Applied & Human-Centered · Computer Science & AI
Software Engineering
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1,466 papers, from 31 August 2026 to 5 November 2026: 1,451 arXiv preprints and 15 OpenAlex records.
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Newest papers, page 54
On Device Agentic Operation Caches -- Classifier-Centric NL-to-Action Generation
Agentic AI is increasingly being embedded in software applications to provide natural language interfaces to features and functionality.What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review
Large language model (LLM) agents increasingly draw on external tools and reusable skills selected at run time from libraries that hold thousands of entries.PDFa11yMut: Measuring Mutation-Specific Detection in PDF Accessibility Checkers
Automated PDF accessibility checkers provide useful conformance evidence, but a clean report is not a complete accessibility oracle.NxM-Version Programming for Quantum Software: High-Level Components across Frameworks and Engines
Quantum computing has in recent years evolved from a purely theoretical field to an active area in both academia and industry.EngIntervene: Benchmarking Multimodal Engineering State Understanding and Design Intervention Reasoning
Multimodal engineering benchmarks largely evaluate static understanding, such as recognizing components, interpreting diagrams, or answering technical questions.InfoEdit: Probing Global Layout Reasoning in Infographic Editing
Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics.WideSWE: Can Coding Agents Coordinate Changes Across Repositories?
Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase.Evaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation
Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests.MetaBench-Harness: Unlocking End-to-End Optimization of Benchmark Harnesses
Rapid progress in Large Language Models (LLMs) is saturating static benchmarks faster than they can be designed.Graph-Guided Repository Environment Construction
Coding agents now increasingly rely on execution to validate their solutions, making the construction of reliable execution environments a critical enabling capability.Path2Spec: Path-Aware Specification Generation via Large Language Models
Formal specifications are critical for program verification, comprehension, and maintenance.Protected Cores Are Not Enough: Certifying AI-Proposed Revisions of Temporal Specifications
Runtime monitoring traditionally evaluates a specification that is fixed before execution or externally modified when requirements change.Neuro-Symbolic Indirect-Call Analysis under Opaque Pointers
Resolving indirect calls is central to call-graph construction for C. Scalable type-based analyses such as MLTA use type information in LLVM IR to associate indirect calls with functions assigned to…SpecRead: A Benchmark for Measuring Whether Language Models Understand Hardware Specifications
Existing benchmarks for large language models (LLMs) in hardware design evaluate downstream artifacts such as generated RTL, assertions, or testbenches.Beyond the Prompt: Linking What Developers Ask, Do, and Understand with Coding Agents
Coding agents can now change code for developers, who describe goals, supply context, and respond to the agent's work.RAISE: Reinforcing Access Control Policy Synthesis in LLMs via Symbolic Evaluation
Translating natural-language access-control requirements into policies requires careful reasoning about permissions, constraints, and exceptions, and even frontier LLMs often produce policies that…Identical Runs, Different Results: Benchmarking AI Coding Agents on Open-Weight Models
Repeated runs of the same coding agent are known to give different benchmark scores.Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
Outcome-only reinforcement learning gives software engineering (SWE) agents a terminal success signal but little direct guidance about intermediate decisions.Green AI: Cost of LLM-Based Code Completion
Code completion is one of the most widely used applications of large language models (LLMs) in software development.Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows
Large-language-model multi-agent systems (LLM-MAS) introduce a characteristic reliability problem: an error produced by one agent can be accepted as context by downstream agents and propagate across…