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Focus Shifts to Structured and Verifiable AI Development

The observed trend moves beyond simple model capability improvements toward building highly reliable systems that function within complex, real-world constraints. This is evidenced by the focus on using composable architecture frameworks or subjecting AI to rigorous validation through high-difficulty benchmarks like the Ramanujan Challenge. Furthermore, securing efficiency and robustness has become a critical concern across diverse domains, from optimizing RAG performance to streamlining embedded system security patches.

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arXivAugust 4, 2026

Fourier-Latent Diffusion for Constrained Generation of Triply Periodic Minimal Surfaces

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잔류 평균 곡률이 낮은 삼중 주기적 최소 곡면(TPMS)을 제어 가능한 방식으로 생성하는 확산 기반 프레임워크를 제안합니다. Fourier 잠재 공간과 트랜스포머 기반 확산 모델을 사용하여 대규모 데이터셋을 바탕으로 정교한 기하학적 구조를 생성합니다.

tpms
diffusion-models
minimal-surfaces
generative-ai
transformer

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Focus Shifts to Structured and Verifiable AI Development

Fourier-Latent Diffusion for Constrained Generation of Triply Periodic Minimal Surfaces

Qwen3.8-Max Launches: When One Model Speaks Both "Dialects" of OpenAI and Anthropic, How Should Multi-Model Architecture Be Written?

VAD: Visual Attribution Distillation A counterfactual target reconstruction algorithm that estimates the visually attributable part of a te…

알리바바가 Qwen3.8-Max를 공개하며 AI 모델 시장의 전략적 변화를 예고하다

어제 "주장은 걸러 들으라"고 했던 Qwen3.8-Max, 실측이 차트에 떴음. 성능은 진짜 소넷급인데 가격도 소넷급임.

Special Architecture in AFM3 20B: Instruction Following Pruning

Intology's Locus beats human-tuned Qwen3-1.7B in auto post-training

No universal hallucination detector, but a universal floor — pre-registered, 10 models. Come break it. [R]

DeepSeek V4 Is Now the 'Kill Line' of AI Models — Here's What That Means

Decision Trees Aren't Trained. They're Grown.

Google 「Gemini Robotics 2」를 1차 자료로 정리. 전신 제어 · 새로운 양팔 로봇 기체 적응 200례 미만 · 일반 공개는

TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference

Coding Agents as Test-Suite Auditors: Finding What Official Suites Miss While Approaching What They Catch

EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming

Post-Training on Office Work Improves Software Engineering: A Behavioral Account of Cross-Domain Transfer

AI-assisted Script Management for Requirements Elicitation Interviews

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Effective and Efficient Context Retrieval via Partial Dependency Graph for Repository-Level Code Generation

Vulnerability Detection in AArch64 Machine Code Using a Digital Twin

Lossless Tensor Compression as Program Synthesis