Исследования1 мин
The Pentagon wants $30 million to build an AI-powered lie detector
The Pentagon wants $30 million to build an AI-powered lie detector.
MIT Technology Review
Каталог публикаций
Все материалы
Все опубликованные аналитические материалы, исследования суверенного стека, новости экосистемы и обзоры моделей ИИ.
2218
Материалов
Исследования1 мин
Claude обнаружил ранее неописанную ферментную систему с повторами ДНК, напоминающими CRISPR, сообщает Anthropic.
🧬 Claude обнаружил ранее неописанную ферментную систему с повторами ДНК, напоминающими CRISPR, сообщает Anthropic. Около 950 агентов работали 21 час и использовали 210 млн токенов. Они исследовали более 200 тысяч обратных транскриптаз - ферментов, копирующих РНК в ДНК, -…
Telegram — AI Machine Learning Big Data
Исследования1 мин
OpenAI предложила правила внешнего аудита безопасности своих моделей
✔️ OpenAI предложила правила внешнего аудита безопасности своих моделей Компания готова открывать независимым организациям данные об обучении, тестировании и развёртывании моделей, чтобы те проверяли её доказательства безопасности. Главный предмет проверки – кейсы безопасности,…
Telegram — AI Machine Learning Big Data
Исследования1 мин
Claude Opus 5.5 научился создавать картины без генератора изображений — он буквально рисует их кодом
Claude Opus 5.5 научился создавать картины без генератора изображений — он буквально рисует их кодом.
Telegram — GPT Time | Нейросети | ИИ
Стартапы1 мин
Lightspeed targets $250M for new India fund, focusing on early-stage AI
The Silicon Valley firm is aligning its India fundraising cycle with its global funds for the first time, as it shifts to a shorter investment period.
TechCrunch — AI
Исследования1 мин
Крупная утечка: ChatGPT может получить новый тариф Pro Max за $500 в месяц.
Крупная утечка: ChatGPT может получить новый тариф Pro Max за $500 в месяц. Пока это только внутренняя конфигурация, официального анонса нет, но цена уже прописана: > $500 в месяц до уплаты налогов, в регионах с НДС отображается около $600 > Название тарифа: Pro Max >…
Telegram — Вайб-кодинг
Исследования1 мин
When Should Forecasting Agents Reason? Behavioral Stress Tests for Reliability Routing
arXiv:2609.28475v1 Announce Type: new Abstract: Forecasting agents increasingly combine language-model reasoning, retrieval, ensembling, and calibration, but it remains unclear when each behavior should be trusted. We study this question on ForecastBench-style binary forecasting…
arXiv — cs.AI
Исследования1 мин
AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
arXiv:2609.29014v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems can automate alpha factor mining, but their reliance on external APIs limits control over cost, availability, and confidentiality. Long research loops also tend to revisit a few…
arXiv — cs.AI
Исследования1 мин
From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLMs
arXiv:2609.28942v1 Announce Type: new Abstract: Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. However, existing methods typically align model outputs with a static value…
arXiv — cs.AI
Исследования1 мин
RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?
arXiv:2609.28850v1 Announce Type: new Abstract: Reproducing a machine learning paper involves most research steps, from installing software and debugging to running experiments, work that AI agents increasingly do. We introduce RECLAIM, a benchmark of 100 NeurIPS 2025 papers…
arXiv — cs.AI
Исследования1 мин
Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation
arXiv:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgments for formal statistical inference is…
arXiv — cs.AI
Исследования1 мин
Progressive Skill Discovery as Access Control for Tool-Using LLM Agents: Structural Governance through Role-Scoped Capability Delivery
arXiv:2609.28693v1 Announce Type: new Abstract: Large Language Model (LLM) agents struggle to scale safely when exposed to vast enterprise toolsets. Providing an agent with access to every internal tool leads to oversized context windows, degraded tool selection, and severe…
arXiv — cs.AI
Исследования1 мин
Agent Memory with Episodic Retrieval for Financial Decision-Making
arXiv:2609.28771v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in financial analysis and reasoning, inspiring recent advances in agent-based trading frameworks. While these systems show promise, prior approaches either…
arXiv — cs.AI
Исследования1 мин
PFArena: Benchmarking Language Models for Protein Modification
arXiv:2609.28921v1 Announce Type: new Abstract: Protein modification requires navigating an immense sequence space, yet wet-lab validation remains low-throughput and costly. Although computational paradigms including protein language models (PLMs), large language models (LLMs),…
arXiv — cs.AI
Исследования1 мин
Control the Harness, Control the Cost: Routing and Governing AI Coding Agents in the Enterprise
arXiv:2609.28919v1 Announce Type: new Abstract: Harnesses, the products that run AI coding agents, are multiplying, and enterprises are rolling them out to their employees: what started as pilots with a few hundred seats is scaling to tens of thousands. Most enterprises do not…
arXiv — cs.AI
Исследования1 мин
Pistis Technical Report
arXiv:2609.28554v1 Announce Type: new Abstract: We introduce the Pistis model family, comprising 27B- and 9B-parameter multimodal large language models built on Qwen3.6 and Qwen3.5, respectively, and developed through a general and scalable post-training framework. The framework…
arXiv — cs.AI
Исследования1 мин
BaseCamp --- An Agentic AI Framework for Automating DNA Sequencing Data Pipelines
arXiv:2609.28557v1 Announce Type: new Abstract: DNA sequencing pipelines, spanning quality control, alignment, variant calling, and annotation, are now reliably executed by workflow management systems that orchestrate established bioinformatics tools at scale. What remains…
arXiv — cs.AI
Исследования1 мин
Driving Epidemic Models with AI Agents: the Epydemix Agent Framework
arXiv:2609.28692v1 Announce Type: new Abstract: Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is not automatic. Here we introduce the Epydemix Agent Framework, an additive…
arXiv — cs.AI
Исследования1 мин
MeshHeal: Two-Timescale Self-Healing for Gray Failures in Decentralized LLM Agent Networks
arXiv:2609.29015v1 Announce Type: new Abstract: Decentralized LLM-based multi-agent systems coordinate through local interactions, but an agent can remain responsive while its task-solving quality persistently degrades. Such gray failures require protecting current tasks before…
arXiv — cs.AI
Исследования1 мин
Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents
arXiv:2609.28876v1 Announce Type: new Abstract: We introduce Forecast-Dojo, a replayable environment for benchmarking and training LLM forecasting agents. It combines resolved prediction-market questions with dated news, allowing agents to research an event and revisit their…
arXiv — cs.AI
Исследования1 мин
Reinforcement Learning with Verifiable Rewards for Small Search Agents
arXiv:2609.28765v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) performs well on problems with clear rewards, such as mathematics and coding, but whether it also works where the reward is less clear remains open. The reason-over-search…
arXiv — cs.AI
Исследования1 мин
Learned Cross-Task Relationships in Multi-Task Models
arXiv:2609.28776v1 Announce Type: new Abstract: We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted pairwise relationships. This approach improves performance via transfer…
arXiv — cs.AI
Исследования1 мин
DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLMs
arXiv:2609.28570v1 Announce Type: new Abstract: Reinforcement learning (RL) is widely used to sharpen reasoning in multimodal large language models (MLLMs), yet its effect on hallucination is uneven. We trace this to two weak points in the \emph{correction chain} from reward to…
arXiv — cs.AI
Исследования1 мин
TWIST: A Proposed Benchmark for Intervention Quality in Conversational Memory, with a Human-Validated Draft-Alignment
arXiv:2609.28575v1 Announce Type: new Abstract: Long-conversation memory benchmarks increasingly test recall and prompted knowledge updates, and recent work studies evolving user beliefs and memory state. TWIST is a proposed benchmark suite for a complementary, unmeasured…
arXiv — cs.AI
Исследования1 мин
Training Object Permanence in World Models
arXiv:2609.28654v1 Announce Type: new Abstract: Object permanence and solidity are hallmarks of human cognitive priors. Recent studies show that video generation models, a paradigmatic class of current world models, have begun to show emerged reasoning abilities, making them…
arXiv — cs.AI
Исследования1 мин
TW3Cast: A Frozen Router of Lightly Fine-Tuned Foundation Models for Time-Series Forecasting on GIFT-Eval, Selected Entirely on the Training Split
arXiv:2609.28506v1 Announce Type: new Abstract: TW3Cast is a time-series forecasting system that reaches position 3 of 130 entries on the GIFT-Eval benchmark by mean MASE rank, as of 2026-09-14. The two entries above it belong to the leaderboard's agentic category, multi-step…
arXiv — cs.AI
Исследования1 мин
Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
arXiv:2609.28609v1 Announce Type: new Abstract: Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning…
arXiv — cs.AI
Исследования1 мин
Back to the Definition: Estimating Step-Level Advantages via Trajectory Graphs for Agentic Reinforcement Learning
arXiv:2609.28963v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) methods, such as GRPO and its variants, have become a leading paradigm for training reasoning and agentic large language models (LLMs). While their group-normalized advantage estimation is…
arXiv — cs.AI
Исследования1 мин
Beyond Surface Style: Aligning Multi-Turn User Simulators with Behavioral Consistency
arXiv:2609.28690v1 Announce Type: new Abstract: Faithful user simulation is fundamental to building, evaluating, and improving interactive AI at scale. However, plausible individual responses do not ensure that simulated users reproduce the intent evolution and outcomes observed…
arXiv — cs.AI
Исследования1 мин
PAWS: Policy-driven Agentic World Simulation
arXiv:2609.28547v1 Announce Type: new Abstract: Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet datasets for financial multi-agent simulation rarely connect these processes to temporally aligned historical…
arXiv — cs.AI