Google DeepMind、デミス・ハサビスが議長へ就任し経営体制を再編

a quiet day.

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AI Twitter Recap

Google DeepMind Leadership Reshuffle and the Discovery Loop Spinout

• A major Google AI reorg landed alongside a high-profile founder exodus: Demis Hassabis is moving to Chair of Google DeepMind and Chief Scientist of Alphabet, explicitly stepping back from day-to-day GDM operations to focus on long-term strategy, AGI, and science. Koray Kavukcuoglu takes operational control as SVP of DeepMind, overseeing Gemini, frontier research, and product/dev teams. The subtext from the ecosystem was clear: this is being read as both a governance reset and an attempt to sharpen product execution around Gemini.

• At the same time, Discovery Loop launched with one of the strongest founding teams in AI infrastructure/research: Jeff Dean, Sanjay Ghemawat, Oriol Vinyals, and Quoc Le are founding Discovery Loop, a Public Benefit Corporation aimed at automating machine learning, science, and engineering. Dean also shared that Radical Ventures and Khosla Ventures are leading the seed round, with participation from Lightspeed, Kleiner Perkins, Doerr Capital, and Alphabet. The technical read-through is important: rather than another general-purpose model startup, this is explicitly targeting autoresearch / automated discovery loops over scientific and engineering workflows.

• Why engineers cared: the market reaction wasn’t just “big names left Google.” It was that several people most associated with Google’s deep infra, model-building, and research execution stack are now pursuing a startup centered on automated science. Commentary from Nat Friedman’s orbit via Nathan Lambert, Andrew Ng, and others framed it as a historical inflection point for Google’s AI efforts and a strong signal that AI-for-science is becoming a primary frontier, not a side quest.

Meta’s Muse Spark 1.2 and Muse Code Push Into the Coding-Agent Race

• Meta shipped both a new coding-focused model and its first serious terminal agent harness: Meta AI, Alexandr Wang, and Fink announced Muse Spark 1.2 and Muse Code (beta). The positioning is notable: Meta says the model and harness were co-trained together, aiming for better first-attempt tool use, cleaner plan execution, and less reprompting. The harness uses persistent specialized agents, parallel sub-agents in isolated worktrees, and a local event log for crash recovery and long-running task durability.

• Benchmarks suggest Meta is now in the serious conversation for coding agents: external summaries highlighted 82.9% on Terminal-Bench 2.1 and 59.3% on DeepSWE 1.1, with Artificial Analysis placing Muse Spark 1.2 at 54 on its Intelligence Index, effectively tied with some leading US models below the very top tier. Multiple tweets emphasized the model’s cost-performance: AA notes unchanged pricing at $1.25 / $4.25 per 1M input/output tokens with discounted cache hits, while community members pointed out very aggressive contributor pricing and unusually fast throughput.

• The technical theme is harness-model co-design: this launch wasn’t read merely as “Meta released another model.” The stronger takeaway is that frontier performance increasingly depends on the pairing of model + harness. Muse Code’s architecture—persistent context, fan-out sub-agents, validation loops, multimodal inputs, and long session durability—puts Meta squarely into the same design space as Claude Code, Codex, Devin-like systems, and custom internal agent runners. Several observers explicitly called out that Meta has now “joined the harness conversation,” not just the raw-model race.

Open-Source Agent Harnesses and Benchmarks Are Becoming a First-Class Battleground

• Prime Intellect’s Prime Agent was one of the most technically interesting harness releases: Prime Intellect introduced Prime Agent, an open-source, open-license harness built around RLM-native programmatic tool calling, persistent multi-agent orchestration, and a self-improving continual harness. A striking design choice: the harness reportedly centers on a single persistent IPython REPL, with tool creation and sub-agent spawning expressed programmatically rather than through a fixed menu of tools. This is a meaningful shift toward treating the harness as an executable substrate instead of a prompt wrapper.

• Benchmarks are increasingly isolating harness effects from backbone effects: DataSpace evaluated data agents over 410 cross-language tasks, 7,439 artifacts, and 15.01 GB across structured and unstructured formats; the standout result was that, with the same backbone, switching harnesses moved accuracy by 15.36 points. Similarly, Boundary-Bench was open-sourced to test agents under realistic enterprise constraints like EDR, SASE, and DLP, arguing that public leaderboards often benchmark in settings no real security team would permit.

• Skill accumulation remains unresolved: ContinualSkillBench tested whether explicit skill libraries actually help multi-step agents. The result is nuanced: sequential execution and prior context help, but explicit skill libraries often only match plain in-context adaptation. In other words, agents are learning from prior interaction, but compressing experience into reusable abstractions is still an open problem.

• DSPy is pushing optimization above prompt level: DSPy/Flex coverage highlighted that GEPA can now optimize program code, not just prompts, with one cited task moving from 90% to 95% accuracy while using 75% fewer LLM calls. That matters because the optimization surface for agent systems is broadening from prompt tokens to control logic, program structure, and search strategy.

Research Agents, Interpretability, and Applied Scientific Reasoning

• Elicit launched a Research Agent explicitly aimed at high-stakes decision support: Elicit positioned its new system as an AI environment for evidence gathering, tradeoff reasoning, and decision support, with both product and API access. The most substantive technical claim came via BioDecisionBench, a benchmark for reasoning failures in pharma decisions; Elicit reports 76.7% coverage of key considerations in “Smartest” mode versus 68.8% for Claude Opus 5 Max. Andreas Stuhlmüller framed the key idea as “verify process, not outcomes” for domains where outcome signals are delayed or unobservable.

• Goodfire shipped interpretable biology tooling rather than another generic platform claim: Goodfire introduced MAPS, a Mechanistic Atlas of Protein Sequences, explaining 2.1 million genetic variants and not just whether a mutation is harmful, but why. They also connected it to their research platform Silico for replication and extension. This stood out because it grounds interpretability in a specific scientific task: mechanistic reasoning over protein-property effects and rare disease hypotheses.

• Applied scientific automation keeps broadening: Sakana AI described integrating its AI Scientist and AB-MCTS frameworks with Daiwa Securities to automate financial data analysis with user-feedback loops, while Archer’s aviation foundation model efforts and discussion around automated scientific discovery reinforced that labs are increasingly aiming beyond chat and coding into domain-specific research stacks.

Infra, Security, and Enterprise Controls for Agents

• Cloudflare’s “Agents Week” drop was one of the denser infra announcements: Ashley Peacock’s summary covered the open-sourcing of Cloudflare OS, an internal agent workspace with isolated runtimes, enterprise grounding, and governance layers; new identity-aware AI Gateway controls for spend and routing; WriteGuard for fine-grained MCP action control and auditability; and a broader Agent Access Model proposal for task-scoped credentials and shrinking permissions. The important pattern is the move from “agents can call tools” to agents as governed enterprise principals.

• Other infra releases reinforced the same trend: turbopuffer shipped sharding in beta for indexing up to 256 TB in a single namespace; Cognition launched Devin Outposts on Vercel Sandbox with microVM isolation, VPN connectivity, and snapshot-resume; and Hugging Face/TRL + OpenEnv published a concrete recipe for RL-training coding agents in remote sandboxes, including token/logprob capture and reward verification over hidden tests.

• Enterprise cost and access control are becoming product categories of their own: LangSmith’s customer-specific gateway controls and Sapiom’s one-key billing/runtime abstraction for multi-provider agents both target a very practical pain point: agents now incur costs across model APIs, communications, scraping, and tool vendors mid-run, so budgets and identity need to be enforced at the orchestration layer.

Top tweets (by engagement)

• Discovery Loop launch: Jeff Dean announces Discovery Loop, a public-benefit startup to automate ML, science, and engineering, with Oriol Vinyals, Quoc Le, and Sanjay Ghemawat.

• Google DeepMind leadership change: Demis Hassabis steps into Chair of GDM and Chief Scientist of Alphabet, with Koray Kavukcuoglu taking day-to-day control.

• Meta’s coding-agent release: Muse Code beta and Muse Spark 1.2 mark Meta’s strongest move yet into coding agents.

• Prime Agent release: Prime Intellect’s open-source RLM harness drew strong attention for its programmable, self-improving design.

• Open-model regulation debate: Clement Delangue’s “don’t regulate steel, crash-test cars” framing sparked substantial discussion over how to regulate open weights vs APIs vs applications.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen 3.8 27B Roadmap Signals

• Qwen Developers' responses from their recent Twitter/X AMA (Activity: 472): The image is a non-technical promotional graphic for Qwen’s Twitter/X AMA, showing the Qwen logo, “ASK ME ANYTHING!”, and a bear mascot; it mainly contextualizes the post as a recap of QwenDevs’ public Q&A rather than conveying technical results itself. The AMA responses hint at an upcoming Qwen 3.8 27B release with a “pretty huge jump,” Qwen 3.8 MoE scale of 2.4T total / 95B active parameters, architecture “similar to 3.5,” heavy RL post-training, hierarchical long-video memory for 100+ hours, and quantization guidance: use QAT or keep attention QKV/output projections in 16-bit while quantizing FFN to 4-bit. Image Commenters were skeptical of the AMA, calling many answers vague or evasive, especially around whether a 122B model or additional small/mid-size releases will ship. There was also some frustration that questions focused on CLI/harness tooling instead of deeper model details.

Commenters noted that the AMA responses were largely non-technical and repetitive, with several answers reduced to variants of “Keep the requests coming… we’ll use them to help prioritize future updates” rather than concrete roadmap, benchmark, or implementation details. The most specific technical frustration was that questions about a potential Qwen 122B model appeared to be dodged, while discussion seemed constrained to the 27B model size.

• More Qwen 3.8 sizes coming (Activity: 2002): The image is a screenshot of an X/Twitter reply where Shuai Bai says the Qwen team is “still working through the lineup for more sizes and architectures” after being asked about a possible Qwen 3.8 35A3B model. Technically, it is only a roadmap hint—no benchmarks, parameter counts, release dates, or architecture details are confirmed—but it suggests more Qwen 3.8 variants may follow the already referenced 27B model. Comments are mostly hype/speculation, especially requests for a much larger 122B model and enthusiasm for additional sizes. One commenter argues Qwen should have announced the broader lineup earlier.

Commenters are specifically hoping the Qwen 3.8 expansion includes larger dense/MoE-class checkpoints around 122B parameters and a smaller 9B tier, implying demand for both high-capability local/hosted inference and more accessible deployment sizes. There is also explicit interest in a Qwen 3.8 Coder variant, suggesting users expect the release cadence to extend to code-specialized fine-tunes rather than only general chat models.

2. llama.cpp Local Runtime Upgrades

• Qwen3-TTS voice cloning is now in mainline llama.cpp — the old demo finally became real support (Activity: 460): The image is a technical Qwen3-TTS infographic, not a meme: it illustrates the “Clone Design” workflow where short reference audio plus text prompts are converted into cloned or style-controlled speech, and shows an architecture with Qwen3 LM, codec embeddings, an MTP module, and a streaming codec decoder. In context, the post highlights that this capability is now merged into mainline llama.cpp via llama-tts, currently targeting Qwen3-TTS-12Hz-1.7B-Base GGUF with speaker references from WAV/MP3 and multilingual output. Image Commenters are interested in practical voice-cloning use cases and broader llama.cpp audio support, especially compared with existing implementations like qwen3-tts.cpp, faster-qwen3-tts, and audio.cpp. An audio.cpp maintainer specifically welcomed fair benchmarks to identify optimization opportunities.

audio.cpp maintainer shared RTX 5090 CUDA benchmarks for Qwen3-TTS 12Hz 1.7B Base Q8 GGUF using audiocpp_cli --metrics with --threads 8. Across five clone requests of ~300 chars, average RTF was 0.130437 / 7.67x realtime with full reference and perf off, 0.129289 / 7.73x with flash_attention, and 0.121632 / 8.22x using a 2s reference plus flash_attention, suggesting only marginal gain from flash attention but a measurable speedup from shorter reference audio.

A commenter noted audio.cpp has had mainline support for weeks and claims support for 50+ audio models, including audio-to-text, text-to-audio, voice cloning, and GGUF quantizations such as Q8 and fp16. Another user compared the new llama.cpp support with existing workflows using qwen3-tts.cpp on ROCm and faster-qwen3-tts