Large-scale AI training is spreading across multiple datacenters, with Google, Microsoft, AWS, Meta, and CoreWeave cited as examples. Because synchronized GPU clusters must exchange data in bursts, inter-site links can become a bottleneck, which Cisco estimates may require aggregate bandwidth about 14x a conventional DCI baseline.
We byteified Qwen 3 8B & Llama 3 8B to create Bwen 8B & Blama 8B. Both come close to matching their source models' performance in our evaluations. Bwen 8B also outperforms Bolmo 7B across our aggregate evaluation suite. https://huggingface.co/collections/allenai/bolmo
6️⃣ Non-Associative Algebra: The team worked with Muse Spark to find an exception to a proposed rule about mathematical structures inspired by biology. They went further by developing an alternative characterization, which the researchers checked and refined. Read the paper: https://ai.meta.com/research/publications/on-solvable-evolution-algebras-and-a-conjecture-by-garcia-martinez-and-perez-rodriguez/
5️⃣ Arithmetic Physics: Researchers working with Muse Spark connected an idea from number theory with a calculation in string theory. They proved the link works in more cases than previously known, building on ideas from the 1980s. Read the paper: https://ai.meta.com/research/publications/string-two-point-function-height-function-on-a-curve/
4️⃣ Optimization: When can you replace a hard math problem with a simpler one without losing anything? Researchers worked with Muse Spark to prove a clear rule for when a particular simplification captures the original exactly, and when it leaves a gap. Read the paper: https://ai.meta.com/research/publications/tightness-of-the-cycle-based-relaxation-for-completed-length-three-alpha-cycles/
3️⃣ Group Theory: Researchers disproved a proposed rule about mathematical structures that describe symmetry by finding one counterexample. Muse Spark generated the search code that found it, and the team verified the result and completed the proof. Read the paper: https://ai.meta.com/research/publications/semiabelian-groups-need-not-be-monomial/
2️⃣ Differential Equations: Imagine a tug-of-war between one effect squeezing a wave inward and another spreading it out. Can the wave keep concentrating forever? With help from Muse Spark, researchers proved that, under the conditions studied, a wave in a laser-inspired model must "blow up" in finite time. Read the paper: https://ai.meta.com/research/publications/finite-time-blow-up-of-radial-negative-energy-solutions-for-the-mass-critical-biharmonic-nonlinear-schrodinger-equation/
1️⃣ Probability: Mathematicians worked with Muse Spark to answer a question about fitting random points onto the surface of a stretched sphere. For the setting studied, they proved a sharp cutoff between when an exact fit is likely and when it is unlikely. Read the paper: https://ai.meta.com/research/publications/the-strict-threshold-for-gaussian-ellipsoid-fitting/
AI at Meta says mathematicians used Muse Spark 1.1 and Muse Spark 1.2 in Thinking Mode through the standard meta.ai chat interface to find solutions to open problems. The company is sharing six resulting papers, each marking which passages were drafted primarily by humans or AI, with mathematicians guiding the work and a second group reviewing it.
We put Muse Realtime Avatar head-to-head with two leading commercial avatar systems in their own live-call products. Raters held 2–3 minute conversations with matched avatar identities, then compared visual quality, sync, character consistency, and mannerisms. Muse Realtime Avatar came out ahead on overall preference.
Read our research blog to learn more about Muse Realtime Avatar: https://research.meta.ai/blog/bringing-your-muse-to-life
Meta's Muse Realtime Voice generates speech tokens encoding both content and prosody, and Muse Realtime Avatar consumes that shared stream to produce streaming video. Using a fixed-length history as motion context keeps computation bounded regardless of conversation length while synchronizing voice, lip motion, and expressions.
Live video streaming needs to respond instantly while remaining visually consistent over long conversations. We achieved this by distilling a large 40-step diffusion teacher with 3-way CFG (120 evaluations per video chunk) into an unguided 2-step causal student with a fixed-length KV cache. Self-forcing helps the student resist drift and maintain near teacher quality with 60x fewer evaluations.
Meta's AIRA₃ system generalizes across domains by changing only the task specification, according to the post. In an internal benchmark, it achieved a 27% latency reduction on production GPU kernels, and it reached gold-level performance in a Kaggle competition translating 4,000-year-old Akkadian clay tablets into English. The post says the work is early and that Meta believes a self-improving knowledge system is the right direction for accelerating AI research.
Meta's AIRA₃ replaces a central controller with many long-running agents, each pairing a model with a coding harness in its own isolated environment. The agents coordinate asynchronously through a shared forum for hypotheses and findings and a shared filesystem for solution artifacts. According to the post, performance gains compound over time as agents build on each other's discoveries.
Meta's AIRA₃ entered the live competition with an ensemble of models, and the 8th-ranked gold-medal entry combined GPT 5.5 (w/ OpenCode) and Claude 4.8 (w/ ClaudeCode). Post-hoc testing found Muse Spark 1.2 (w/ MuseCode) also reached gold-medal level, while Muse Spark 1.1 (w/ OpenCode) and GLM 5.2 (w/ OpenCode) reached silver-medal level, all graded on the same private test set.
Piotr Migdał tested 16 AI models on 1,040 mushroom photos covering 55 species, and the best, Gemini-3.8-flash, was correct on its first guess only 65 percent of the time. Dangerous mistakes were common, with the death cap called edible 16 percent of the time, and Qwen3.8-27b wrongly labeled poisonous mushrooms edible 36 percent of the time. Migdał warns users not to eat any mushroom because an AI says it is safe.
Meta released Brain2Qwerty v2, an end-to-end deep learning pipeline that decodes sentences in real time from non-invasive brain recordings. The model reached 61% word accuracy across participants, compared with 8% for other non-invasive methods, and 78% for the best participant. Meta also released the v1 and v2 training code, and partner BCBL released the v1 dataset.
AIWhy it matters: The source reports word accuracy and data-scaling results for non-invasive decoding, offering a benchmark against surgical brain-computer interfaces and prior non-invasive methods.