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21 件
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スライド 3 枚17:15EN
Matthew Berman· 2026年8月28日The Hugging Face Incident: AI Evaluation, Sandbox Escape and Reward HackingMatthew Berman · Forward FutureMatthew Berman explains the reported OpenAI evaluation incident and the security and incentive failures it exposed.AI SafetyAI securityCybersecurity+1
スライド 9 枚16:11EN
Matthew Berman· 2026年6月19日Seven AI Coding Agent Loops and When to Use ThemMatthew Berman · Forward FutureMatthew Berman explains how triggers, goals and repeatable checks can turn coding tasks into agent workflows, with examples for performance, documentation and product quality.コーディングエージェント開発ワークフローとテストエージェント工学+2
スライド 5 枚17:03EN
Matthew Berman· 2026年5月27日DeepSWE: Testing Coding Agents on Longer Software TasksMatthew Berman · Forward FutureMatthew Berman reviews Datacurve’s benchmark, its verifier audit and the tradeoffs of comparing models under a shared harness.Benchmarksコーディングエージェント評価(Evals)+2
スライド 5 枚25:15EN
Matthew Berman· 2026年4月8日Matthew Berman on Claude Mythos, Project Glasswing and AI SecurityMatthew Berman · Forward FutureBerman examines Anthropic’s limited cybersecurity preview and its reported capabilities, then weighs the promise and risks of increasingly capable AI systems.AI securityインフラとデプロイ評価(Evals)+2
スライド 3 枚27:15EN
Matthew Berman· 2026年3月31日Meta-Harness: Searching for Better AI Agent InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a paper on using a coding agent to iteratively improve model harnesses and discusses its experimental limits.Harness engineeringエージェント工学評価(Evals)+1
スライド 3 枚15:54EN
Matthew Berman· 2025年10月1日Claude Sonnet 4.5: Coding, Long Tasks and a Simulated DesktopMatthew Berman, Dario Amodei · Forward FutureMatthew Berman reviews Anthropic’s launch claims and tries a generated-interface demo.コーディングエージェント評価(Evals)エージェント工学+1
スライド 16 枚25:24EN
Matthew Berman· 2025年8月7日GPT-5 Tested on Coding, Simulations and Multimodal TasksMatthew Berman · Forward FutureMatthew Berman tries browser applications, visual simulations and image prompts, showing both successes and defects in his early hands-on tests.コーディングエージェントSoftware engineeringMultimodal AI+2
スライド 9 枚15:30EN
Matthew Berman· 2025年7月31日Qwen3 Tested: Coding Demos, Long Context and Response BehaviorMatthew Berman · Forward FutureMatthew Berman tests generated simulations, political and safety prompts, hand tracking and word puzzles using a hosted Qwen3 model.Open-weight modelsコーディングエージェント評価(Evals)+2
スライド 9 枚22:05EN
Matthew Berman· 2025年6月9日Apple’s “Illusion of Thinking”: Puzzle Limits and a Code-Assisted TestMatthew Berman, Ilya Sutskever · Forward FutureMatthew Berman reviews Apple’s reasoning-model experiments and tests a different way to solve Tower of Hanoi using generated code.Reasoning & test-time computeBenchmarks評価(Evals)+1
スライド 4 枚19:06EN
Matthew Berman· 2025年4月25日Sleep-Time Compute: Preparing AI Context Before a QuestionMatthew Berman · Forward FutureMatthew Berman reviews a 2025 paper on moving some reasoning ahead of a user query, and explains where its experiments found benefits and limits.Reasoning & test-time computeAgent memoryAI cost & performance+2
スライド 10 枚26:35EN
Matthew Berman· 2025年4月23日Testing Gemini 2.5 Flash on Coding and Video UnderstandingMatthew Berman · Forward FutureMatthew Berman compares Google’s faster model with Gemini 2.5 Pro through interactive simulations, games and questions about a long video.モデルと学習コーディングエージェントMultimodal AI+2
スライド 5 枚26:05EN
Matthew Berman· 2025年3月31日Inside Claude: What Interpretability Research Reveals—and What It Cannot Yet ExplainMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s case studies on multilingual features, planning, arithmetic, hallucinations and safety.AI researchモデルと学習AI Safety+1
スライド 5 枚19:06EN
Matthew Berman· 2024年12月28日Building Effective AI Agents: Patterns, Trade-offs and EvaluationMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s guide to workflows and agents, adding examples from his own projects.エージェント工学エージェント評価(Evals)+1
スライド 9 枚24:08EN
Matthew Berman· 2024年12月12日What Apollo’s 2024 Scheming Tests Found in Language ModelsMatthew Berman · Forward FutureMatthew Berman reviews experiments on deceptive actions, concealment and sandbagging, with the study’s staged goals and sandbox limitations in view.AI Safety評価(Evals)AI Alignment+1
スライド 6 枚26:43EN
Matthew Berman· 2024年10月10日Testing OpenAI o1 on Planning, Rules and Spatial ReasoningMatthew Berman · Forward FutureMatthew Berman reviews a 2024 pilot study comparing o1-preview, o1-mini and GPT-4 on six planning domains.評価(Evals)Reasoning & test-time computeAI research+2
スライド 5 枚19:38EN
Matthew Berman· 2024年9月9日Reflection 70B: Conflicting Results and Unanswered QuestionsMatthew Berman · Forward FutureMatthew Berman reviews his early coverage, independent evaluations and competing claims about the September 2024 release.BenchmarksOpen-weight models評価(Evals)+2
スライド 3 枚16:28EN
Matthew Berman· 2024年4月23日Testing Reka Core: Multimodal Benchmarks, Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman reviews Reka’s reported model comparisons and tests Core on code, logic and image understanding.Multimodal AIBenchmarksモデルと学習+1
スライド 3 枚15:01EN
Matthew Berman· 2024年4月19日Testing Llama 3 at Launch: Code, Reasoning and Image GenerationMatthew Berman · Forward FutureMatthew Berman tries Meta AI’s new interface on a snake game, logic puzzles and rapid image generation.Open-weight models評価(Evals)モデルと学習+1
スライド 2 枚21:16EN
Matthew Berman· 2024年3月9日ArtPrompt: What a 2024 Study Found About Visual Text and Model SafetyMatthew Berman · Forward FutureMatthew Berman reviews an ASCII-art recognition benchmark and reports mixed results from his own informal tests.AI Safetyセキュリティと安全性評価(Evals)+1
スライド 3 枚23:00EN
Matthew Berman· 2024年2月22日Testing Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.Open-weight models評価(Evals)モデルと学習
スライド 6 枚18:08EN
Matthew Berman· 2023年6月29日Six Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.BenchmarksOpen-weight modelsモデルと学習+1