if the guy actually read my work he would see that what i actually said was
– Pure LLMs alone would not get us to AGI (even if they improved in some respects)
– Pure LLMs would continue to struggle with hallucinations and reasoning (they have)
– We need to incorporate elements
RESEARCH
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Pure LLMs Insufficient for AGI: Reasoning and Hallucination Challenges
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Meta’s Muse Spark: Multimodal AI Model with Impressive Reasoning Benchmarks
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Meta Superintelligence Labsjust dropped Muse Spark, their first model after a full nine-month rebuild of their AI stack. the tl;dr (summary) It's a natively multimodal reasoning model that now powers Meta AI. It's competitive on reasoning and multimodal benchmarks, introduces a multi-agent "Contemplating mode," and Meta frames it as step one on a scaling ladder toward "personal superintelligence." Where it's strong: -Multimodal perception and visual reasoning (visual STEM, entity recognition, localization) -Health reasoning, built with input from 1,000+ physicians -Test-time reasoning efficiency, using thinking time penalties to compress reasoning tokens -Contemplating mode hits 58% on Humanity's Last Exam and 38% on FrontierScience Research, putting it in the ballpark of Gemini Deep Think and GPT Pro -Pretraining efficiency: reaches the same capability as Llama 4 Maverick with over 10x less compute Where it's weaker (Meta's own admission): -Long-horizon agentic systems -Coding workflows Key scaling findings: -RL compute scales smoothly with log-linear growth on pass@1 and pass@16 -Multi-agent orchestration scales performance without proportional latency increase -Phase transition behavior on AIME: the model first extends reasoning, then compresses it under length penalties, then extends again for higher accuracy My take: very good model, really surprised what meta offered here. And keep in mind: 99% of all instagram / facebook user dont need an LLM for doing academic reserach but for everyday reasoning. Well done, meta! Chubby♨️ (@kimmonismus) Lol what?! Meta has been cooking! These benchmarks are really freaking good holy!! — https://nitter.net/kimmonismus/status/2041918006779957407#m
→ View original post on X — @kimmonismus, 2026-04-08 16:42 UTC
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CaP Evolution: Agentic Coding and Large Models as Primitives
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Thx Stephen! But quite a bit has changed since 2022…agentic coding is evolving rapidly now and CaP can incorporate large models as primitives. We’re working on extensions and will share updates soon. https://t.co/XHlfRSkME3
— Ken Goldberg (@Ken_Goldberg) 8 avril 2026Thx Stephen! But quite a bit has changed since 2022…agentic coding is evolving rapidly now and CaP can incorporate large models as primitives. We’re working on extensions and will share updates soon. Stephen James (@stepjamUK) 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗰𝗮𝗻 𝗽𝗮𝘀𝘀 𝗹𝗮𝘄 𝗲𝘅𝗮𝗺𝘀. 𝗧𝗵𝗲𝘆 𝗰𝗮𝗻 𝘄𝗿𝗶𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗰𝗼𝗱𝗲. 𝗕𝘂𝘁 𝗮𝘀𝗸 𝘁𝗵𝗲𝗺 𝘁𝗼 𝘄𝗿𝗶𝘁𝗲 𝗮 𝗽𝗿𝗼𝗴𝗿𝗮𝗺 𝘁𝗵𝗮𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗮 𝗿𝗲𝗮𝗹 𝗿𝗼𝗯𝗼𝘁, 𝗮𝗻𝗱 𝘁𝗵𝗲𝘆 𝘀𝘁𝗶𝗹𝗹 𝗳𝗮𝗹𝗹 𝘀𝗵𝗼𝗿𝘁 𝗼𝗳 𝗮 𝗵𝘂𝗺𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝘁. That's the core finding from CaP-X, a new framework from NVIDIA, UC Berkeley, Stanford, and CMU that systematically benchmarks coding agents for robot manipulation. The underlying idea is not new. Code as Policy has been around since 2022/2023, and it is best understood as a modern evolution of Task and Motion Planning – a classical robotics paradigm where engineers manually decompose high-level goals into structured programs combining perception, planning, and control. What has changed is that instead of a human writing that code, a language model does it. It works well when the abstractions are high-level. It degrades significantly when models have to reason at the level human engineers actually work at: raw perception outputs, IK solvers, collision constraints. Here is what the research actually shows: 𝗧𝗵𝗲 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗴𝗮𝗽 𝗶𝘀 𝗿𝗲𝗮𝗹. Performance drops as you move from high-level primitives to low-level APIs. Not because the models lack intelligence, but because the scaffolding disappears. 𝗠𝘂𝗹𝘁𝗶-𝘁𝘂𝗿𝗻 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘀 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗮𝘁 𝗹𝗼𝘀𝘀. Multi-turn feedback with execution traces and structured observations dramatically improves performance. Raw images alone actually hurt. 𝗥𝗟 𝗼𝗻 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗺𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿𝘀 𝘇𝗲𝗿𝗼-𝘀𝗵𝗼𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱. A 7B model fine-tuned with RL in simulation transfers zero-shot to a real Franka robot by reasoning over structured APIs. The takeaway is simple. The bottleneck is not model size. It is the feedback loop, the abstraction layer, and the system around the model. Credit: @letian_fu, Justin Yu, Karim El-Refai, Ethan Kou, @HaoruXue, @DrJimFan, and the full team across @nvidia, @UCBerkeley, @Stanford, and @CMU_Robotics And of course @AGIBOTofficial for providing the hardware in the attached video! What do you think is holding Code as Policy back from production deployment? Paper link in comments. — https://nitter.net/stepjamUK/status/2041878733531849153#m
→ View original post on X — @ken_goldberg, 2026-04-08 16:41 UTC
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Optimism linked to lower dementia risk in long-term study
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No cause and effect established, but the more optimism the less dementia in >9,000 individuals with up to 14 year follow-up https://
agsjournals.onlinelibrary.wiley.com/doi/10.1111/jg
s.70392?utm_campaign=publicity_wly&utm_content=wrh_4_6_26&utm_medium=email&utm_source=muckrack&utm_term=jgs
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Meta’s New Muse Spark Model Delivers Impressive Benchmark Results
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Lol what?! Meta has been cooking! These benchmarks are really freaking good holy!! Alexandr Wang (@alexandr_wang) 1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵 — https://nitter.net/alexandr_wang/status/2041909376508985381#m
→ View original post on X — @kimmonismus, 2026-04-08 16:36 UTC
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5 AI Model Architectures Every Engineer Should Know
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5 #AI Model Architectures Every AI Engineer Should Know by Arham Islam @Marktechpost Learn more: bit.ly/4s5g1pA #LLM #ArtificialIntelligence #GenerativeAI #ML #MachineLearning
→ View original post on X — @ronald_vanloon, 2026-04-08 16:25 UTC
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Domain Experts as Essential AI Partners for Actionable Insights
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Their deep, contextual knowledge of physical processes is the ingredient that turns raw data into actionable insight. They're not obstacles, they're essential partners.
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Meta’s Muse Spark Returns Company to Frontier AI Race
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Meta is back! Muse Spark scores 52 on the Artificial Analysis Intelligence Index, behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Muse Spark is the first new release since Llama 4 in April 2025 and also Meta's first release that is not open weights Muse Spark is a new model from @Meta evaluated on Artificial Analysis. We were given early access by Meta to independently benchmark the model. It is the first frontier-class model from Meta since Llama 4 Maverick was released in April 2025, and notably the first @AIatMeta model that is not being released as open weights. The release follows Meta's reorganization of its AI efforts under Meta Superintelligence Labs, and signals that Meta is re-entering the frontier race after roughly a year of relative quiet. For context, Llama 4 Maverick and Scout scored 18 and 13 respectively on the Artificial Analysis Intelligence Index as non-reasoning models at the time of their release, while Muse Spark scores 52. Muse Spark essentially closes the gap between to the frontier in a single release. The model is not open source and is not yet accessible via an API but Meta has shared they expect this to come soon. Meta is also integrating Muse Spark into their first party products including their Meta AI chat product, Facebook, Instagram and Threads. Key takeaways from our benchmarks: ➤ Muse Spark scores 52 on the Artificial Analysis Intelligence Index, placing it within the top 5 models we have benchmarked. It sits ahead of Claude Sonnet 4.6, GLM-5.1, MiniMax-M2.7, Grok 4.20 and behind Gemini 3.1 Pro Preview, GPT-5.4 and Claude Opus 4.6 ➤ Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5 (110M) ➤ Muse Spark is the second-most capable vision model we have benchmarked. It scores 80.5% on MMMU-Pro, behind only Gemini 3.1 Pro Preview (82.4%) ➤ Muse Spark performs strongly on reasoning and instruction-following evaluations. It scores 39.9% on HLE, trailing only Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (xhigh, 41.6%). The model also achieved 5th highest in CritPT with a score of 11%, an eval that is focused on difficult physics research questions. This is substantially above above Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%) ➤ Agentic performance does not stand out. On GDPval-AA, our evalaution focused on real world work tasks, Muse Spark scores 1427, behind both Claude Sonnet 4.6 at 1648 and GPT-5.4 at 1676, but ahead of Gemini 3.1 Pro Preview at 1320. On On TerminalBench Hard, Muse Spark trails Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro. Muse Spark joins others in achieving a high τ²-Bench Telecom score of 92% Key model details: ➤ Modalities: Multimodal including text and vision input, text output ➤ License: Proprietary, Meta's first frontier model not released as open weights ➤ Availability: No public API at the time of publishing. Meta expects to provide API access soon. Meta has started integration into their first party AI offering Meta AI and inside Facebook, Instagram, and Threads
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DISCO: AI-Designed Enzymes Through Diffusion Co-design
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More sequence-structure co-design brings a happy tear to my eye https://t.co/ScQ0mhsA6e
— Jason Yim (@json_yim) 8 avril 2026More sequence-structure co-design brings a happy tear to my eye Jarrid Rector-Brooks (@jarridrb) What if AI could invent enzymes that nature hasn’t seen? 👩🔬🧑🔬 Introducing 🪩 DISCO: Diffusion for Sequence-structure CO-design 14 rounds of directed evolution and over a year of wet lab work. That's what it took to engineer an enzyme for selective C(sp³)–H insertion, one of the most challenging transformations in organic chemistry. DISCO surpasses this with a single plate. No pre-specified catalytic residues, no template, no theozyme, no inverse folding, just joint diffusion over protein sequence and structure. 📝 Blog: disco-design.github.io/ 📄 Paper: arxiv.org/abs/2604.05181 💻 Code: github.com/DISCO-design/DISC… — https://nitter.net/jarridrb/status/2041893841301860542#m
