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2060
Stories
Исследования1 мин

Self-Improving Agents with World Model Predictive Paths

arXiv:2610.28945v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to interact with environments and improve their decisions during deployment. However, how anticipating future outcomes and drawing on accumulated interaction experience…

arXiv — cs.AI
Исследования1 мин

Speedrunning Relational Foundation Model Pretraining

arXiv:2610.28855v1 Announce Type: new Abstract: Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate.…

arXiv — cs.AI
Исследования1 мин

To Manipulate or Not: Opinion Dynamics in LLM Interactions

arXiv:2610.28875v1 Announce Type: new Abstract: Large language models increasingly take part in extended conversations where they can both shape and be shaped by another party's views, yet most persuasion research measures only one side of that exchange. We introduce a…

arXiv — cs.AI
Исследования1 мин

GRAIL: From Saliency to Semantics in Time-Series Explanation

arXiv:2610.29036v1 Announce Type: new Abstract: Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful patterns live across many samples, not in any one of them.…

arXiv — cs.AI
Исследования1 мин

Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing

arXiv:2610.29057v1 Announce Type: new Abstract: Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated answer. This setting requires multi-instance…

arXiv — cs.AI
Исследования1 мин

Behavior Beyond Text: Evaluating Refusal Transfer Failure in LLM Agents

arXiv:2610.28982v1 Announce Type: new Abstract: While large language models are increasingly deployed as tool-calling agents, their safety evaluations still focus primarily on their ability to refuse harmful requests in text. We investigate whether the refusal behavior in text…

arXiv — cs.AI
Исследования1 мин

Fig: Automated Multiagent Behavior Elicitor

arXiv:2610.28891v1 Announce Type: new Abstract: Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors. We introduce Fig (Finding Interactions in Games), an…

arXiv — cs.AI
Исследования1 мин

GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development

arXiv:2610.29014v1 Announce Type: new Abstract: Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open…

arXiv — cs.AI
Исследования1 мин

Inheritance as an Admission Test for LLM Prompt Merge Operators

arXiv:2610.29023v1 Announce Type: new Abstract: Large language model (LLM) agents are often improved by iterating until a fixed verifier is done. Once that verifier saturates, the loop has nothing left to climb. We instead score agents against the current population, and a…

arXiv — cs.AI
Исследования1 мин

AutoFraming: Synthesizing Dynamic Frames of C Code with LLMs

arXiv:2610.29046v1 Announce Type: new Abstract: Large language models (LLMs) have shown considerable promise in synthesizing formal specifications from source code. However, existing approaches primarily focus on functional contracts and loop invariants, while largely neglecting…

arXiv — cs.AI
Исследования1 мин

D-Skill: Distilling Teacher Knowledge through Agent Skills

arXiv:2610.28914v1 Announce Type: new Abstract: Frontier language-model agents excel at complex tasks, but high inference costs make transferring their capabilities to smaller models essential. Traditional knowledge distillation achieves this through parameter updates, requiring…

arXiv — cs.AI
Исследования1 мин

Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis

arXiv:2610.28836v1 Announce Type: new Abstract: This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlist and automatically…

arXiv — cs.AI
Стартапы1 мин

CLM-8B:用于选择代理操作的开放且快速的模型

🌟 CLM-8B:用于选择代理动作的开放且快速的模型 斯坦福大学与 NVIDIA 合作,公开了对比语言模型课程的第一个模型。这是 Jev 的开放模拟,由前研究员的 TypeSafe AI 初创公司于 9 月 15 日提出......

Telegram — AI Machine Learning Big Data
Исследования1 мин

Anthropic 发布了 Claude Duet:第一个给你写信的私人经纪人

🤖 Anthropic 发布了 Claude Duet:第一个给你写信的私人经纪人。克劳德已经有了特工,但他们必须启动并等待。 Duet 的工作方式有所不同:您将一个任务放入类似信使的线程中,它在云计算机的后台执行,并在...时写入。

Telegram — AI Machine Learning Big Data
Стартапы1 мин

梦想实验室提出了一个控制陌生机器人的模型

✔️ Dream Labs 提出了一个控制陌生机器人的模型 来自 GR00T 机器人项目团队的四位前 NVIDIA 研究人员通过他们的初创公司走出了隐身状态,并开放了 Dream-1 模型的公共 API。该公司由 Joel Jang 领导,他领导了 GR00T……

Telegram — AI Machine Learning Big Data
Исследования1 мин

V7 cuts costs 78% while boosting accuracy with GPT‑5.6 Luna

С выходом GPT 5.6 мы увидели огромные улучшения в создании корпоративных рабочих процессов с помощью B7 Go. Ещё несколько месяцев назад модели не могли по-настоящему понять сложные процессы, такие как андеррайтинг страховок или проверка финансовых сделок, но новейшие модели GPT…

YouTube — OpenAI Official
Исследования1 мин

Anthropic 推出 Claude Duet 对标 Muse,推出新 AI 智能体

Anthropic 刚刚宣布推出 AI 智能体 Claude Duet,以竞争 Meta 的 Muse 和 OpenAI 的 Dots。据 Anthropic 称,这款面向专业人士和小企业主的新智能体将以「研究预览版」形式发布,并作为独立应用在 iOS 上提供……

The Verge — AI
Исследования1 мин

Claude разработал белки, которые могут помочь в создании противоядия от укусов скорпионов

🦂 Claude разработал белки, которые могут помочь в создании противоядия от укусов скорпионов Исследователи использовали Claude для проектирования белков, способных связываться с опасными токсинами скорпионов и потенциально блокировать их действие. Всего за 64 часа ИИ создал…

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