	123.98	Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distributed environments. The model trade-offs introduced by this shift remain underexplored, and our goal is to study them. We use the open-source na...	[ 0.0245789997279644, -0.09005799889564514, 0.00871300045400858, 0.03439300134778023, 0.01057399995625019, -0.11997199803590775, -0.0170499999076128, 0.07791300117969513, 0.0061599998734891415, 0.02568100020289421, 0.12115699797868729, -0.024991000071167946, -0.07395900040864944, 0.019495999...	[ -0.014174000360071659, -0.05802199989557266, 0.05764700099825859, 0.047540999948978424, -0.017190000042319298, -0.12149500101804733, -0.07197300344705582, 0.04043800011277199, -0.05829000100493431, 0.0030129998922348022, 0.02053700014948845, -0.05826300010085106, -0.0502999983727932, -0.04...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Neural Program Synthesis & Generation Architecture	[ "Multi-Language / Polyglot" ]	Specialized Autoregressive Code Transformer	[ "HumanEval (Functional Python Correctness)" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "HumanEval (Functional Python Correctness)", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2511.11608v1", "title": "Why Should the Server Do It All?: A Scalable, Versatile, and Model-Agnostic Fram", "cosine_sim": 0.5515 }, { "paper_id": "2602.08676v3", "title": "LLaDA2.1: Speeding Up Text Diffusion via Token Editing", "cosine_sim": 0.5234 }, { "paper_id"...	git clone https://github.com/karpathy/nanochat && cd nanochat && (pip install -e . \|\| pip install -r requirements.txt)	Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distributed environments.	The model trade-offs introduced by this shift remain underexplored, and our goal is to study them.	DiLoCo achieves stable convergence and competitive loss in pretraining but yields worse MMLU, GSM8K, and HumanEval scores after mid-training and SFT.	4	Formal Verification, Theorem Proving & Symbolic Logic	Explosive (>50/mo)	232	2026-08-21T14:39:48.981685
2606.02963v1	KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators	2026-06-01T23:48:55Z	[ "cs.LG" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each wit... to enhance autonomous code synthesis, achieving On NVIDIA B200, KForge achieves a 2.12$\%$ improvement in end-to-end throughput....	Taras Sereda	6	[ "Taras Sereda", "Burak Bartan", "Ankita Nayak", "Tom St. John", "Natalie Serrino", "Zain Asgar" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2606.02963v1	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	119.93	Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct compute and memory profiles. For optimal efficiency, each stage should run on the accelerator best suited to it. This creates a systems chal...	[ -0.03654899820685387, -0.06515999883413315, -0.028748000040650368, 0.03246799856424332, 0.06185000017285347, -0.09104599803686142, -0.0603020004928112, -0.04893599823117256, -0.04920800030231476, -0.0842209979891777, -0.04712500050663948, -0.13733400404453278, -0.009084999561309814, -0.083...	[ -0.04515400156378746, -0.07560399919748306, -0.06313599646091461, 0.04154599830508232, 0.027215000241994858, -0.0899370014667511, -0.03132899850606918, 0.017210999503731728, -0.029625000432133675, -0.07179299741983414, -0.09405100345611572, -0.12672999501228333, 0.01032199990004301, 0.0030...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Neural Program Synthesis & Generation Architecture	[ "Multi-Language / Polyglot" ]	Specialized Autoregressive Code Transformer	[ "HumanEval & SWE-bench Functional Synthesis Benchmark" ]	[ "achieves a 2.12", "achieving a 5.13" ]	[ { "benchmark_name": "HumanEval & SWE-bench Functional Synthesis Benchmark", "metric": "pass@1 / resolved", "score": "achieves a 2.12" } ]	[ { "paper_id": "2511.13274v1", "title": "KForge: Program Synthesis for Diverse AI Hardware Accelerators", "cosine_sim": 0.8613 }, { "paper_id": "2510.16996v1", "title": "STARK: Strategic Team of Agents for Refining Kernels", "cosine_sim": 0.818 }, { "paper_id": "2603.08721v2", ...	No public code repository attached. Paper provides formal program synthesis specification.	Production inference increasingly targets a heterogeneous mix of accelerators.	Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct compute and memory profiles.	On NVIDIA B200, KForge achieves a 2.12$\%$ improvement in end-to-end throughput compared to TensorRT-LLM on the gpt-oss-20b inference speed benchmark.	4	Formal Verification, Theorem Proving & Symbolic Logic	Explosive (>50/mo)	232	2026-08-21T14:36:33.131487
2603.00729v1	Qwen3-Coder-Next Technical Report	2026-02-28T16:25:04Z	[ "cs.CL" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes To achieve this, we perform agentic training through large-scale synthesis of verifiable c... to enhance autonomous code synthesis, achieving Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Co....	Ruisheng Cao	20	[ "Ruisheng Cao", "Mouxiang Chen", "Jiawei Chen", "Zeyu Cui", "Yunlong Feng", "Binyuan Hui", "Yuheng Jing", "Kaixin Li", "Mingze Li", "Junyang Lin", "Zeyao Ma", "Kashun Shum", "Xuwu Wang", "Jinxi Wei", "Jiaxi Yang", "Jiajun Zhang", "Lei Zhang", "Zongmeng Zhang", "Wenting Zhao", "...	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2603.00729v1	VERIFIED_LIVE	https://github.com/QwenLM/Qwen3-Coder	[ "https://github.com/QwenLM/Qwen3-Coder" ]	16,800	0	2026-08-21	Unspecified	117.55	We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters during inference, enabling strong coding capability with efficient inference. In this work, we explore how far strong training recipes can ...	[ -0.1630139946937561, -0.02379699982702732, -0.03865800052881241, -0.042472999542951584, -0.003106999909505248, 0.025072000920772552, -0.027929000556468964, 0.00444599986076355, -0.011842000298202038, 0.05302400141954422, 0.07548099756240845, -0.04769299924373627, 0.03791100159287453, 0.016...	[ -0.08927900344133377, -0.0901539996266365, -0.04215899854898453, 0.01765899918973446, 0.01207599975168705, -0.011757000349462032, -0.04595300182700157, -0.04198500141501427, -0.00380500010214746, -0.0008329999982379377, -0.03073900006711483, -0.11849399656057358, 0.05341099947690964, 0.030...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Autonomous Repo-Level SWE Agent (SWE-bench)	[ "Multi-Language / Polyglot" ]	Claude-3.5-Sonnet Agentic SWE Backbone	[ "SWE-bench (Autonomous Software Engineering)" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "SWE-bench (Autonomous Software Engineering)", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2506.03136v2", "title": "Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning", "cosine_sim": 0.7544 }, { "paper_id": "2607.05471v1", "title": "KAT-Coder-V2.5 Technical Report", "cosine_sim": 0.7481 }, { "paper_id": "2608.17393v1", "title": "LEGO-RL...	git clone https://github.com/QwenLM/Qwen3-Coder && cd Qwen3-Coder && (pip install -e . \|\| pip install -r requirements.txt)	We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents.	To achieve this, we perform agentic training through large-scale synthesis of verifiable coding tasks paired with executable environments, allowing learning directly from environment feedback via mid-training and reinforcement learning.	Across agent-centric benchmarks including SWE-Bench and Terminal-Bench, Qwen3-Coder-Next achieves competitive performance relative to its active parameter count.	5	Interactive Coding Assistants & Human-AI Pair Programming	Explosive (>50/mo)	232	2026-08-21T14:38:28.350988
2603.06107v1	Real-World Fault Detection for C-Extended Python Projects with Automated Unit Test Generation	2026-03-06T10:05:29Z	[ "cs.SE" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	Proposes To overcome this problem, we propose separating the generation and execution stages of the... to enhance autonomous code synthesis, achieving Subprocess-execution allowed automated testing of up to 56.5% more modules and d....	Lucas Berg	7	[ "Lucas Berg", "Lukas Krodinger", "Stephan Lukasczyk", "Annibale Panichella", "Gordon Fraser", "Wim Vanhoof", "Xavier Devroey" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2603.06107v1	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	116.92	Many popular Python libraries use C-extensions for performance-critical operations allowing users to combine the best of the two worlds: The simplicity and versatility of Python and the performance of C. A drawback of this approach is that exceptions raised in C can bypass Python's exception handling and cause the enti...	[ -0.09296099841594696, -0.01591699942946434, -0.021114999428391457, 0.07172200083732605, 0.035085998475551605, -0.13524700701236725, -0.07286900281906128, 0.02332100085914135, -0.06964100152254105, -0.0204050000756979, -0.004652000032365322, -0.08622299879789352, 0.03316900134086609, -0.011...	[ -0.10700400173664093, 0.029191000387072563, -0.039521001279354095, 0.05558300018310547, 0.008333999663591385, -0.1200999990105629, -0.07753700017929077, 0.03271099925041199, -0.04572699964046478, -0.023290999233722687, -0.03943299874663353, -0.08316399902105331, 0.04874400049448013, 0.0040...	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Neural Program Synthesis & Generation Architecture	[ "Python", "Java / Kotlin" ]	Specialized Autoregressive Code Transformer	[ "HumanEval & SWE-bench Functional Synthesis Benchmark" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[ { "benchmark_name": "HumanEval & SWE-bench Functional Synthesis Benchmark", "metric": "pass@1 / resolved", "score": "Quantitative Program Synthesis & Pass@k Metric" } ]	[ { "paper_id": "2507.01477v2", "title": "Combining Type Inference and Automated Unit Test Generation for Python", "cosine_sim": 0.7048 }, { "paper_id": "2606.08588v1", "title": "LLM vs. Human Unit Tests: Fault Detection on Real Python Bugs", "cosine_sim": 0.6869 }, { "paper_id": "...	No public code repository attached. Paper provides formal program synthesis specification.	Many popular Python libraries use C-extensions for performance-critical operations allowing users to combine the best of the two worlds: The simplicity and versatility of Python and the performance of C.	To overcome this problem, we propose separating the generation and execution stages of the test-generation process.	Subprocess-execution allowed automated testing of up to 56.5% more modules and discovered 213 unique crash causes, revealing 32 previously unknown faults.	7	Syntax-Guided AST Modeling & Semantic Code Search	Explosive (>50/mo)	232	2026-08-21T14:38:22.082864
2605.03956v1	Generating Proof-of-Vulnerability Tests to Help Enhance the Security of Complex Software	2026-05-05T16:39:29Z	[ "cs.CR", "cs.SE" ]	ArXiv Standard	http://arxiv.org/licenses/nonexclusive-distrib/1.0/	true	60	Internal R&D Only (Copyleft or Academic Terms)	ArXiv Standard Distribution; Repository License Unspecified	1	"Proposes Prior work shows that developers often require concrete and executable evidence, i.e., pro(...TRUNCATED)	Shravya Kanchi	5	[ "Shravya Kanchi", "Xiaoyan Zang", "Ying Zhang", "Danfeng Yao", "Na Meng" ]	[ "Autonomous Software Engineering AI Lab" ]	http://arxiv.org/abs/2605.03956v1	NOT_DETECTED	Not Applicable	[]	0	0	Not Applicable	Not Applicable	115.33	"Developers create modern software applications (Apps) on top of third-party libraries (Libs). When (...TRUNCATED)	[-0.08836500346660614,0.06098699942231178,-0.03901999816298485,0.01435100007802248,0.038385998457670(...TRUNCATED)	[-0.11676599830389023,-0.0031500000040978193,-0.05471700057387352,0.00483600003644824,0.029249999672(...TRUNCATED)	AI Code Generation, Autonomous SWE Agents & Program Synthesis	Static Analysis & Vulnerability Remediation	[ "Java / Kotlin" ]	GPT-4o / Codex Specialized Coding LLM	[ "HumanEval & SWE-bench Functional Synthesis Benchmark" ]	[ "Quantitative Program Synthesis & Pass@k Metric" ]	[{"benchmark_name":"HumanEval & SWE-bench Functional Synthesis Benchmark","metric":"pass@1 / resolve(...TRUNCATED)	[{"paper_id":"2507.15241v1","title":"FaultLine: Automated Proof-of-Vulnerability Generation Using LL(...TRUNCATED)	No public code repository attached. Paper provides formal program synthesis specification.	Developers create modern software applications (Apps) on top of third-party libraries (Libs).	"Prior work shows that developers often require concrete and executable evidence, i.e., proof-of-vul(...TRUNCATED)	"PoVSmith substantially outperforms the state-of-the-art LLM-based approach, as it reduces human inv(...TRUNCATED)	7	Syntax-Guided AST Modeling & Semantic Code Search	Explosive (>50/mo)	232	2026-08-21T14:37:12.539413

End of preview. Expand in Data Studio

YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

💻 AI Code Generation, SWE Agents & Program Synthesis Dataset (2026 Edition)

A structured research dataset featuring 3,181 domain-verified research papers and 771 official code repositories focused on Autonomous Software Engineering Agents (SWE-bench), Program Synthesis, DeepSeek-Coder-V2, Qwen2.5-Coder, Test-Driven Code Repair, Self-Healing Software, AST Semantic Modeling, and Formal Logic Verification (2023–2026).

Built with Universal Scientific Engine V17.1 Gold, providing 47 schema attributes with verified repository attribution, 8 AI topological semantic clusters, pre-calculated Top-3 Semantic Nearest Neighbors Graph, structured benchmark leaderboards, and native 384-dimensional dense PyTorch embeddings.

📊 Dataset Schema Highlights (47 Columns)

Field	Type	Description
`paper_id`	String	Unique ArXiv identifier
`title`	String	Research paper title
`cluster_topic_name`	String	1 of 8 AI Topological Semantic Clusters
`code_agent_execution_mode`	String	Execution mode (SWE-bench Agent, Pair Programmer, Self-Repair)
`coding_foundation_backbone`	String	Model backbone (DeepSeek-Coder-V2, Qwen2.5-Coder, Claude-3.5)
`programming_languages_supported`	List[String]	Supported languages (Python, Rust, C++, TypeScript, Go)
`tested_benchmarks`	List[String]	Benchmarks evaluated (SWE-bench, HumanEval, MBPP, LiveCodeBench)
`benchmark_leaderboard_json`	List[Struct]	Structured pass@1 & resolved scores
`semantic_nearest_neighbors_top3`	List[Struct]	Pre-calculated top-3 related papers with cosine similarity
`commercial_ip_safety_score`	Integer	0–100 commercial compliance index (94% Enterprise Safe)
`tldr_neural_summary`	String	15-word executive summary of key innovation
`title_vector_384d`	List[Float]	384d PyTorch embedding (`all-MiniLM-L6-v2`)
`abstract_vector_384d`	List[Float]	384d dense contextual PyTorch embedding
`reproduction_recipe`	String	1-line bash setup command

🧩 8 AI Semantic Clusters Breakdown

Test-Driven Program Repair & Self-Healing Code Synthesis (599 papers)
Code Foundation Models & Specialized Instruction Distillation (501 papers)
Formal Verification, Theorem Proving & Symbolic Logic (452 papers)
Syntax-Guided AST Modeling & Semantic Code Search (421 papers)
Interactive Coding Assistants & Human-AI Pair Programming (356 papers)
Static Analysis, Vulnerability Detection & Automated Security Patching (336 papers)
Autonomous Repo-Level Software Engineering Agents (SWE-bench) (276 papers)
Multi-Language Code Translation & Cross-Platform Migration (240 papers)

📊 Interactive OpenAngels Visual Dashboard Included

Open DATASET_ANALYTICS_DASHBOARD_100_SAMPLE.html directly in your browser (Chrome/Edge/Safari) to explore the interactive visual intelligence directory with real-time filtering, search, and paper metrics.

💻 1-Click Python Quickstart

import pyarrow.parquet as pq

# Load 100-Sample Teaser
table = pq.read_table("AI_CODE_GENERATION_SWE_AGENTS_PROGRAM_SYNTHESIS_2026_100_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample AI Coding papers.")
print(f"Top Paper: {df['title'].iloc[0]}")
print(f"Execution Mode: {df['code_agent_execution_mode'].iloc[0]}")
print(f"Top-3 Nearest Neighbors: {df['semantic_nearest_neighbors_top3'].iloc[0]}")

🚀 Get the Full 3,181-Paper Enterprise Edition

The complete commercial production dataset (3,181 papers in Parquet with 384d vectors, SQLite DB, Clean CSV, Interactive OpenAngels HTML Dashboard, and JSON) is available here:

👉 BeatsProm AI Code Generation & SWE Agents Dataset Full Edition

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