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Open-SWE-Traces: 207K Multilingual Trajectories Set New Standard for Autonomous Software Engineering Agents Infant-Inspired Noise Boosts Deep RL Exploration, Research from arXiv Shows Mutual Distillation of Dual Foundation Models Achieves State-of-the-Art PET/CT Segmentation with Only 5 Labeled Cases SPARK Method Activates Latent Security Knowledge in LLMs for Secure Code Generation Apple explains why Siri AI took so long: first version ready last year but rebuilt from ground up New LLM Framework Detects Phishing Emails with Over 90% Accuracy Dual-Granularity Orthogonal Disentanglement: New Framework Boosts Generalizable Audio Deepfake Detection Medical Image Segmentation Survey: U-Net, Transformers, SAM and Clinical Translation Challenges Bayesian Inference and Decision Audits Reveal Unreliability in Frontier AI Evaluation Archives Dali casualty exposes erosion of technical ownership in shipmanagement, warns veteran Kapoor Open-SWE-Traces: 207K Multilingual Trajectories Set New Standard for Autonomous Software Engineering Agents Infant-Inspired Noise Boosts Deep RL Exploration, Research from arXiv Shows Mutual Distillation of Dual Foundation Models Achieves State-of-the-Art PET/CT Segmentation with Only 5 Labeled Cases SPARK Method Activates Latent Security Knowledge in LLMs for Secure Code Generation Apple explains why Siri AI took so long: first version ready last year but rebuilt from ground up New LLM Framework Detects Phishing Emails with Over 90% Accuracy Dual-Granularity Orthogonal Disentanglement: New Framework Boosts Generalizable Audio Deepfake Detection Medical Image Segmentation Survey: U-Net, Transformers, SAM and Clinical Translation Challenges Bayesian Inference and Decision Audits Reveal Unreliability in Frontier AI Evaluation Archives Dali casualty exposes erosion of technical ownership in shipmanagement, warns veteran Kapoor

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Semi-Supervised Framework Scales LLM Reasoning Using 10-15x Fewer Labels Than Traditional Methods

Artificial Intelligence #llm#reasoning

Semi-Supervised Framework Scales LLM Reasoning Using 10-15x Fewer Labels Than Traditional Methods

A new semi-supervised framework for training LLM reasoning uses a lightweight verifier to judge reasoning quality, requiring only a few labeled samples. Experiments on math problems and visual question answering show accuracy comparable to 10-15x more labeled data. The method could reduce the cost of building large-scale reasoning datasets.

Jun 16, 2026 2 sources

New Agentic Programming Framework Shifts Control from LLMs to Deterministic Code for Greater Reliability

Artificial Intelligence #llm#ai

New Agentic Programming Framework Shifts Control from LLMs to Deterministic Code for Greater Reliability

A new paper argues that current LLM agent frameworks have architectural flaws leading to token explosion, control-flow hallucination, and unreliable completion. The authors propose Agentic Programming, where the program governs all control flow and the LLM is an adaptive component called LLM-as-Code, invoked only for reasoning or generation. A case study on computer-use agents shows improved stability in long visual operation sequences.

Jun 16, 2026 1 source