Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
DECOMOE: DECOUPLING VISUAL PROPAGATION AND ... Summary pending (next check). Could not confirmPreprint	Multimodal models via openreview · 15 Oct, 06:19	Waiting for check
On the Evaluation of Large Language Models in Unit Test Generation This paper presents an empirical study evaluating open-source language models for automated unit test generation in software development. Peer-reviewedOlder paper (2024) Older paper (2024) — discussed this week	AI safety and reliability in deployment via acm · 15 Oct, 06:19	Not relevant Evaluates unit test generation rather than operational AI deployment safety.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Multimodal models via ieee · 15 Oct, 06:19	Not relevant Generates layout bounding boxes rather than multimodal reasoning across vision and text.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Computer vision for industrial applications via ieee · 15 Oct, 06:19	Not relevant Focuses on discrete layout synthesis, not industrial defect detection.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints. Peer-reviewedOlder paper (2023) Older paper (2023) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Covers bounding box generation, not retrieval-augmented generation.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Addresses software engineering requirements rather than operational AI safety and robustness.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	LLM evaluation and benchmarks via ieee · 15 Oct, 06:19	Not relevant Empirical study of requirements generation rather than an LLM evaluation benchmark.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Uses LLM prompting pipelines, not multi-step autonomous planning agents.
Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study This paper evaluates the ability of language models to generate non-functional requirements from functional software specifications. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About non-functional requirement extraction, not retrieval-augmented generation.
MAGE: A Multi-Agent Engine for Automated RTL Code Generation This paper introduces an open-source multi-agent system designed to generate, verify, and debug hardware description code. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Focuses on Verilog code generation, not retrieval-augmented generation.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About video generation benchmarking, not retrieval-augmented generation systems.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Computer vision for industrial applications via ieee · 15 Oct, 06:19	Not relevant Targets generative video evaluation, not industrial visual inspection or defect detection.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Focuses on video quality benchmarking rather than AI safety or guardrails.
Video-Bench: Human-Aligned Video Generation Benchmark This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Evaluates video generation models, not multi-step tool-using agents.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	LLM evaluation and benchmarks via ieee · 15 Oct, 06:19	Not relevant Develops a code generation framework rather than a general evaluation benchmark.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 15 Oct, 06:19	Not relevant Focuses on hardware code synthesis, not deployment safety or reliability.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI agents via ieee · 15 Oct, 06:19	Not relevant Applies evolutionary optimization to LLM prompts, not multi-step agent systems.
REvolution: An Evolutionary Framework for RTL Generation driven by Large Language Models This paper combines evolutionary computation with language models to optimize register-transfer level hardware code for correctness and efficiency. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant About RTL code generation, not retrieval-augmented generation.
A Survey of Synthetic Data Generation for Machine Learning This paper surveys synthetic data generation and data augmentation techniques for machine learning with an emphasis on healthcare. Peer-reviewedOlder paper (2021) Older paper (2021) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Focuses on synthetic data generation, not retrieval-augmented generation.
Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	LLM evaluation and benchmarks via acm · 15 Oct, 06:19	Not relevant Proposes requirements engineering frameworks rather than LLM evaluation benchmarks.
Specifying the Delegated-Autonomy Boundary: Requirements Engineering for Agentic AI This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	LLMs for code via acm · 15 Oct, 06:19	Not relevant Focuses on general autonomy specification rather than code generation systems.
Agentic Hyperautomation: A Distributed Architecture for Scalable AI-Driven Workflows This paper presents a multi-agent framework where a central model coordinates specialized agents and tools for business workflow automation. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	Retrieval-augmented generation (RAG) via ieee · 15 Oct, 06:19	Not relevant Focuses on multi-agent hyperautomation; RAG is only a minor component.
When pose graphs are not enough: a failure analysis of pose-graph-based camera estimation for Gaussian splatting This paper analyzes the accuracy limits of pose-graph optimization compared to bundle adjustment for drone-based Gaussian splatting. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Covers real estate 3D reconstruction, not manufacturing defect detection.
Efficient Multimodal Beam Prediction for Real-World 6G UAV Communications via Image-Position-Synthetic Fusion This paper evaluates a multimodal neural network that fuses drone camera images and kinematic data for communications beam prediction. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Studies 6G UAV beam prediction, not industrial visual inspection.
Jensen-Bregman split Gibbs sampling This paper extends split Gibbs sampling to non-Euclidean geometries using Jensen-Bregman divergence for imaging inverse problems. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Presents sampling algorithms for inverse problems, not industrial quality control.
Calibrating Generative Flows through Regional Probability Matching This paper introduces a flow-matching method that preserves regional probability mass to model rare events more accurately. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Addresses generative flow mathematics, not industrial computer vision applications.
Low-Altitude Agentic Networks: From UAV Connectivity to Workflow-Driven Capability Orchestration This paper proposes an agentic architecture that dynamically coordinates distributed drone capabilities based on workflow demands. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Focuses on UAV networking workflows, not industrial visual inspection.
Recovery-Oriented Optical System Design Based on Fisher Information This paper proposes an optical system design framework based on Fisher information to improve downstream image restoration performance. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:19	Not relevant Addresses optical lens design, not industrial inspection or defect detection.
Vibe Coding with Google AI Studio Case Study This case study evaluates a non-programmer using an AI development tool to build a database application in a non-English language. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Examines non-programmer code generation rather than AI safety guardrails.
Conserved amount, negotiable placement. Prompting perturbs one architecture, an AI reviewer both The paper examines how prompting conditions and automated agent reviews affect code complexity placement across two software architectures. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on software architecture evolution, not AI deployment safety.
Agentic Evolution of SQL Test Generators The authors use coding agents and execution validation to add new database dialect features into reusable database test generators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Addresses database testing evolution rather than AI deployment safety guardrails.
HoldSpec: A Machine-Checked Model and Conformance Suite for the Payment Authorization Hold Lifecycle The paper models payment authorization hold lifecycles using formal state machines to generate conformance tests for provider application interfaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on payment API conformance testing, not AI deployment safety.
HoldSpec: A Machine-Checked Model and Conformance Suite for the Payment Authorization Hold Lifecycle The paper models payment authorization hold lifecycles using formal state machines to generate conformance tests for provider application interfaces. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Covers formal TLA+ specifications for payments, not LLMs for code.
Taylor-Approximation-Based Repair of High Floating-Point Errors The authors present a framework that uses localized Taylor approximations to repair high floating-point calculation errors in numerical C routines. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Presents numerical C routine repair via Taylor approximations without LLMs.
Taylor-Approximation-Based Repair of High Floating-Point Errors The authors present a framework that uses localized Taylor approximations to repair high floating-point calculation errors in numerical C routines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Covers classical numerical floating-point repair, not LLM deployment safety.
Rethinking SZZ Search Strategies: Lessons from Claude Code The paper studies whether flexible search strategies used by coding agents improve the identification of commits that introduce software bugs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Studies commit history search strategies rather than AI deployment risks.
When Is a Reconciliation Break Real? A Temporal Correctness Contract for Streaming Financial Reconciliation The authors provide a formal correctness contract to identify genuine breaks in asynchronous streaming financial reconciliation systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on financial streaming correctness rather than AI deployment safety.
When Is a Reconciliation Break Real? A Temporal Correctness Contract for Streaming Financial Reconciliation The authors provide a formal correctness contract to identify genuine breaks in asynchronous streaming financial reconciliation systems. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant About formal stream processing contracts, not LLM code generation.
Executable but Unfaithful: Mock Object, Stub, and Assertion Generation by LLMs and Coding Agents The paper introduces a benchmark to evaluate how accurately language models and coding agents generate mock objects, stubs, and assertions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Targets unit test mock generation, not deployment safety or guardrails.
TileCas: Cascading Kernel Development Across Programming Abstractions The paper presents an interface that lets coding agents navigate and edit multiple abstraction levels when generating accelerator kernels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on compiler kernel generation rather than safety or reliability.
Testuary: Agentic Test Generation with Zero-Shot Parallelism The authors combine zero-shot test suite generation with agentic refinement to improve test coverage and mutation scores. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on automated test generation rather than deployment safety guardrails.
Verifying Autoware Controller Node by Combining Model Verification and Runtime Monitoring The paper combines formal dynamic logic models and runtime monitoring to verify trajectory-following control in the Autoware autonomous driving platform. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Addresses formal verification of autonomous driving controllers, not LLMs for code.
Verifying Autoware Controller Node by Combining Model Verification and Runtime Monitoring The paper combines formal dynamic logic models and runtime monitoring to verify trajectory-following control in the Autoware autonomous driving platform. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on formal control theory for vehicles rather than LLM safety, guardrails, or alignment.
PassSum: Leveraging Paths of Abstract Syntax Trees and Self-Supervision for Code Summarization The paper proposes a code summarization method that encodes abstract syntax tree paths using self-supervised learning to reduce training overhead. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on code summarization performance, not AI system safety or reliability in deployment.
Comparing the Pre-trained Models of Source Code by Re-pre-training under a Unified Setup The paper evaluates pre-trained code models under a standardized experimental setup to compare the impacts of their pre-training tasks fairly. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Benchmarks pre-training objectives for code models, not deployment safety or guardrails.
Reviewing Beyond the Change: Repository Size and Existing Code Across Agent and Human Pull Requests The paper studies how often human and agent pull request reviews reference existing repository code outside the submitted change. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Studies repository context in code reviews, not deployment safety or reliability risks.
Separating Statistical Inference from Experimental Runtime: An Architecture for Sequential Scientific Analysis The authors present a software architecture that separates incremental statistical calculations from execution runtimes and data delivery pipelines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Addresses general scientific computing architecture rather than AI deployment safety risks.
Separating Statistical Inference from Experimental Runtime: An Architecture for Sequential Scientific Analysis The authors present a software architecture that separates incremental statistical calculations from execution runtimes and data delivery pipelines. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Proposes a software architecture for sequential statistics, not LLMs for code.
BRQGen: Towards Adaptive Completeness Assessment and Follow-Up Question Generation for Bug Reports The paper proposes a framework that identifies missing information in bug reports and generates targeted follow-up questions only when needed. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on bug report clarification rather than AI safety or reliability in production.
Software Testing Meets Bayesian Workflow: A Literature-Driven Artifact Study of Probabilistic Programs The authors analyze statistical literature to turn visual judgment guidelines into testable correctness specifications for probabilistic programs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on statistical software testing, not AI deployment safety or model guardrails.
Software Testing Meets Bayesian Workflow: A Literature-Driven Artifact Study of Probabilistic Programs The authors analyze statistical literature to turn visual judgment guidelines into testable correctness specifications for probabilistic programs. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Studies testing of Bayesian probabilistic programs without addressing LLMs for code.
Domain-Knowledge Driven Configuration Tuning of Kubernetes Workloads The paper presents a method using domain constraints to tune Kubernetes workload configurations without disrupting live production performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Deals with platform infrastructure tuning rather than safety and reliability of AI deployments.
Domain-Knowledge Driven Configuration Tuning of Kubernetes Workloads The paper presents a method using domain constraints to tune Kubernetes workload configurations without disrupting live production performance. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Tackles Kubernetes cloud configuration optimization, not LLMs for code or software development.
DeepGroundRepair: From API-Grounded Generation to Shape-Aware Repair for Deep Learning Programs This paper presents a three-stage generation and repair framework to resolve API constraints and tensor shape errors in deep learning programs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on program synthesis correctness rather than deployment safety, guardrails, or jailbreaks.
When Is RTL Better? Auditing Evaluation Stability with Tool-Grounded Requirement Profiles The authors introduce tool-grounded requirement profiles to audit and stabilize how register-transfer-level code generation is evaluated. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on hardware code synthesis evaluation rather than AI system deployment safety.
Cross-Project Bug Localization: What Works, What Doesn't, and Why The paper compares three cross-project bug localization model architectures across 13 open-source Python projects against limited within-project baselines. Preprint	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Evaluates traditional deep learning models for bug localization without involving LLMs for code.
Cross-Project Bug Localization: What Works, What Doesn't, and Why The paper compares three cross-project bug localization model architectures across 13 open-source Python projects against limited within-project baselines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Investigates cross-project bug localization algorithms, not AI deployment safety or reliability.
State-Aware Generation of Deployable Websites from Visual Prototypes and Requirements The paper introduces a graph framework to maintain consistency across visual elements, workflows, and code when generating websites from prototypes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Addresses prototype-to-code alignment rather than general AI safety, guardrails, or hallucination prevention.
Mobile App Analysis in the New Era: Challenges and the Road Ahead This paper reviews challenges in mobile app analysis across code artifacts, runtime systems, and application ecosystems. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:19	Not relevant Focuses on conventional mobile app testing and compliance, not AI deployment safety or reliability.
Mobile App Analysis in the New Era: Challenges and the Road Ahead This paper reviews challenges in mobile app analysis across code artifacts, runtime systems, and application ecosystems. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:19	Not relevant Discusses general mobile app analysis challenges rather than code LLMs or coding assistants.

8,161–8,220 of 17,313← Previous Next →