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Ukraine moves to crack down on scam call centers after corruption scandal
Ukraine's parliament passed legislation criminalizing fraudulent call centers with 7-12 year prison terms after a bribery scandal implicating prosecutors.
Ukraine's Verkhovna Rada passed legislation making electronic-communications fraud and organizing or working for fraudulent call centers separate crimes punishable by 7-12 years, awaiting President Zelensky's signature. The bill advanced after NABU alleged prosecutors took bribes since mid-2025 to shield scam call centers; five suspects were named and Prosecutor General Ruslan Kravchenko, who denies wrongdoing, was dismissed by parliament and presidential decree. Ukrainian authorities previously reported 411 searches and 94 suspected call centers shut down in one week, including a Kyiv operation that stole over $500,000 from dozens of Americans.
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Can Skills Learned in Games Transfer to Real-World Work?
Good Start Labs trains models in strategy games like 1830 and Diplomacy, showing terminal-agent training transfers to financial research benchmarks.
Good Start Labs, spun out of Every with $3.6M from General Catalyst and Inovia, trains AI models in verifiable strategy games. A 30B model trained as a multi-turn terminal agent in 1830: The Game of Railroads and Robber Barons improved Finance-Agent benchmark performance, while single-turn QA training did not transfer. The founders also co-authored COS-PLAY, a paper on co-evolving LLM decision and skill-bank agents for long-horizon tasks.
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Why you should work on AI for AI Research — Richard Socher of Recursive
Richard Socher's new lab Recursive, backed by $4.65B seed, targets AI systems that automate AI research itself.
Latent Space interviews Richard Socher, founder of You.com and AIX Ventures, about his new venture Recursive, which raised a $4.65 billion seed round to build the 'Eureka Machine' — a superintelligence for automating invention and AI research. Early claimed results include an AI research system outperforming humans and their agents on optimization tasks within two days, and NVIDIA GPU kernel improvements discovered without CUDA experts. Discussion spans reward hacking, constitutional AI critique, AI regulation, open-source models as geopolitical soft power, and hard-takeoff constraints.
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Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation
Study shows specialists trained on question-answer pairs implicitly select latent reasoning trajectories, and tuning choices control the precision-generalization trade-off in distillation.
The work demonstrates that specialist optimization implicitly selects from a latent trajectory space when specialists are trained only on question-answer pairs without explicit reasoning supervision. Using student distillation as an agnostic probe across 27 specialist-student pairings, specialization-generalization profiles correlate exceptionally strongly. Explicitly controlling the specialist's distributional drift systematically shifts both teacher and distilled student along a controllable trade-off between domain precision and general-capability retention across chemistry, physics, and multilingual settings, even across divergent model families.
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Agent as Policy for Robotic Manipulation
Agent as Policy lets a general-purpose agent drive a physical robot via runtime reasoning and program generation, reaching 100% success on manipulation tasks.
The paper introduces Agent as Policy (AGP), which puts task planning and execution for a physical robot under a general-purpose agent's control with no task-specific or environment-specific training. The agent interprets visual evidence, writes executable programs, issues motion commands, and revises actions based on physical outcomes. AGP was evaluated on real-world manipulation tasks including assembly from human videos, block construction from goal images, die reorientation, targeted throwing, and bimanual towel folding. It achieved success rates of 100%, 100%, and 80% on three block construction configurations.
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