Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
Moralbench: Moral evaluation of llms.SIGKDD Explor., 27(1):62–71
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Vibe-coded software shows near-zero in-artifact variability; Variability by Regeneration uses the LLM as a derivation engine that regenerates dead-code-free binaries for each product-line variant.
An empirical study of 57 ML evaluation harnesses shows 41.4% of operational issues occur in the specification stage, driven mainly by unimplemented features, documentation gaps, and missing input validation.
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
A conceptual model proposes representing LLM workflow definitions, instances, and inference records as persistent typed objects in a shared knowledge substrate, distinguishing deterministic derive from LLM-mediated infer.
Evaluation of 15 LLM configurations across four conditions in a supply chain EDA benchmark finds most lack sufficient repeatability for autonomous deployment, with GPT-5.4 at extra-high reasoning effort scoring highest on mean score (0.8748) and proposed Business utility (0.6952).
QPipe deploys specialized LLM agents for parsing, formulation, code generation, review, execution and verification to produce quantum applications from 20 natural-language test-optimization requirements, reporting 100% compilation and 96.7% execution success with solutions that beat a genetic-algori
citing papers explorer
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Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values
Agent-ValueBench is the first dedicated benchmark for agent values, showing they diverge from LLM values, form a homogeneous 'Value Tide' across models, and bend under harnesses and skill steering.
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Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration
Vibe-coded software shows near-zero in-artifact variability; Variability by Regeneration uses the LLM as a derivation engine that regenerates dead-code-free binaries for each product-line variant.
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Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
An empirical study of 57 ML evaluation harnesses shows 41.4% of operational issues occur in the specification stage, driven mainly by unimplemented features, documentation gaps, and missing input validation.
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SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
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Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
A conceptual model proposes representing LLM workflow definitions, instances, and inference records as persistent typed objects in a shared knowledge substrate, distinguishing deterministic derive from LLM-mediated infer.
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Business Utility of Large Language Models as Exploratory Data Analysis Agents
Evaluation of 15 LLM configurations across four conditions in a supply chain EDA benchmark finds most lack sufficient repeatability for autonomous deployment, with GPT-5.4 at extra-high reasoning effort scoring highest on mean score (0.8748) and proposed Business utility (0.6952).
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Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization
QPipe deploys specialized LLM agents for parsing, formulation, code generation, review, execution and verification to produce quantum applications from 20 natural-language test-optimization requirements, reporting 100% compilation and 96.7% execution success with solutions that beat a genetic-algori