Eryk Salvaggio arxiv.org
Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs
ArXiv link for Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs
Posts to AI papers, or people sharing explainer threads of research. Be mindful of hype and aware that many are pre-prints (ie, not peer reviewed). To guarantee your post shows up here use #papersky. Created by @eryk.bsky.social.
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Like a beehive refining nectar into pure honey, SimpleQA Verified distills complex language models into clear measures of factual accuracy. This 1,000-prompt benchmark filters through noise and bias, offering a clearer path forward with Gemini 2.
🔗 Source: arxiv.org/abs/2509.07968v1
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CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models
Read more: arxiv.org/html/2509.15027v1
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The novel EVOREASONER framework enhances LLMs' dynamic question-answering capabilities by incorporating temporal reasoning and knowledge graph evolution, markedly improving performance and enabling smaller models to compete with vastly larger ones. arxiv.org/abs/2509.15464
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Top 30 most popular arXiv papers in the last 30 days.
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Links: abs, pdf
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arxiv.org
Virtual Agent Economies
The rapid adoption of autonomous AI agents is giving rise to a new economic layer where agents transact and coordinate at scales and speeds beyond direct human oversight. We propose the "sandbox econo...
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@shuberfuber.bsky.social
about 1 hour agoNah.
It's all because of one paper.
arxiv.org/abs/1706.03762
The entire explosion of modern generative AI is because of that paper.
arxiv.org
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It is perhaps not fully acknowledged, but neither is it unknown.
arxiv.org/pdf/2503.22919
arxiv.org
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This study presents Img2CAD, enabling the reverse engineering of 3D CAD models from images via vision-language models and attribute prediction. It transforms manufacturing editing, allowing intuitive modifications and showcasing potential for real-world applications. arxiv.org/abs/2408.01437
arxiv.org
Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization
ArXiv link for Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization
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Paper2Agent: Stanford Reimagining Research Papers as Interactive AI Agents arxiv.org/abs/2509.06917 (news.ycombinator.com/item…)
arxiv.org
Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents
We introduce Paper2Agent, an automated framework that converts research papers into AI agents. Paper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery. Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant. It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (MCP) server, then iteratively generates and runs tests to refine and robustify the resulting MCP. These paper MCPs can then be flexibly connected to a chat agent (e.g. Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper. We demonstrate Paper2Agent's effectiveness in creating reliable and capable paper agents through in-depth case studies. Paper2Agent created an agent that leverages AlphaGenome to interpret genomic variants and agents based on ScanPy and TISSUE to carry out single-cell and spatial transcriptomics analyses. We validate that these paper agents can reproduce the original paper's results and can correctly carry out novel user queries. By turning static papers into dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for knowledge dissemination and a foundation for the collaborative ecosystem of AI co-scientists.
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🚨Our NeurIPS 2025 competition Mouse vs. AI is LIVE!
We combine a visual navigation task + large-scale mouse neural data to test what makes visual RL agents robust and brain-like.
Top teams: featured at NeurIPS + co-author our summary paper. Join the challenge!
Whitepaper: arxiv.org/abs/2509.14446
arxiv.org
Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness and Neural Alignment
Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environ...
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🚨Our NeurIPS 2025 competition Mouse vs. AI is LIVE!
We combine a visual navigation task + large-scale mouse neural data to test what makes visual RL agents robust and brain-like.
Top teams: featured at NeurIPS + co-author our summary paper. Join the challenge!
Whitepaper: arxiv.org/abs/2509.14446
arxiv.org
Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness and Neural Alignment
Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environ...
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Submit your model. Compete against mice.
Which model architectures solve the task, and which find brain-like solutions?
Let us uncover what it takes to build robust, biologically inspired agents!
Read the whitepaper: arxiv.org/abs/2509.14446
Explore the challenge: robustforaging.github.io
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Submit your model. Compete against mice.
Which model architectures solve the task, and which find brain-like solutions?
Let us uncover what it takes to build robust, biologically inspired agents!
Read the whitepaper: arxiv.org/abs/2509.14446
Explore the challenge: robustforaging.github.io
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LLMs Unemployment
"Occupations more exposed to LLMs experience notable earnings gains ... we find no systematic changes in unemployment, which remains relatively low for exposed occupations both before and after LLM adoption."
#ai #science #llm #employ #economics #policy
arxiv.org/abs/2509.15510
arxiv.org
The (Short-Term) Effects of Large Language Models on Unemployment and Earnings
Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. This paper examines the...
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Large Language Models for Security Operations Centers: A Comprehensive Survey
Read more: arxiv.org/html/2509.10858v1
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A study introduces REFER, a frequency-based prompting method that enhances fairness in summarising opinions by language models. Results show notable improvements in viewpoint representation, suggesting this technique could reshape AI's approach to bias in opinions. arxiv.org/abs/2509.15723
arxiv.org
REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
ArXiv link for REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
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Paper2Agent: Stanford Reimagining Research Papers as Interactive AI Agents
arxiv.org/abs/2509.06917
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Paper2Agent: Stanford Reimagining Research Papers as Interactive AI Agents
#HackerNews
arxiv.org/abs/2509.06917
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Good one on various disinfo around autism in Latin America, including Cuba:
arxiv.org/abs/2504.01991
arxiv.org
Disinformation about autism in Latin America and the Caribbean: Mapping 150 false causes and 150 false cures of ASD in conspiracy theory communities on Telegram
How do conspiracy theory communities in Latin America and the Caribbean structure, articulate, and sustain the dissemination of disinformation about autism? To answer this question, this research inve...
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Paper2Agent: Stanford Reimagining Research Papers as Interactive AI Agents
L: arxiv.org/abs/2509.06917
C: news.ycombinator.com/item…
posted on 2025.09.22 at 18:02:01 (c=0, p=3)
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📢📢 Exciting news!
Our paper, "Exploring Diffusion Transformer Designs via Grafting," has been accepted as an Oral at #NeurIPS2025, with only 77 out of 21k submissions receiving this honor.
📄Paper: arxiv.org/abs/2506.05340
🌎Website: grafting.stanford.edu
🧑🏻💻Code: github.com/keshik6/graf...
grafting.stanford.edu
Exploring Diffusion Transformer Designs via Grafting
Exploring Diffusion Transformer Designs via Grafting
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@cosmos4u.bsky.social
about 3 hours agoA Post-Starburst Pathway for the Formation of Massive Galaxies and Black Holes at z>6: arxiv.org/abs/2409.07113 -> Supermassive Black Holes Shine in the Most Distant "Dying" Massive Galaxies: subarutelescope.org/en/ne…. - New Evidence for Rapid Co-evolution.
arxiv.org
A Post-Starburst Pathway for the Formation of Massive Galaxies and Black Holes at z>6
Understanding the rapid formation of supermassive black holes (SMBHs) in the early universe requires insight into stellar mass growth in host galaxies. Here, we present NIRSpec rest-frame optical spec...
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