A developer trained a specialized chess LLM from scratch that generates legal moves 96% of the time. In direct comparison, GPT-5 produces illegal moves within the first 10 moves of every game tested. This small, focused model reveals a fundamental truth about general AI's struggle with rule-based sy...
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Large language models are hitting a wall: decoding latency cripples real-time applications, and existing acceleration techniques can't scale beyond single machines. A new research framework called DSD proposes a distributed solution that could fundamentally change how we deploy AI across edge and cloud environments.
A viral Reddit thread declaring 'She doesn't exist' has sparked a massive conversation about AI companionship. But the real story isn't about existence—it's about a fundamental misunderstanding of what's actually happening in these interactions.
A paying customer's forensic analysis, using Perplexity's own documentation, exposed a significant downgrade in its flagship 'Deep Research' feature. The company's response—a permanent ban and thread deletion—signals a dangerous new phase for AI consumers. This isn't just about one feature; it's a warning about the opaque systems we're increasingly asked to trust.
A viral Reddit post titled 'She doesn't exist' has ignited a major conversation about AI companionship. Garnering nearly 7,000 upvotes and 1,300 comments, the discussion reveals a critical public reckoning with the emotional mechanics of generative AI.
Sparse Autoencoders promised to crack open the black box of large language models, revealing the concepts they learn. But their features are often a tangled mess. New research introduces AlignSAE, a method that forces these features to align with a human-defined ontology. The results reveal a fundamental shift in how we might control and understand AI.
A new benchmark called CycliST exposes a fundamental flaw in today's video language models. While they excel at describing what's in a frame, they fail at understanding the cyclical patterns that govern our world.
A new system called CodeDistiller reveals the fundamental bottleneck in automated scientific discovery. The real limitation isn't model size or training data—it's the quality of the code libraries these AI agents can access and generate.
New research reveals DeepSeek-v3.2 achieves state-of-the-art performance with a novel Mixture-of-Experts architecture. The open-source model demonstrates superior efficiency while maintaining competitive reasoning capabilities across multiple evaluation frameworks.
A new method called AlignSAE promises to crack open the black box of large language models by forcing their internal features to align with human-defined concepts. This breakthrough in interpretability could lead to safer, more controllable, and more trustworthy AI systems by making their 'knowledge' inspectable and editable.
Securus Technologies has deployed an AI system trained on half a billion inmate communications to flag potential criminal activity. The controversial pilot program represents one of the largest-scale applications of predictive AI in law enforcement history, raising urgent questions about privacy, bias, and the future of surveillance.
A new framework called Matrix is challenging the centralized status quo of AI data generation. By enabling agents to coordinate peer-to-peer, it promises to eliminate bottlenecks and unlock unprecedented scale for creating the training data that fuels modern LLMs.
A telecom company has trained an AI on millions of inmate conversations and is now using it to scan calls for planned crimes. This pilot program represents one of the most direct applications of predictive AI in the criminal justice system, raising urgent questions about privacy, bias, and the future of surveillance.