Paragon
@paragonaimag.bsky.social
Paragon is an independent magazine tracking only the revolutionary developments shaping AI's future. Daily signals, deep analysis, and a public prediction record we're willing to be scored on. Signal, never noise.
🔴 RoboArchitect automates robot agent harness design from rollouts
RoboArchitect uses a coding model to observe a frozen robot policy’s rollouts, diagnose failures, and iteratively revise its surrounding agent harness. The approach aims to replace h
Source: https://arxiv.org/abs/2610.42141v1
RoboArchitect uses a coding model to observe a frozen robot policy’s rollouts, diagnose failures, and iteratively revise its surrounding agent harness. The approach aims to replace h
Source: https://arxiv.org/abs/2610.42141v1
November 2, 2026 at 7:01 AM
🔴 RoboArchitect automates robot agent harness design from rollouts
RoboArchitect uses a coding model to observe a frozen robot policy’s rollouts, diagnose failures, and iteratively revise its surrounding agent harness. The approach aims to replace h
Source: https://arxiv.org/abs/2610.42141v1
RoboArchitect uses a coding model to observe a frozen robot policy’s rollouts, diagnose failures, and iteratively revise its surrounding agent harness. The approach aims to replace h
Source: https://arxiv.org/abs/2610.42141v1
Google Maps is an AI prediction engine: it reads slow phones, not roads. 99 phones in a wagon once faked a whole traffic jam. Paragon AI MAG explains how your ETA is really made, plus 7 tricks to try tonight. Full video: youtu.be/9BuOVHxgcfk
November 1, 2026 at 12:54 PM
Google Maps is an AI prediction engine: it reads slow phones, not roads. 99 phones in a wagon once faked a whole traffic jam. Paragon AI MAG explains how your ETA is really made, plus 7 tricks to try tonight. Full video: youtu.be/9BuOVHxgcfk
🔴 Controlled benchmark clarifies failures in language-model data attribution
The paper studies why language-model data-attribution methods fail, noting evaluations often vary several dataset properties at once. It introduces CATT, a controlled fact-
Source: https://arxiv.org/abs/2610.40655v1
The paper studies why language-model data-attribution methods fail, noting evaluations often vary several dataset properties at once. It introduces CATT, a controlled fact-
Source: https://arxiv.org/abs/2610.40655v1
November 1, 2026 at 7:01 AM
🔴 Controlled benchmark clarifies failures in language-model data attribution
The paper studies why language-model data-attribution methods fail, noting evaluations often vary several dataset properties at once. It introduces CATT, a controlled fact-
Source: https://arxiv.org/abs/2610.40655v1
The paper studies why language-model data-attribution methods fail, noting evaluations often vary several dataset properties at once. It introduces CATT, a controlled fact-
Source: https://arxiv.org/abs/2610.40655v1
Working on something with AI? Let's connect
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November 1, 2026 at 4:17 AM
Working on something with AI? Let's connect
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The chatbot era is yielding to persistent AI agents. OpenAI's Dots connects to 4,000+ apps; Anthropic's Claude Duet links to 6,000+ services. Both require approval before outside actions. Full video on Paragon AI MAG: youtu.be/f9-04mfO8vo — follow Paragon AI MAG on Bluesky for daily AI insights.
Your AI Now Works While You Sleep: Always-On Agents, Explained | Paragon AI MAG
YouTube video by Paragon AI Mag
youtu.be
October 31, 2026 at 4:47 PM
The chatbot era is yielding to persistent AI agents. OpenAI's Dots connects to 4,000+ apps; Anthropic's Claude Duet links to 6,000+ services. Both require approval before outside actions. Full video on Paragon AI MAG: youtu.be/f9-04mfO8vo — follow Paragon AI MAG on Bluesky for daily AI insights.
🔴 Random logit bias rivals weight perturbation for model gains
Researchers reproduce gains attributed to Neural Thickets, but show they do not require dense expert neighborhoods in weight space. Perturbing one decoder block recovers much improvement
Source: https://arxiv.org/abs/2610.40649v1
Researchers reproduce gains attributed to Neural Thickets, but show they do not require dense expert neighborhoods in weight space. Perturbing one decoder block recovers much improvement
Source: https://arxiv.org/abs/2610.40649v1
October 31, 2026 at 7:02 AM
🔴 Random logit bias rivals weight perturbation for model gains
Researchers reproduce gains attributed to Neural Thickets, but show they do not require dense expert neighborhoods in weight space. Perturbing one decoder block recovers much improvement
Source: https://arxiv.org/abs/2610.40649v1
Researchers reproduce gains attributed to Neural Thickets, but show they do not require dense expert neighborhoods in weight space. Perturbing one decoder block recovers much improvement
Source: https://arxiv.org/abs/2610.40649v1
Building with AI and looking for your people? Let's connect
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October 31, 2026 at 4:16 AM
Building with AI and looking for your people? Let's connect
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AI just found the keys to millions of websites — including ones you use every day. 🔑
October's PHP2Shell story, the $55 TV-box worm study, and the 7 steps that protect you — explained in plain words.
Full video: www.youtube.com/watch?v=hUg3-eb-A3A
Podcast show: redcircle.com/shows/cbce81...
October's PHP2Shell story, the $55 TV-box worm study, and the 7 steps that protect you — explained in plain words.
Full video: www.youtube.com/watch?v=hUg3-eb-A3A
Podcast show: redcircle.com/shows/cbce81...
October 30, 2026 at 2:43 PM
AI just found the keys to millions of websites — including ones you use every day. 🔑
October's PHP2Shell story, the $55 TV-box worm study, and the 7 steps that protect you — explained in plain words.
Full video: www.youtube.com/watch?v=hUg3-eb-A3A
Podcast show: redcircle.com/shows/cbce81...
October's PHP2Shell story, the $55 TV-box worm study, and the 7 steps that protect you — explained in plain words.
Full video: www.youtube.com/watch?v=hUg3-eb-A3A
Podcast show: redcircle.com/shows/cbce81...
ChatGPT isn't thinking — it's predicting the next token through ~175B parameters.
69-second explainer on our YouTube Shorts, full breakdown here: youtu.be/zWNakhjsUp0
69-second explainer on our YouTube Shorts, full breakdown here: youtu.be/zWNakhjsUp0
October 30, 2026 at 7:47 AM
ChatGPT isn't thinking — it's predicting the next token through ~175B parameters.
69-second explainer on our YouTube Shorts, full breakdown here: youtu.be/zWNakhjsUp0
69-second explainer on our YouTube Shorts, full breakdown here: youtu.be/zWNakhjsUp0
🔴 Value of Information Unifies Cheap Retrieval and Memory Credit Assignment
The paper argues retrieval cost and credit assignment in reinforcement learning share one quantity: the value of information. Estimated from archived rollouts with a learned
Source: https://arxiv.org/abs/2610.40030v1
The paper argues retrieval cost and credit assignment in reinforcement learning share one quantity: the value of information. Estimated from archived rollouts with a learned
Source: https://arxiv.org/abs/2610.40030v1
October 30, 2026 at 7:00 AM
🔴 Value of Information Unifies Cheap Retrieval and Memory Credit Assignment
The paper argues retrieval cost and credit assignment in reinforcement learning share one quantity: the value of information. Estimated from archived rollouts with a learned
Source: https://arxiv.org/abs/2610.40030v1
The paper argues retrieval cost and credit assignment in reinforcement learning share one quantity: the value of information. Estimated from archived rollouts with a learned
Source: https://arxiv.org/abs/2610.40030v1
Exploring new AI tools with other builders? Let's connect.
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October 30, 2026 at 4:24 AM
Exploring new AI tools with other builders? Let's connect.
If you are a:
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Let's connect and grow together.
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🟡 Learned attention gates separate instructions from data to resist prompt injection
Researchers propose Heads of Security (HoS), which adds small learned per-head attention gates to enforce instruction–data separation. Tested on Llama-3.3-70B-Instr
Source: https://arxiv.org/abs/2610.37657v1
Researchers propose Heads of Security (HoS), which adds small learned per-head attention gates to enforce instruction–data separation. Tested on Llama-3.3-70B-Instr
Source: https://arxiv.org/abs/2610.37657v1
October 29, 2026 at 6:59 AM
🟡 Learned attention gates separate instructions from data to resist prompt injection
Researchers propose Heads of Security (HoS), which adds small learned per-head attention gates to enforce instruction–data separation. Tested on Llama-3.3-70B-Instr
Source: https://arxiv.org/abs/2610.37657v1
Researchers propose Heads of Security (HoS), which adds small learned per-head attention gates to enforce instruction–data separation. Tested on Llama-3.3-70B-Instr
Source: https://arxiv.org/abs/2610.37657v1
Shipping something with AI? Let's connect
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October 29, 2026 at 4:23 AM
Shipping something with AI? Let's connect
If you are a:
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🟡 Test asks LLMs to discover altered physics laws unaided
Researchers introduce LongChang, a framework creating simulated worlds with altered physics laws. LLMs act as explorers, manipulating objects and building tools, without being told what to in
Source: https://arxiv.org/abs/2610.37649v1
Researchers introduce LongChang, a framework creating simulated worlds with altered physics laws. LLMs act as explorers, manipulating objects and building tools, without being told what to in
Source: https://arxiv.org/abs/2610.37649v1
October 28, 2026 at 6:59 AM
🟡 Test asks LLMs to discover altered physics laws unaided
Researchers introduce LongChang, a framework creating simulated worlds with altered physics laws. LLMs act as explorers, manipulating objects and building tools, without being told what to in
Source: https://arxiv.org/abs/2610.37649v1
Researchers introduce LongChang, a framework creating simulated worlds with altered physics laws. LLMs act as explorers, manipulating objects and building tools, without being told what to in
Source: https://arxiv.org/abs/2610.37649v1
Swapping notes with people building with AI? Let's connect
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October 28, 2026 at 4:14 AM
Swapping notes with people building with AI? Let's connect
If you are a:
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🟡 Byteification retrofits LLMs to count individual letters reliably
Nature reported a technique called byteification that retrofits existing large language models
Source: https://www.nature.com/articles/d41586-026-03059-2?error=cookies_not_supported&code=5a9db367-b0c2-4355-9772-eb1073c0eddd
Nature reported a technique called byteification that retrofits existing large language models
Source: https://www.nature.com/articles/d41586-026-03059-2?error=cookies_not_supported&code=5a9db367-b0c2-4355-9772-eb1073c0eddd
October 27, 2026 at 6:59 AM
🟡 Byteification retrofits LLMs to count individual letters reliably
Nature reported a technique called byteification that retrofits existing large language models
Source: https://www.nature.com/articles/d41586-026-03059-2?error=cookies_not_supported&code=5a9db367-b0c2-4355-9772-eb1073c0eddd
Nature reported a technique called byteification that retrofits existing large language models
Source: https://www.nature.com/articles/d41586-026-03059-2?error=cookies_not_supported&code=5a9db367-b0c2-4355-9772-eb1073c0eddd
Building with AI? Let's grow together
If you are a:
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Drop a comment — I follow back everyone. Let's connect and build together
If you are a:
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Drop a comment — I follow back everyone. Let's connect and build together
October 27, 2026 at 4:15 AM
Building with AI? Let's grow together
If you are a:
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Drop a comment — I follow back everyone. Let's connect and build together
If you are a:
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🟡 First Fully Polynomial-Time Algorithm Learns Positive ReLU Combinations
Researchers give the first fully polynomial-time PAC algorithm for learning positive linear combinations of k ReLUs under Gaussian inputs in L2 loss. It uses polynomial sample
Source: https://arxiv.org/abs/2610.32940v1
Researchers give the first fully polynomial-time PAC algorithm for learning positive linear combinations of k ReLUs under Gaussian inputs in L2 loss. It uses polynomial sample
Source: https://arxiv.org/abs/2610.32940v1
October 26, 2026 at 6:59 AM
🟡 First Fully Polynomial-Time Algorithm Learns Positive ReLU Combinations
Researchers give the first fully polynomial-time PAC algorithm for learning positive linear combinations of k ReLUs under Gaussian inputs in L2 loss. It uses polynomial sample
Source: https://arxiv.org/abs/2610.32940v1
Researchers give the first fully polynomial-time PAC algorithm for learning positive linear combinations of k ReLUs under Gaussian inputs in L2 loss. It uses polynomial sample
Source: https://arxiv.org/abs/2610.32940v1
Want to meet more people building with AI? Let's connect.
If you are a vibe coder, developer, AI founder / indie hacker, freelancer, designer, or just AI-obsessed, drop a comment. I follow back everyone. Let's grow together.
If you are a vibe coder, developer, AI founder / indie hacker, freelancer, designer, or just AI-obsessed, drop a comment. I follow back everyone. Let's grow together.
October 26, 2026 at 4:17 AM
Want to meet more people building with AI? Let's connect.
If you are a vibe coder, developer, AI founder / indie hacker, freelancer, designer, or just AI-obsessed, drop a comment. I follow back everyone. Let's grow together.
If you are a vibe coder, developer, AI founder / indie hacker, freelancer, designer, or just AI-obsessed, drop a comment. I follow back everyone. Let's grow together.
🔴 Direct Pass@k optimization prunes reasoning candidates while preserving diversity
Researchers propose directly optimizing Pass@k for thought pruning, balancing candidate quality and diversity when discarding partial reasoning traces early to save
Source: https://arxiv.org/abs/2610.31667v1
Researchers propose directly optimizing Pass@k for thought pruning, balancing candidate quality and diversity when discarding partial reasoning traces early to save
Source: https://arxiv.org/abs/2610.31667v1
October 25, 2026 at 6:58 AM
🔴 Direct Pass@k optimization prunes reasoning candidates while preserving diversity
Researchers propose directly optimizing Pass@k for thought pruning, balancing candidate quality and diversity when discarding partial reasoning traces early to save
Source: https://arxiv.org/abs/2610.31667v1
Researchers propose directly optimizing Pass@k for thought pruning, balancing candidate quality and diversity when discarding partial reasoning traces early to save
Source: https://arxiv.org/abs/2610.31667v1
Building something with AI? Let's connect.
Vibe coders, developers, AI founders, indie hackers, freelancers, designers — if you're AI-obsessed, I want to meet you.
Drop a comment — I follow back everyone.
Vibe coders, developers, AI founders, indie hackers, freelancers, designers — if you're AI-obsessed, I want to meet you.
Drop a comment — I follow back everyone.
October 25, 2026 at 4:22 AM
Building something with AI? Let's connect.
Vibe coders, developers, AI founders, indie hackers, freelancers, designers — if you're AI-obsessed, I want to meet you.
Drop a comment — I follow back everyone.
Vibe coders, developers, AI founders, indie hackers, freelancers, designers — if you're AI-obsessed, I want to meet you.
Drop a comment — I follow back everyone.
Shipping with AI? I'd love to meet you.
If you're a vibe coder, developer, AI founder, indie hacker, freelancer, designer — or just AI-obsessed — let's connect and grow together.
Drop a comment — I follow back everyone.
If you're a vibe coder, developer, AI founder, indie hacker, freelancer, designer — or just AI-obsessed — let's connect and grow together.
Drop a comment — I follow back everyone.
October 24, 2026 at 5:32 PM
Shipping with AI? I'd love to meet you.
If you're a vibe coder, developer, AI founder, indie hacker, freelancer, designer — or just AI-obsessed — let's connect and grow together.
Drop a comment — I follow back everyone.
If you're a vibe coder, developer, AI founder, indie hacker, freelancer, designer — or just AI-obsessed — let's connect and grow together.
Drop a comment — I follow back everyone.
Looking to #connect with people building with AI 🤝
Vibe coders • Developers • AI founders • Indie hackers • Freelancers • The AI-obsessed
Comment below — I follow back everyone. Let's grow together.
Vibe coders • Developers • AI founders • Indie hackers • Freelancers • The AI-obsessed
Comment below — I follow back everyone. Let's grow together.
October 24, 2026 at 11:41 AM
Looking to #connect with people building with AI 🤝
Vibe coders • Developers • AI founders • Indie hackers • Freelancers • The AI-obsessed
Comment below — I follow back everyone. Let's grow together.
Vibe coders • Developers • AI founders • Indie hackers • Freelancers • The AI-obsessed
Comment below — I follow back everyone. Let's grow together.
🔴 OLMo-3 Checkpoints Question Whether RLVR Drives Instrumental Convergence
Using OLMo-3 checkpoints, authors test whether reinforcement learning with verifiable rewards increases expression of instrumental convergence, such as self-preservation, res
Source: https://arxiv.org/abs/2610.31624v1
Using OLMo-3 checkpoints, authors test whether reinforcement learning with verifiable rewards increases expression of instrumental convergence, such as self-preservation, res
Source: https://arxiv.org/abs/2610.31624v1
October 24, 2026 at 6:59 AM
🔴 OLMo-3 Checkpoints Question Whether RLVR Drives Instrumental Convergence
Using OLMo-3 checkpoints, authors test whether reinforcement learning with verifiable rewards increases expression of instrumental convergence, such as self-preservation, res
Source: https://arxiv.org/abs/2610.31624v1
Using OLMo-3 checkpoints, authors test whether reinforcement learning with verifiable rewards increases expression of instrumental convergence, such as self-preservation, res
Source: https://arxiv.org/abs/2610.31624v1
🔴 Skeptical: one personal site reports Ginkgo plugged GPT-5 into its cloud lab in February — 36,000 experiments across 6 cycles, the AI designing and learning from real wet-lab work. No links to Ginkgo/OpenAI data or protocols. Scale isn’t proof. That’s Paragon. 🔍
miguelguerrero.eu/article-ai-f...
miguelguerrero.eu/article-ai-f...
The AI Scientist: From Research Assistants to Autonomous Discovery
Mapping the AI for science landscape — from Elicit to FutureHouse's autonomous scientists to the Genesis Mission. Promethean promise meets Pandora's caution.
miguelguerrero.eu
October 23, 2026 at 7:16 AM
🔴 Skeptical: one personal site reports Ginkgo plugged GPT-5 into its cloud lab in February — 36,000 experiments across 6 cycles, the AI designing and learning from real wet-lab work. No links to Ginkgo/OpenAI data or protocols. Scale isn’t proof. That’s Paragon. 🔍
miguelguerrero.eu/article-ai-f...
miguelguerrero.eu/article-ai-f...