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Growing Concerns Over AI Code Quality and Security

The feed has recently focused heavily on the advancements of AI agents and the new challenges they introduce to software development. There is a noticeable technical effort to ensure output reliability, seen in tools like 'depgraph' for detecting latent code debt from AI-generated code or the use of adaptive ensemble methods (COVE) for pre-validating LLM outputs. Furthermore, alongside predictions that AI agents will evolve to autonomously manage aspects of human life, there is a concurrent warning about escalating security risks and infrastructure vulnerabilities, indicating significant uncertainty.

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As of Nov 4, 2026, 11:36 PM UTC

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arXivOctober 14, 2026

Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents

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본 연구는 LLM 에이전트가 불완전한 초기 지시사항을 바탕으로 실제 요구사항을 추론하는 '결과 정렬(outcome alignment)' 능력을 정의하고 측정했습니다. SWE-chat 분석 결과, 사용자의 진술된 요구사항은 실제 필요와 괴리가 크다는 점을 발견했습니다. 이를 검증하기 위해 새로운 벤치마크 TiGanEval을 구축했으며, 최신 모델들조차 이 능력에서 현저한 부족함을 보였습니다.

llm-agent
outcome-alignment
software-engineering
benchmarking
tiganeval

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Growing Concerns Over AI Code Quality and Security

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Growing Concerns Over AI Code Quality and Security

Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents

Interaction-as-a-Judge

Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing

SpecCloak: Probing Harmful Code Generation through Engineering Specifications

Soliduzz: Finding Miscompilations in Smart-Contract Compilers with Assertion-Driven Equivalence Oracles

SWEPatchTest: Evaluating Software Tests Beyond Reproduction

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Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments

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