RESEARCH

→ View original post on X — @gp_pulipaka

Machine Learning Optimizes Base Editors via Single-Round Diversification

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@gp_pulipaka

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11 June 2026 0h26

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round Diversification! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist

→ View original post on X — @nandodf

Few-shot Autoregressive Density Estimation paper referenced by GPT3

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@nandodf

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11 June 2026 0h12

We wrote this: Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. https://
arxiv.org/abs/1710.10304 Soon after, the GPT3 paper had this to say: “Metalearning in language models has been utilized in [RWC+19], though with much more limited results and no

→ View original post on X — @_akhaliq

SCAIL-2: End-to-end character animation unification with context conditioning

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@_akhaliq

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11 June 2026 0h11

SCAIL-2

Unifying Controlled Character Animation with End-to-end In-Context Conditioning pic.twitter.com/bD0lUcHipJ
— AK (@_akhaliq) 10 juin 2026

SCAIL-2 Unification of controlled character animation with end-to-end context conditioning

Shoutouts to multi-agent LLM systems, DSPy, and GRASP papers

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11 June 2026 0h03

shoutouts:
• why multi-agent LLM systems fail? (arXiv:2503.13657) — @mertcemri @melissapan + @istoica05 @matei_zaharia @profjoeyg @adityagp & team • DSPy (arXiv:2310.03714) — @lateinteraction + @hazyresearch lab & co-authors
• GRASP (arXiv:2605.29668) — Jonas Moll,

Gated approach to agent self-modification via forking and testing

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11 June 2026 0h03

less novel, but still very interesting impo is the gated approach to self-modification the agent basically forks itself, propose a patch, run through multiple tests (static/sandbox/diff), and something called a binding held out gate before modificaiton lands

Showcasing controlled self improvement with regime-to-seam approach

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11 June 2026 0h03

i showcase "controlled" self improvement with a novel regime-to-seam approach where failures are categorized and allowed to fix targeted areas of the agent while interesting, it's more to showcase the type of self-modification that's easy to set up with activegraph

ActiveGraph: Auditable Gated Improvement Loop Demonstrated on LongMemEval

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11 June 2026 0h03

in arxiv paper #2, i tackle the last topic from paper #1: @activegraphai as an architectural affordance for self-improving agents "Regimes: An Auditable, Held-Out Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph" i demonstrate this with a reproducible gated

→ View original post on X — @levelsio

Refuting claims that satellite latency blocks AI inference or training

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@levelsio

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11 June 2026 0h01

Next thing people are saying you can't do inference or training because satellite to satellite latency is too big Also mostly wrong: https://
x.com/i/grok/share/3
4b7a26aa6954d5ab02d8bbdf63fd2b2
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