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その.envのAPIキーが、AIエージェントを「内通者」に変える――“人間前提のやり方”は破綻したThat API key in .env can turn your AI agent into an insider threatAs AI agents spread, the credentials behind them, API keys and tokens, are multiplying. The article argues that key management designed around human users breaks down once always-on agents act autonomously, and that a single key left in a .env file can make an agent an insider.
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Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science
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A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models
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Disentangling Representation Evolution in Transformers through Directional Decomposition
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Discovery Foundation Models: Toward Open-Ended Discovery Intelligence
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Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
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Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering
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Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale
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HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses
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Recurrent GraphNeural NetworkswithSet-BasedAggregation
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Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adoption under Rapid Technological Progress
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Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
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LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys
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LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction
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Task-Directed Residual AddUNet:Perfect-Reconstruction Routing for Full-Rate Representations
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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations
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Learning to Coach for Experiential Learning
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Proportional-Fair Resource Allocation and Dual-Threshold Early-Exit Inference for Secure Cooperative Multi-Layer Edge Intelligence
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Accelerating Dropless MoE Training in JAX with NVIDIA Transformer EngineNVIDIA boosts JAX dropless MoE training 10x with Transformer EngineNVIDIA details Transformer Engine optimizations for dropless MoE training in JAX: grouped GEMM for ragged expert shapes, MXFP8 quantization, and fused dispatch/combine via NCCL EP. DeepSeek-V3 671B throughput rose from 103 to 1,068 TFLOPS/GPU (10.4x), with 97% scaling efficiency at 1,024 GPUs.
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Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression
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CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
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Sharp Rates and a One-Line Correction for Spectral Representation Learning
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AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
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Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking
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Delegating Authorization to Misaligned Agents: Coalitional Alignment and Safe Control
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When Should a World Model Move? Loss-Conditioned State Execution
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Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation
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MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
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EvoOntology: A Self-Evolving Ontology Layer for Data Agents
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Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings