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01AIhub · Research news and interviews
At the International Conference on Machine Learning (ICML 2026) , Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma , and Hokin Deng presented their work Vision Language Models Cannot Reason About Physical Transformation . In this interview, William Yijiang Li tells us more about the research, the method the team used, and the controlled experiments they carried out. What is the topic of the research in your paper and why is it an interesting area for study? Our research asks a fairly fundamental question: do vision-language models actually understand how the physical world changes over time? Modern vision-language models are increasingly being used in areas such as robotics, embodied agents, and video understanding. They can recognize objects, describe scenes, and answer surprisingly sophisticated questions. However, being able to describe a scene is different from maintaining an understanding of an object or physical quantity as the scene changes. We study this through the concept of conservation . For example, if water is poured from a short, wide glass into a tall, narrow one, its appearance changes dramatically, but the amount of water remains the same. Similarly, spreading a row of coins farther apart does not change the number of coins. These tasks are simple for humans, but they require an important form of reasoning: you need to track what happens during a transformation and distinguish changes in appearance from changes in the underlying physical state. This is particularly interesting for embodied AI. If an AI system is expected to interact with the real world, it cannot rely only on static visual recognition. It needs to maintain and update representations of objects and their properties as events unfold. What are the main contributions of your research? I would summarize the contributions in three parts. First, and most importantly, our study shows that current vision-language models still struggle to maintain and update a stable representation of the physical world as it changes over time. Although these models can often answer basic questions about physical concepts such as conservation correctly, our controlled experiments show that this does not necessarily mean they understand the underlying transformation. Models that perform well when a quantity is conserved…
Discovery · Google DeepMind
Google DeepMind creates watermarked AI-designed proteins that still work in lab tests SynthID Bio embeds a detectable signature into AI-generated protein sequences and structures, with Nature research showing the watermark can survive without significantly disrupting biological function Google DeepMind researchers have demonstrated a way to watermark AI-designed proteins while preserving their biological function, an attempt to bring the provenance systems already used for AI-generated media into synthetic biology. The new method, SynthID Bio, puts a detectable signature directly into either a protein’s amino acid sequence or its predicted three-dimensional structure. The central challenge is very different from watermarking an AI-generated image. Changing a few pixels may leave a picture effectively unchanged. Altering a protein can change how it folds, binds to other molecules or functions altogether. DeepMind’s researchers had to show not simply that their watermark could be detected, but that the resulting biological designs still worked. In laboratory testing reported in Nature, watermarked protein binders remained functional across three targets: VEGF-A, PD-L1 and the receptor-binding domain of the SARS-CoV-2 spike protein. Pushmeet Kohli, Chief Scientist at Google Cloud and VP Science at Google DeepMind, described the result on LinkedIn as the “successful synthesis of AI-designed proteins that are both functional and watermarked.” How do you watermark a protein? SynthID Bio actually covers two different approaches. For protein sequences, the system subtly changes how amino acids are selected while an AI model generates the sequence. The resulting signature can then be detected using a secret watermarking key. For three-dimensional protein structures, the researchers took a different route. They fine-tuned part of AlphaFold 3 so the watermark is built into the predicted atomic coordinates produced by the model. The recommended version of that structural method achieved a detection rate above 99.8% at a 0.1% false-positive rate in the researchers’ tests, without reducing key structural accuracy measures compared with the AlphaFold 3 baseline. The more important test for the sequence watermark happened outside the computer. Researchers synthesized watermarked and non-watermarked protein binders and tested their ability to attach to the three target prot…
Nature Portfolio · Nature Biotechnology
Programmable RNA sensors such as toehold switches are used to detect specific RNA sequences. However, their reliance on protein-based or RNA-based outputs limits their use in multiplexed and sequencing-based applications. Here we introduce Detectrons, modular biosensors that couple programmable toehold switches with retron-mediated reverse transcription to transduce RNA inputs into unique DNA barcodes, converting dynamic RNA signals into durable DNA records within living cells. The framework enables alternative modes of transcript-based sensing with applications including viral infection detection. Through the construction of a synthetic toehold retron library and application of machine learning, we uncover key design principles that improve signal strength and specificity. We apply Detectrons to the multiplexed live-cell detection of specific phage infections, enabling transcript-triggered barcode synthesis and quantitative host susceptibility profiling in pooled bacterial populations. Detectrons provide a scalable and generalizable strategy for phage screening and for recording transcriptional events in complex bacterial communities. RNA sequences are detected and converted directly into DNA barcodes.
Nature Portfolio · Nature Computational Science
Physics-based neuromorphic computing promises major gains in performance and energy efficiency, but training remains largely in silico, requiring costly physical modeling and suffering from fabrication-induced errors. Here we introduce on-chip, in situ physical gradient descent (INSPIRE), a general training method for photonic integrated circuits. INSPIRE uses on-chip synthetic time-reversal holography to measure the full complex fields of bidirectional photonic modes, enabling gradient computation and parameter updates directly in the physical system. The framework is topology-agnostic and compatible with diverse optical circuits. Experiments yield trained matrices with a relative error of 0.26%, and we demonstrate in situ training through scattering media for matrices larger than the number of native tunable elements. INSPIRE also enables meta-photonic circuits to perform in situ meta-learning, realizing single-shot photonic learning with 251-fold model compression and 136-fold task-specific training acceleration. This work offers a practical route toward adaptive and efficient intelligent photonic systems. This study reports a physics-based in situ learning method that trains artificial neural networks directly within the photonic integrated circuits, turning diverse optical circuits into efficient AI learning hardware.
Hugging Face Daily Papers
Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The benchmark covers four downstream datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Prithvi-EO-2.0, SatMAE, and DOFA across three random seeds, we show that aggregate performance consistently masks biome-dependent disparities across architectures and tasks. For example, Prithvi-EO-2.0 reaches 90.98% overall macro-F1 on m-EuroSAT but a mean worst-group score of only 83.72%, while m-SA-Crop-Type drops from 27.30% overall mIoU to 18.47% in the Xeric and Mineralogical group. We further evaluate Biome-Orthogonal Linear Probing (BOLP), Dynamic Biome Reweighting (DBR), and GroupDRO as complementary mitigation baselines. Their effectiveness is model- and task-dependent; for example, BOLP improves Prithvi-EO-2.0 worst-group F1@opt on m-BigEarthNet from 46.12% to 50.27% without updating the RSFM backbone. FairRSFM provides a reusable protocol for diagnosing and mitigating ecological robustness gaps in remote sensing foundation models. Code and datasets are available at: https://github.com/aminurhossain/FairRSFM.
