模型与评测｜Pai 的 AI 雷达热点

Copilot 全面可用 Fable 5.1

GitHub Changelog：Copilot 接上 Fable 5.1。

EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction

Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a single pass of a frontier model over an agentic benchmark can cost hun

arxiv.org · 赞 110打开原文

#51

The Post-AI Internet Doesn't Look Great

jordangoodman.xyz打开原文

#52

Embedded Rust RTOS vs. C RTOS

tweedegolf.nl打开原文

#53

Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and ve

arxiv.org · 赞 496打开原文

#54

Fable 5.1 系统卡

隐蔽任务和监控难度上升写进系统卡，Mythos 走受邀。

anthropic.com打开原文

#55

Fable 5.1 画了一只会动的鹈鹕

Simon 用新模型做动画鹈鹕，当能力探针。

simonwillison.net打开原文

#56

Anthropic 新闻稿：Fable 5.1

新闻页与模型页互补，讲定价和开放范围。

anthropic.com打开原文

#57

Show HN: Redactle LLM Leaderboard

I got nerd sniped and ran a bunch of evaluations to see how well models could play Redactle. It turns out Gemini flash is in a league of it's own in both performance and cost. It's

redactle.net打开原文

#58

Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and ve

arxiv.org打开原文

#59

Proper Scoring Rules Shape LLM Forecasting

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#60

Cliff: Learning Process Rewards from the First Mistake

Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards le

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#61