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Meta launches the F3 Lightning infrastructure layer
On November 4, 2026, Meta launched the F3 Lightning infrastructure layer, which decouples feature publishing from shared execution on top of F3, enabling each feature to be independently compiled, validated, deployed, and rolled back. According to the Meta Engineering Blog, F3 originally merged all features into a single graph as the publishing unit, so an error in a single definition would block the entire publishing process, while a single request now involves more than 20,000 features. Through four optimizations—stable computation identifiers, a single shared execution plan, cross-feature shared memory buffers, and dense index mapping—F3 Lightning achieves independent per-feature publishing while retaining the efficiency of shared computation; similar features go live about 3.6 times faster than with the original F3, with no increase in infrastructure compute costs. There are currently no further developments.
On November 4, 2026, Meta launched the F3 Lightning infrastructure layer, which decouples feature publishing from shared execution on top of F3, enabling each feature to be independently compiled, validated, deployed, and rolled back. According to the Meta Engineering Blog, F3 originally merged all features into a single graph as the publishing unit, so an error in a single definition would block the entire publishing process, while a single request now involves more than 20,000 features. Throug
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Meta launches F3 Lightning infrastructure layer
On November 4, 2026, Meta launched the F3 Lightning infrastructure layer, which decouples feature publishing from shared execution on top of F3, enabling each feature to be independently compiled, validated, deployed, and rolled back. According to the Meta Engineering Blog, F3 originally merged all features into a single graph as the publishing unit, so an error in a single definition would block the entire publishing process, while a single request now involves more than 20,000 features. Through four optimizations—stable computation identifiers, a single shared execution plan, cross-feature shared memory buffers, and dense index mapping—F3 Lightning achieves independent per-feature publishing while retaining the efficiency of shared computation; similar features go live about 3.6 times faster than with the original F3, with no increase in infrastructure compute costs. There are currently no further developments.
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View story:Meta launches the F3 Lightning infrastructure layer
Meta launches AI outfit app Style Sketch
Meta launches AI outfit app Style Sketch. According to a TechCrunch AI report, the app can connect to Instagram and build a virtual wardrobe from photos the user has already posted; it also supports manually adding items by taking photos, recording videos, or uploading album images. The app recommends outfits based on the user's style and lets users try them on a "realistic model" that matches their body type and skin tone; it also supports generating day-by-day outfit plans based on destination, trip length, and weather. Phoenix Technology reported the same day that the app has launched in Indonesia, Singapore, and Thailand, and supports iOS and Android. Meta did not respond on whether it will expand the release scope.
Meta launches AI outfit app Style Sketch. According to a TechCrunch AI report, the app can connect to Instagram and build a virtual wardrobe from photos the user has already posted; it also supports manually adding items by taking photos, recording videos, or uploading album images. The app recommends outfits based on the user's style and lets users try them on a "realistic model" that matches their body type and skin tone; it also supports generating day-by-day outfit plans based on destination
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Meta launches AI outfit app Style Sketch
Meta launches AI outfit app Style Sketch. According to a TechCrunch AI report, the app can connect to Instagram and build a virtual wardrobe from photos the user has already posted; it also supports manually adding items by taking photos, recording videos, or uploading album images. The app recommends outfits based on the user's style and lets users try them on a "realistic model" that matches their body type and skin tone; it also supports generating day-by-day outfit plans based on destination, trip length, and weather. Phoenix Technology reported the same day that the app has launched in Indonesia, Singapore, and Thailand, and supports iOS and Android. Meta did not respond on whether it will expand the release scope.
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Meta announces support for scientific research at five U.S. universities
On October 29, 2026, Meta announced support for scientific research at five U.S. universities: Caltech, the Ohio State University College of Medicine, Louisiana State University, Indiana University, and UTHealth Houston. Meta said it will provide $2 million in funding and access to its latest models to support research using its models to advance medicine, physics, chemistry, biology, computer science, and mathematics. Specific projects include using the open-weight SAM model to monitor infant tracheostomy tube displacement, using multimodal AI to help dementia patients communicate, and combining abdominal CT, medical records, and ECG data to identify HFpEF heart failure risk early. In a separate Meta release the same day, the company said it is opening Muse Wonder, Muse Spark, Muse Glimmer, Muse Spark Open, SAM, V-JEPA, DINO, and the multilingual model SeamlessM4T to researchers; these universities will use the models for areas including mathematics and physics research, X-ray screening and heart failure detection, infant tracheostomy monitoring, maternal and newborn care, and brain cancer treatment.
On October 29, 2026, Meta announced support for scientific research at five U.S. universities: Caltech, the Ohio State University College of Medicine, Louisiana State University, Indiana University, and UTHealth Houston. Meta said it will provide $2 million in funding and access to its latest models to support research using its models to advance medicine, physics, chemistry, biology, computer science, and mathematics. Specific projects include using the open-weight SAM model to monitor infant t
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Meta announces support for research at five universities
On October 29, 2026, Meta announced support for scientific research at five U.S. universities: Caltech, the Ohio State University College of Medicine, Louisiana State University, Indiana University, and UTHealth Houston. Meta said it will provide $2 million in funding and access to its latest models to support research using its models to advance medicine, physics, chemistry, biology, computer science, and mathematics. Specific projects include using the open-weight SAM model to monitor infant tracheostomy tube displacement, using multimodal AI to help dementia patients communicate, and combining abdominal CT, medical records, and ECG data to identify HFpEF heart failure risk early. In a separate Meta release the same day, the company said it is opening Muse Wonder, Muse Spark, Muse Glimmer, Muse Spark Open, SAM, V-JEPA, DINO, and the multilingual model SeamlessM4T to researchers; these universities will use the models for areas including mathematics and physics research, X-ray screening and heart failure detection, infant tracheostomy monitoring, maternal and newborn care, and brain cancer treatment.
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View story:Meta announces support for scientific research at five U.S. universities
Meta announces support for five universities to conduct AI scientific research and medical research
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Meta announces support for five universities to conduct AI scientific research and medical research
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Meta paper finds that RL post-training reduces task coverage under multiple attempts
Meta published a paper studying the impact of reinforcement learning post-training on agents' performance across multiple attempts. The study tested 14 pairs of base models and post-trained models on 3 agent benchmarks, and built the PTGS sampler, which raises the temperature for difficult prompts and lowers it for easy prompts. The results show that post-training makes agents perform better on their first attempt, but they solve fewer tasks that are solvable within up to 128 attempts. On WebShop, the gemma-4-31B base model solved over 85% of tasks within 128 attempts, while the post-trained version solved 56%. The Sharpening Tax proposed by the authors is positive in 36 of 42 model-benchmark combinations, meaning coverage shrinks. The authors suggest that when selecting models for retryable agents, one should measure how many tasks the model can ultimately solve, rather than looking only at first-attempt accuracy.
Meta published a paper studying the impact of reinforcement learning post-training on agents' performance across multiple attempts. The study tested 14 pairs of base models and post-trained models on 3 agent benchmarks, and built the PTGS sampler, which raises the temperature for difficult prompts and lowers it for easy prompts. The results show that post-training makes agents perform better on their first attempt, but they solve fewer tasks that are solvable within up to 128 attempts. On WebSho
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Meta paper finds RL post-training reduces task coverage across multiple attempts
Meta published a paper studying the impact of reinforcement learning post-training on agents' performance across multiple attempts. The study tested 14 pairs of base models and post-trained models on 3 agent benchmarks, and built the PTGS sampler, which raises the temperature for difficult prompts and lowers it for easy prompts. The results show that post-training makes agents perform better on their first attempt, but they solve fewer tasks that are solvable within up to 128 attempts. On WebShop, the gemma-4-31B base model solved over 85% of tasks within 128 attempts, while the post-trained version solved 56%. The Sharpening Tax proposed by the authors is positive in 36 of 42 model-benchmark combinations, meaning coverage shrinks. The authors suggest that when selecting models for retryable agents, one should measure how many tasks the model can ultimately solve, rather than looking only at first-attempt accuracy.
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View story:Meta paper finds that RL post-training reduces task coverage under multiple attempts
Rohan Paul introduces the paper by Princeton and Meta on continuous improvement of AI agents
On October 30, 2026, Rohan Paul published an article introducing a new paper from Princeton and Meta on the continuous improvement of AI agents. The paper proposes the Self-Direction method: it has failed agents reflect on the current run, trains the lessons into LoRA adapters about 0.02% the size of the model, and then a router loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rose from 38.8% to 55.8%, outperforming in-context reflection and the Skills.md file approach; among 185 errors that had occurred previously, 67.3% of fixes were still effective by the third encounter, compared with 42.9% for simple retries. There have been no subsequent developments to this introduction so far.
On October 30, 2026, Rohan Paul published an article introducing a new paper from Princeton and Meta on the continuous improvement of AI agents. The paper proposes the Self-Direction method: it has failed agents reflect on the current run, trains the lessons into LoRA adapters about 0.02% the size of the model, and then a router loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rose from 38.8% to 55.8%, outperforming in-context reflection
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Rohan Paul introduces the Princeton and Meta paper on continuous improvement of AI agents
On October 30, 2026, Rohan Paul published an article introducing a new paper from Princeton and Meta on the continuous improvement of AI agents. The paper proposes the Self-Direction method: it has failed agents reflect on the current run, trains the lessons into LoRA adapters about 0.02% the size of the model, and then a router loads the closest adapter when a similar failure is encountered. On Qwen3.6-27B, the GAIA task success rate rose from 38.8% to 55.8%, outperforming in-context reflection and the Skills.md file approach; among 185 errors that had occurred previously, 67.3% of fixes were still effective by the third encounter, compared with 42.9% for simple retries. There have been no subsequent developments to this introduction so far.
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NIL Technology releases ultra-miniature eye-tracking camera metaEye
Danish optics company NIL Technology has released the ultra-miniature eye-tracking camera metaEye. The product is described as the world's first camera designed entirely with meta-optics; its overall size is smaller than a grain of rice, and it integrates two metalens optical elements (MOE), a bandpass filter and multiple optical apertures, mounted directly on the image sensor's cover glass without the need for traditional refractive lenses. NILT says the relevant customer is moving toward mass production in 2026, and metaEye is also the first product in its metaCamera modular camera architecture.
Danish optics company NIL Technology has released the ultra-miniature eye-tracking camera metaEye. The product is described as the world's first camera designed entirely with meta-optics; its overall size is smaller than a grain of rice, and it integrates two metalens optical elements (MOE), a bandpass filter and multiple optical apertures, mounted directly on the image sensor's cover glass without the need for traditional refractive lenses. NILT says the relevant customer is moving toward mass
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NIL Technology releases ultra-miniature eye-tracking camera metaEye
Danish optics company NIL Technology has released the ultra-miniature eye-tracking camera metaEye. The product is described as the world's first camera designed entirely with meta-optics; its overall size is smaller than a grain of rice, and it integrates two metalens optical elements (MOE), a bandpass filter and multiple optical apertures, mounted directly on the image sensor's cover glass without the need for traditional refractive lenses. NILT says the relevant customer is moving toward mass production in 2026, and metaEye is also the first product in its metaCamera modular camera architecture.
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View story:NIL Technology releases ultra-miniature eye-tracking camera metaEye
NVIDIA and Meta Announce Expanded Collaboration on Muse Models
On October 27, 2026, NVIDIA and Meta announced an expansion of their collaboration on Muse and the Muse series of models to improve inference and training performance, with Meta's Muse models and agents running on NVIDIA infrastructure. According to an announcement published by the NVIDIA Newsroom that day, Meta plans to expand deployment of NVIDIA Vera Rubin and Vera CPU and to adopt Spectrum-X Ethernet and BlueField-4; the two sides will also evaluate the NVIDIA OpenShell reference architecture and NVIDIA Sentry for secure agent deployment and behavior monitoring. The same announcement stated that NVIDIA will invest $3 billion in Meta's equity offering. 36Kr AI reported on October 28 that this investment is part of Meta's total $95 billion fundraising plan, with AMD and Broadcom also expressing interest in investing; Meta expects full-year capital expenditure to reach $135 billion to $145 billion. The collaboration and investment plans have now been announced, while subsequent deployment progress remains to be disclosed.
On October 27, 2026, NVIDIA and Meta announced an expansion of their collaboration on Muse and the Muse series of models to improve inference and training performance, with Meta's Muse models and agents running on NVIDIA infrastructure. According to an announcement published by the NVIDIA Newsroom that day, Meta plans to expand deployment of NVIDIA Vera Rubin and Vera CPU and to adopt Spectrum-X Ethernet and BlueField-4; the two sides will also evaluate the NVIDIA OpenShell reference architectur
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NVIDIA and Meta announce expanded Muse model collaboration
On October 27, 2026, NVIDIA and Meta announced an expansion of their collaboration on Muse and the Muse series of models to improve inference and training performance, with Meta's Muse models and agents running on NVIDIA infrastructure. According to an announcement published by the NVIDIA Newsroom that day, Meta plans to expand deployment of NVIDIA Vera Rubin and Vera CPU and to adopt Spectrum-X Ethernet and BlueField-4; the two sides will also evaluate the NVIDIA OpenShell reference architecture and NVIDIA Sentry for secure agent deployment and behavior monitoring. The same announcement stated that NVIDIA will invest $3 billion in Meta's equity offering. 36Kr AI reported on October 28 that this investment is part of Meta's total $95 billion fundraising plan, with AMD and Broadcom also expressing interest in investing; Meta expects full-year capital expenditure to reach $135 billion to $145 billion. The collaboration and investment plans have now been announced, while subsequent deployment progress remains to be disclosed.
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View story:NVIDIA and Meta Announce Expanded Collaboration on Muse Models