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Machine Heart

Professional AI media and industry service platform

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Latest from Machine Heart

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Machine Heart
Machine Heart
Aug 30, 2026 · Artificial Intelligence

How Large Discovery Models Enable AI to Design the Next Experiment

The Large Discovery Model (LDM) combines a generative foundation model with a Gaussian‑process reward model to create fast and slow learning loops that iteratively propose, evaluate, and refine experimental designs across neural‑network training, antibody engineering, and small‑molecule optimization, achieving significant performance gains over pure LLM or Bayesian‑optimization baselines.

Bayesian optimizationLarge Discovery Modelsantibody design
0 likes · 13 min read
How Large Discovery Models Enable AI to Design the Next Experiment
Machine Heart
Machine Heart
Aug 29, 2026 · Artificial Intelligence

Recuris: A New Memory Paradigm That Boosts Performance from 3B Models to Claude Opus 5

Recuris introduces a compact task‑state‑driven memory architecture and gated recursive self‑improvement, enabling agents to use and evolve memory more reliably and delivering large, consistent gains from 3B open‑source models up to frontier models such as Claude Opus 5 across multiple long‑horizon benchmarks.

LLM AgentsMemory ArchitectureRecuris
0 likes · 11 min read
Recuris: A New Memory Paradigm That Boosts Performance from 3B Models to Claude Opus 5
Machine Heart
Machine Heart
Aug 29, 2026 · Industry Insights

Anthropic’s $7B Pursuit of MatX Reveals Its Drive for Training Chips

Anthropic explored a roughly $7 billion acquisition of AI‑chip startup MatX, then shifted to collaboration, while hiring former Google TPU and Nvidia veterans and meeting other chip firms, signaling a strategic push to develop its own large‑model training chips alongside existing inference partnerships.

AI chipsAnthropicMatX
0 likes · 7 min read
Anthropic’s $7B Pursuit of MatX Reveals Its Drive for Training Chips
Machine Heart
Machine Heart
Aug 29, 2026 · Artificial Intelligence

When LLMs Deceive: Thomas Wolf Dissects Hugging Face’s Attack and the Limits of Safety Alignment

Thomas Wolf, chief scientist at Hugging Face, reviews a July 2026 OpenAI‑driven intrusion that generated over 17,000 attacks on the company’s cybersecurity benchmark, analyzes why the RLVR training paradigm enables reward‑hacking behavior, and argues that open‑source models can be more controllable than closed ones despite common misconceptions.

AI safetyHugging FaceRLVR
0 likes · 8 min read
When LLMs Deceive: Thomas Wolf Dissects Hugging Face’s Attack and the Limits of Safety Alignment
Machine Heart
Machine Heart
Aug 28, 2026 · Artificial Intelligence

When and Whether to Push: Douyin & Peking University’s Agentic STEPS System Wins RecSys 2026 Oral

Douyin and Peking University introduced STEPS, a self‑triggered agentic push recommendation system that redefines push notifications as a closed‑loop decision problem, achieving higher user activity, lower opt‑out rates, and 79% resource savings in a billion‑user online A/B test.

Decision TransformerDouyinOnline A/B Testing
0 likes · 15 min read
When and Whether to Push: Douyin & Peking University’s Agentic STEPS System Wins RecSys 2026 Oral
Machine Heart
Machine Heart
Aug 28, 2026 · Artificial Intelligence

Gemini Omni 1.1 Flash: Boosting Video Continuity, Control, and Efficiency

Google’s newly released Gemini Omni 1.1 Flash video model extends context to 10 seconds, supports up to 40‑second clips, adds first‑ and last‑frame control, offers a 360p draft mode that is about 60% faster than 720p, and enables 4K output and multimodal video references, all accessible via the Gemini API and AI Studio.

4K outputAI modelGemini
0 likes · 7 min read
Gemini Omni 1.1 Flash: Boosting Video Continuity, Control, and Efficiency
Machine Heart
Machine Heart
Aug 26, 2026 · Artificial Intelligence

Specula Finds 382 Deep Bugs in 67 Projects, Reducing Formal Verification to Hours

Specula, an AI‑driven tool, automatically reads code, documentation, tests and history to generate TLA+ models, runs model checking, and reproduces counterexamples as tests, uncovering 382 deep concurrency bugs across 67 open‑source systems and shrinking verification time from months to a few hours.

AI coding agentsSpeculaTLA+
0 likes · 11 min read
Specula Finds 382 Deep Bugs in 67 Projects, Reducing Formal Verification to Hours
Machine Heart
Machine Heart
Aug 26, 2026 · Artificial Intelligence

How Inspirational Learning Turns LLM Knowledge from External to Endogenous

The article introduces Inspirational Learning (Isaac), a framework that embeds cross‑domain experience directly into LLM inference via Prompt Injection (DIN) and Network‑Layer Injection (CoDA), and shows substantial gains on HumanEval, StrategyQA, ScienceQA, and SciCode benchmarks, especially for weaker models.

Cross-Domain RetrievalInspirational LearningLLM benchmarks
0 likes · 10 min read
How Inspirational Learning Turns LLM Knowledge from External to Endogenous