Turing Post @TheTuringPost
On X we surface the AI research that matters and explain the ideas behind it. In the newsletter, we connect the dots between AI’s past, present, and future ⬇️ turingpost.com Join over 102,000 readers Joined June 2020-
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AutoScientists – a research lab made of agents @Harvard researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain
Must-read research of the week ▪️ Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses ▪️ Rethinking Continual Experience Internalization for Self-Evolving LLM Agents ▪️ GrepSeek: Training Search Agents for Direct Corpus Interaction ▪️ WALL-WM: Carving World Action Modeling at the Event Joints ▪️ On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters ▪️ Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution ▪️ KVarN: Variance-Normalized KV-Cache Quantization Mitigates Error Accumulation in Reasoning Tasks ▪️ Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning ▪️ Self-Distilled Policy Gradient ▪️ OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents ▪️ MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery ▪️ OCC-RAG: Optimal Cognitive Core for Faithful Question Answering Find the full list and the most important AI news of the week here: turingpost.com/p/continual-le…
"Businesses are built on love and trust. Trustworthiness is the most underrated asset in all of business." @ericries, entrepreneur and author of The Lean Startup and Incorruptible, on the real competitive advantages across industries Watch the full interview on how AI is disrupting capitalism – and what comes next: youtube.com/watch?v=ypHhsC…
A true research highlight Researchers from @Harvard, @MIT and others built an LLM agent "economy" Their system is called Economy of Minds and it replaces orchestration with economic incentives. Agents: → bid in auctions for the right to act → pay each other → gain virtual wealth from rewards and spend it → mutate if successful → and go bankrupt if ineffective ▪️ And surprisingly, coordination emerges automatically. Agents specialize, cooperate, and develop multi-step reasoning strategies without being explicitly told how to work together. Why? The idea comes from Friedrich Hayek → Markets coordinate knowledge without central planning. Now we're seeing the same principle applied to AI. That's why we need to start building better economies instead of smarter manager agents. There’s another proof that this path is effective – the large performance gains of Economy of Minds: • Math reasoning: 15.9% → 57.0% • Financial research: 45.0% → 60.0% • Scientific research: 5.0% → 20.0% • Accelerator design: 2× better energy-delay tradeoff • Distributed systems: 657 vs 930 optimization cost
.@axiommathai has pushed formal verification beyond pure math into economics. → It announced EconLib, a Lean-based library for economic theory ▪️ The project starts with a formalization of Robert Aumann’s famous "agreeing to disagree" theorem. But AxiomProver didn't just verify the proof. It uncovered a hidden assumption: requiring every information cell to have positive probability makes Bayesian updates possible, and it also quietly forces the information structure to be countable. In other words, the system was doing "assumption accounting," exposing exactly which assumptions make the result work. ▪️ The team also formalized the Monderer–Samet p-belief extension, where disagreement becomes possible but quantitatively bounded. The project aims to become a Mathlib-style foundation for game theory, Nash equilibria, auction theory, information economics, and prediction-market logic.
this is the biggest wake-up call to protect and nourish open source AI if you don't build out sovereign and independent models+infra closed labs will patronize you to an insulting degree
mythos will be bad ON PURPOSE on ai "frontier llm research" tasks, this is very very sad for the research community also the fact that this is un purpose not visible to the user is crazy
Coase’s question "When is it cheaper to stop bidding and just create a firm?" was tested in this paper a little bit. Researchers added a full-tool generalist agent, but it still didn’t dominate. Specialists keep winning because they're more precise locally. For now Hayek wins the experiment, but Coase is probably the next boundary to test fully
Paper: arxiv.org/abs/2606.02859… Project page: zhentingqi.github.io/internal/proje… GitHub: github.com/zhentingqi/EoM
Great news! There'll be a free one-day event – AWS Summit NYC on June 17 → aws.amazon.com/events/summits… We recommend to join it for: → 200+ expert-led sessions through hands-on workshops and live demos → latest AI, cloud infrastructure and modernization innovations → business empowerment tips → practical insights from leading voices like Dr. Swami Sivasubramanian Register to save your spot: aws.amazon.com/events/summits… @awscloud
Specialised autonomous agent loops are the next big thing esp. in life sciences and research domains.
AutoScientists – a research lab made of agents @Harvard researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel,
@justgrm Exploration keeps going, and there'll always be many options that work for different cases
15 Policy Optimization and Preference Optimization techniques important in 2026 ▪️ GRPO ▪️ DPO ▪️ REINFORCE++ ▪️ DAPO (Dynamic sAmpling) ▪️ Dr. GRPO ▪️ GSPO (Group Sequence) ▪️ DHPO (Dynamic Hybrid) ▪️ EP-GRPO (Entropy-Progress Aligned) ▪️ TR-GRPO (Token-Regulated) ▪️ DPPO (Dynamic Pruning) ▪️ ARPO (Agentic Reinforced) ▪️ VPO (Vector PO) ▪️ InSPO (Intrinsic Self-reflective Preference Optimization) ▪️ TI-DPO (Token-Importance Guided DPO) ▪️ RAPPO (Reliable Alignment for Preference PO) Save this list as a quick reference for the most relevant policy optimization methods in 2026: turingpost.com/p/reasoning-rl…
this was a really fun piece to write for @TheTuringPost. i'm very grateful for the opportunity to partner with @Kseniase_ on it. start authoring outcomes!
OpenProse – an open-source "logical English" language that turns your agentic workflows into reusable agent programs. It runs inside the coding agent you already use: Claude Code, Codex, OpenCode, Hermes, Pi – and gives it a structured contract to follow. → The key idea – the
Santiago @svpino
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Christoph Molnar 🦋... @ChristophMolnar
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Pranav Rajpurkar @pranavrajpurkar
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Caglar @caglar_ee
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eli @elipromisel
158 Followers 984 Following advising execs on startups, VC, emerging tech, innovation, fintech, mobility, consumer, and more…| now @scnfoundry | prev @usc @hkust @unibocconi
Rambo @pumpmaximalist
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adam @deer_adam
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Andrew Ng @AndrewYNg
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Google DeepMind @GoogleDeepMind
1.5M Followers 279 Following The engine room of @Google. Building AI safely and responsibly to solve the world’s most complex problems. Join us: https://t.co/jUHQA27iBL
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270 Followers 407 Following Product Lead @ LinkedIn Trust, xMeta head of GenAI Red Team. Exploring AI, Agents, AI Societal Impact, AI Trust, Safety & Security.
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Nav Toor @heynavtoor
142K Followers 248 Following Helping you master AI daily with step-by-step AI guides, latest news, & practical tools • DM for Collabs
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885 Followers 1K Following In principle, this intends to be a logbook of my readings. I'm a computer science professor with a PhD in physics. Also a sophomore Med student.
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281 Followers 1K Following SWE focusing on AI/ML | MSc AI Board Game Designer @HexPoker
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