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5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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human-AI collaboration

topic7 events
papersTODAY 04:00 UTC

Mind2Dialogue Framework Trains Language Models to Model User Mental States

A new arXiv paper introduces Mind2Dialogue, a method that trains language models to be more aware of the people they interact with by simulating users' mental states during training. The authors frame the problem as a supervision gap: models need signals about human internal states, which are rarely available in standard dialogue data. The approach targets long-term collaboration in learning, reasoning, and decision-making tasks.

llm/dialogue/user-modeling/training
papersSEP 12 04:00 UTC

AI Soccer Analyst Tool Supports Verifiable Human-AI Soccer Data Analysis

A new arXiv paper introduces AI Soccer Analyst, a system designed to help sports analysts work with soccer data through a stage-aware, verifiable human-AI collaboration workflow. The authors argue that while large language models make programming easier for analysts, prompt-to-report pipelines can hide the reasoning steps and supporting evidence behind conclusions. Their approach aims to keep the analysis process transparent and checkable by structuring collaboration around distinct stages.

research/agents/human-ai-collaboration/sports-analytics
papersSEP 12 04:00 UTC

arXiv paper proposes evaluating AI agents on resilience across repeated interactions

A new arXiv preprint argues that measuring whether an agent completes a single task is insufficient for judging fitness in long-running deployments. The authors propose evaluating agents on how well they hold up as challenges accumulate, including shifting conditions, repeated interactions, and reliance on human collaborators in shared workflows. The work frames resilience and considerate participation as dimensions that need dedicated benchmarks.

agents/evaluation/benchmarks/arxiv
papersSEP 10 04:00 UTC

MOSAIC: Open-Source Interface for Mixing AI Agents Across Paradigms

Researchers have released MOSAIC, an open-source platform that allows AI agents built on different decision-making paradigms to operate side by side in the same environment. A unified agent-level interface supports both cross-paradigm agent mixing and human-AI collaboration. The design also makes it possible to compare different agent paradigms fairly under identical conditions, something existing infrastructure could not do.

agents/open-source/human-ai-collaboration
papersSEP 10 04:00 UTC

New arXiv paper explores co-creating life goals from everyday computer use

A preprint cross-listed in arXiv's AI and computational linguistics categories examines how software could infer what a person is ultimately trying to achieve based on their routine computer activity. Leveraging recent progress in user modeling, the authors describe an approach in which AI systems and users jointly articulate life goals rather than the machine simply automating individual tasks. The v2 announcement indicates a revised version of the paper.

user-modeling/human-ai-interaction/research
papersSEP 10 04:00 UTC

SocialRL trains LLMs' social intelligence with multi-turn reinforcement learning and reward design

A new arXiv paper presents SocialRL, a framework that applies multi-turn reinforcement learning together with carefully designed rewards to sharpen how language models handle social context in extended conversations. The work targets agents' capacity to read situational cues, infer speaker intent, and adjust behavior over sustained dialogue, with the goal of more effective and trustworthy human-AI collaboration.

reinforcement-learning/agents/dialogue/human-ai-interaction
papersSEP 10 04:00 UTC

Pairit Introduces a Platform for Live Experiments on Human-AI Collaboration

Researchers have unveiled Pairit, a programmable platform for running live experiments in which human participants and AI systems coordinate, delegate tasks, and make decisions together. The tool is aimed at studying organizational design questions as AI becomes part of real-time group work, supporting sessions that mix human-to-human and human-AI interaction.

human-ai-collaboration/research-tools/platform/experiments