Ai2's Bolmo paper in Nature shows how to retrofit existing language models to read raw bytes instead of tokens. What byteification is, how Bolmo, Bwen and Blama perform, what the two-stage process costs, and which tasks benefit.
Open-weight models now handle most tokens on gateways like Vercel's and OpenRouter, yet closed models still take about 86% of estimated spend. What the September 2026 data shows, why Chinese open models lead, and how a 9B longevity model topped frontier systems on a domain benchmark.
A Nature paper describes Paper2Agent, which converts a research paper and its code into an MCP server that a chat agent can use. How it works, the AlphaGenome case built in about 45 minutes for US$14, accuracy results, limits, and how researchers can use it.
Context windows have grown from 4K tokens to millions, but bigger is not always better. Here is what changed in 2026 and how it affects real-world LLM usage.
A new paper finds 80% of developers use AI tools but only 29% trust them. Here are the eight assumptions the research says are wrong, and what actually matters.
A new Nature paper argues our entire understanding of LLMs is built on flawed human metaphors — and proposes a radical alternative called machine experientialism that reframes how we think about AI cognition.
The AI field spent two years proving that longer reasoning chains produce better answers. Now a wave of new research shows the opposite: beyond a certain point, more 'thinking' actively degrades performance. The overthinking problem is real, measurable, and it's forcing a rethink of how we deploy reasoning models.
Meta FAIR and academic partners' Brain2Qwerty v2 decodes sentences people type from non-invasive MEG recordings, with 61% average and 78% best-participant word accuracy. How it works, how it compares with v1 and implants, why MEG matters, its limits and the neural privacy questions it raises.
Pre-training scale isn't the only lever anymore. Test-time compute scaling lets models think longer at inference — and the results are reshaping how we measure AI capability.
A June 2026 Nature Medicine study found three frontier LLMs outscored two specialized clinical AI tools on MedQA, HealthBench and real clinician queries. The results, the vendor pushback including a retraction demand, and what it means for enterprises choosing general models versus specialist AI.
A clear guide to multimodal large language models: pipeline versus natively multimodal designs, the research milestones from CLIP to GPT-4o and Gemini, how current assistants handle images, audio and video, practical uses, known weaknesses, benchmarks and security risks.
An accessible guide to Mixture of Experts, the sparse architecture behind DeepSeek-V3, Llama 4, Mixtral, gpt-oss and Gemini 1.5: how routing works, total versus active parameters, load balancing, the papers that shaped it, common myths and practical trade-offs for serving and fine-tuning.
Anthropic's Claude Opus 4.6 delivers a 1M token context window that actually works, revolutionary agent teams, and benchmark performances that redefine agentic AI capabilities.
From Claude Opus 4.6's million-token context to DeepSeek's memory architecture revolution, explore the technical advances reshaping AI in early 2026.
What DeepSeek R1 is and why it mattered: the R1-Zero reinforcement learning experiment, the multi-stage training behind R1, published benchmarks against OpenAI o1, the distilled 1.5B to 70B models, MIT licensing, API pricing at launch and how to run it.
A look back at the pre-launch predictions for GPT-5 and how they compare with what OpenAI actually released on August 7, 2025: a unified system with a router, a 400K-token API context window, and new developer controls.