https://stacks.braintank.ai/2026-11-02T00:00:00Z https://stacks.braintank.ai/d/arxiv-2601.15351.html2026-11-02T00:00:00ZbraintankAI

OmniSpectra, a 12.2-million-parameter model pretrained on eight surveys, reads physical wavelengths directly and is reported to beat substantially larger rivals.

https://stacks.braintank.ai/d/arxiv-2610.32749.html2026-11-01T00:00:00ZbraintankAI

A preprint offers a more realistic test for audio-visual voice extraction, plus a streaming model the authors say is far smaller and faster than an existing baseline.

https://stacks.braintank.ai/d/arxiv-2610.24520.html2026-11-01T00:00:00ZbraintankAI

A test built on hundreds of synthetic sessions finds the best agent memory method answering only about a third of habit questions correctly.

https://stacks.braintank.ai/d/arxiv-2610.36068.html2026-11-01T00:00:00ZbraintankAI

A preprint describes a training method that replays a robot's decisions in a learned simulator, tries alternatives, and credits the choices that made the difference.

https://stacks.braintank.ai/d/W7224853653.html2026-11-01T00:00:00ZbraintankAI

Researchers combine two physics-based driver models with a Transformer-LSTM network and report lower acceleration errors on Norwegian winter driving data.

https://stacks.braintank.ai/d/W4407785632.html2026-11-01T00:00:00ZbraintankAI

Grading the intermediate steps of an AI search agent, rather than only its final answer, produced agents that coped better with unfamiliar tasks, a study reports.

https://stacks.braintank.ai/d/W7224109065.html2026-11-01T00:00:00ZbraintankAI

Sequencing more than five million immune cells from 1,925 people shows how inherited variation tunes gene expression, often in just one or a few cell types.

https://stacks.braintank.ai/d/arxiv-2610.38568.html2026-11-01T00:00:00ZbraintankAI

A preprint from Xiaomi Research describes a training loop that watches its own learning signal and swaps in fresh, checkable questions, with no human-labelled data.

https://stacks.braintank.ai/d/arxiv-2610.39302.html2026-11-01T00:00:00ZbraintankAI

A preprint proves that one family of AI models learns the same representation across data types when trained on the right pairs, and calls next-token prediction an inefficient route.

https://stacks.braintank.ai/d/W7155156745.html2026-11-01T00:00:00ZbraintankAI

Scaling a plasma's starting current sheet with the size of the system erases a supposedly universal reconnection rate, according to new computer simulations.

https://stacks.braintank.ai/d/arxiv-2610.34073.html2026-11-01T00:00:00ZbraintankAI

A large-scale evaluation suggests that the way memory systems reorganise an agent's history can hide evidence, a cost the authors call memory tax.

https://stacks.braintank.ai/d/W7224107502.html2026-11-01T00:00:00ZbraintankAI

A preregistered analysis of 40 brain-imaging datasets finds that common measures of how well people reframe their feelings disagree, and none tracks prefrontal activity.

https://stacks.braintank.ai/d/arxiv-2610.36671.html2026-11-01T00:00:00ZbraintankAI

A decoding method from KAIST AI checks whether the picture would change the answer before processing it, cutting reported decoding computation to about a third.

https://stacks.braintank.ai/d/arxiv-2610.37666.html2026-11-01T00:00:00ZbraintankAI

ThinkWorld, a preprint framework from NVIDIA-affiliated researchers, rewards a robot model when the imagined outcome of a planned motion matches what real footage shows.

https://stacks.braintank.ai/d/W4417175683.html2026-11-01T00:00:00ZbraintankAI

In a benchmark where diagnosticians must decide what information to gather, a language-model system far outscored generalist doctors, under controlled conditions.

https://stacks.braintank.ai/d/arxiv-2610.39301.html2026-11-01T00:00:00ZbraintankAI

A preprint describes one shared encoder and one output schema for ten parsing tasks, with the authors reporting leading scores on document and multimodal benchmarks.

https://stacks.braintank.ai/d/W4404313154.html2026-11-01T00:00:00ZbraintankAI

Microscopy reveals two localised electronic states hiding among a metal's mobile electrons, an electronic counterpart to an effect already achieved with light.

https://stacks.braintank.ai/d/W7130561082.html2026-11-01T00:00:00ZbraintankAI

A survey of language-model research that draws on narratology finds work on reading stories ahead of work on writing them, and argues against a single test of ‘narrative quality’.

https://stacks.braintank.ai/d/W7139913235.html2026-11-01T00:00:00ZbraintankAI

Flying above nearly all of the atmosphere, SuperBIT measured the shapes of galaxies behind 30 colliding clusters, and the authors tested whether those shapes can be trusted.

https://stacks.braintank.ai/d/W7224084810.html2026-11-01T00:00:00ZbraintankAI

A study ties genetic variation to single-cell chromatin accessibility and finds that the genetic signal helps an AI model map how genes are switched on.

https://stacks.braintank.ai/d/arxiv-2610.37230.html2026-11-01T00:00:00ZbraintankAI

TAO, described in a preprint, picks which agent sessions get GPU time by weighing when their tools will return, what is cached where and how many tokens they carry.

https://stacks.braintank.ai/d/arxiv-2610.35684.html2026-11-01T00:00:00ZbraintankAI

A KAIST AI preprint steers video generation with geometry maps rebuilt from reference photos, so a subject stays the same as the camera circles it.

https://stacks.braintank.ai/d/W7224744201.html2026-11-01T00:00:00ZbraintankAI

Researchers adapted a causal-discovery method to hospital records from 1,937 COVID-19 patients, finding linked lingering symptoms and urging caution on treatment effects.

https://stacks.braintank.ai/d/arxiv-2610.37381.html2026-11-01T00:00:00ZbraintankAI

A preprint describes a vision-language-action model that learns compact physical tokens about how scenes will change, sidestepping the delay of generating video.

https://stacks.braintank.ai/d/arxiv-2610.34283.html2026-11-01T00:00:00ZbraintankAI

A preprint names a training pitfall called interface lock-in and reports that replaying mixed spatial data in time order improves video models on three benchmarks at once.

https://stacks.braintank.ai/d/W7154962243.html2026-11-01T00:00:00ZbraintankAI

Stimulation and recordings in 87 neurosurgical participants map four functional regions of the insula and an asymmetric pathway through which cognition may shape pain.

https://stacks.braintank.ai/d/arxiv-2610.36729.html2026-11-01T00:00:00ZbraintankAI

A video world model from Tencent Hunyuan follows successive typed instructions and camera moves while keeping track of what has already happened on screen.

https://stacks.braintank.ai/d/W4406975366.html2026-11-01T00:00:00ZbraintankAI

Polarised X-ray scattering uncovers a magnetic ripple with a definite twist, whose spin polarisation varies with momentum in line with the material's symmetry.

https://stacks.braintank.ai/p/W7213594818.html2026-10-31T00:00:00ZHui Bao et al.

Nano Letters

https://stacks.braintank.ai/p/W7224737457.html2026-10-31T00:00:00ZLiyu Chen et al.

ACS Nano

https://stacks.braintank.ai/p/2610.29927.html2026-10-30T00:00:00ZYifan Sun et al.

A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty. In this work, we develop this measure-based view of benchmark evaluation. This perspective allows us to identify and formalize two central failure modes: measure mismatch, where the benchmark difficulty distribution differs from the intended difficulty distribution, and coverage gaps, where uncovered regions along the difficulty axis prevent benchmarks from detecting meaningful localized training progress. We propose two diagnostic metrics that capture these two failure modes an

https://stacks.braintank.ai/p/2610.38331.html2026-10-30T00:00:00ZJayden Teoh

Multi-Token Prediction (MTP) is now a common ingredient in language-model pre-training, as it improves downstream performance and enables faster inference through speculative decoding. We identify a flaw in standard MTP training, in which a residual component of the MTP gradient interferes with both next-token and multi-token learning, increasingly so with scale. Specifically, the MTP cross-entropy gradient can be decomposed exactly into a KL self-distillation term that pulls each MTP head toward the model's own next-token prediction (NTP) distribution and improves speculative decoding, and a

https://stacks.braintank.ai/p/2610.38352.html2026-10-30T00:00:00ZChenyu Mu et al.

Producing long-form videos from narration requires interdependent decisions that remain coherent across shots. Existing agentic editors often use multi-stage workflows driven by general models, leaving the quality-cost trade-off of compact, task-adapted policies unclear. We address this gap with WeVisCut, a long-form script-to-video system that connects tool execution with policy learning. Its agent harness maintains shared production state so that tool feedback informs subsequent decisions and repairs. Because finished videos do not expose these decisions, our four-stage data pipeline combine

https://stacks.braintank.ai/p/2610.38425.html2026-10-30T00:00:00ZShaohan Yu et al.

Humans routinely consume multimodal content, where textual and visual information is naturally interleaved. Although Unified Multimodal Models (UMMs) can jointly generate text and images, multimodal interleaved generation remains largely underexplored. Progress in this area is hindered by two key limitations: (1) the lack of well-defined interleaved generation tasks and corresponding training data, and (2) the absence of fine-grained evaluation protocols that can reliably diagnose model performance across different capability dimensions. To this end, we present IntReal, comprising two compleme

https://stacks.braintank.ai/p/2610.21143.html2026-10-30T00:00:00ZYingzhuo Deng et al.

Spoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference. Lower-rate speech tokenizers shorten sequences but may introduce compression-quality trade-offs and costly replacement without breaking this coupling. To address this limitation, we propose CoSTA, a compatibility-preserving post-hoc framework that decouples generation and synthesis token spaces while retaining the original speech tokenizer and decoder. CoSTA extends the generation token space with compressed representations via mean-ini

https://stacks.braintank.ai/p/2610.37786.html2026-10-30T00:00:00ZTianci Xie et al.

Web agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, while generative construction can diverge from specified real websites. Capture-and-replay preserves observations, with interaction coverage constrained by captured content and replay capabilities. We present WebAnything, a fully automated framework that reconstructs source-grounded, reusable Web environments from real URLs without per-site manual implementation or repair after initial setup and authorization. An Explorer-Buil

https://stacks.braintank.ai/p/2610.38342.html2026-10-30T00:00:00ZHuangfei Jiang et al.

Financial agents must form forecasts and investment judgments from decision-time evidence, with predictions evaluated against subsequent outcomes. Existing benchmarks assess financial knowledge, research workflows, or forecasting, but evaluating the full research process requires a strictly frozen information set and professional evaluation criteria. We introduce Investor's Last Exam (ILE), an equity-research benchmark led by experienced buy-side investors and fund managers. Its three components are a versioned financial database spanning 3.43 million document versions and nine source categori

https://stacks.braintank.ai/p/2610.40454.html2026-10-30T00:00:00ZAdilet Metinov et al.

Retrieval-augmented generation commonly serves separate models for reranking and answering, duplicating model residency and passage processing. We introduce Glance, which equips the generator with a lightweight decision adapter and pointer head to score candidate passages from a shared prefill. Short relevance probes branch from the cached context, producing one score per passage without separately prefilling each query-passage pair. A single resident 9B backbone supports generation, general decisions and reranking, with approximately 0.1 GiB of additional memory per decision skill. After 39 m

https://stacks.braintank.ai/p/2610.38678.html2026-10-30T00:00:00ZLu Wang et al.

Recent advances in multimodal large language models have increased interest in interaction paradigms for video understanding. The evolution from offline to streaming and proactive interaction has gradually shifted models from passive responders toward more proactive agents that can decide when to answer. However, existing proactive paradigms typically assume that the information required to answer a query is complete, leaving models to only wait or answer. In the real world, however, information is often incomplete due to uncertainty from either the video or the user. In proactive paradigms, s

https://stacks.braintank.ai/p/2610.39664.html2026-10-30T00:00:00ZHaiyang Xu et al.

Agentic image editors decompose complex requests into steps, but when a step fails, they mostly retry with the same narrow set of tools and a judge that scores each output by eye directly. We argue that such agents are bounded by two spaces: the action space of what the agent can do, and the observation space of what its judge can see. On our expert-annotated benchmark, a general-purpose editor yields no usable result in over 40% of the cases, and no judge is reliable across edit types. We present EditAgent, which expands both spaces. It routes each step to general editors and specialized meth

https://stacks.braintank.ai/p/2610.39264.html2026-10-30T00:00:00ZPeitong Duan et al.

While frontier vision-language models demonstrate strong capabilities in user interface (UI) navigation, their high inference costs prohibit everyday use. Conversely, smaller models trained via supervised fine-tuning (SFT) or offline reinforcement learning often memorize static trajectories, leading to brittle execution and an inability to recover from errors. To address these limitations, we propose a novel online reinforcement learning framework that trains UI navigation agents directly on live iOS applications. Our framework decouples the agent into two specialized components: a Planning ag

https://stacks.braintank.ai/p/2610.40564.html2026-10-30T00:00:00ZYuda Song et al.

Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms. We show that this algorithmic separation is not fundamental. By deriving suitable RL objectives as maximum-likelihood fitting to model-generated target distributions, we place all of these stages under a single likelihood-based training algorithm, unified training. This perspective turns the foundation-model training lifecycle into a problem of choosing which data to train on, in what proportions, and when

https://stacks.braintank.ai/p/2610.40453.html2026-10-30T00:00:00ZYue Gong et al.

We present PhysInsert, a framework for physically plausible reference-based object insertion that jointly adapts objects and their surrounding scenes. Existing methods often rely on masked inputs, generation-based pairs, or training pairs with limited viewpoint variation, leaving pose and illumination adaptation and bidirectional object-scene interactions insufficiently constrained. We construct the Insert World dataset with strictly aligned Blender-rendered counterfactual pairs and object references across viewpoints and lighting conditions. Building on these data, Geometry-Guided Adaptation

https://stacks.braintank.ai/p/2610.40362.html2026-10-30T00:00:00ZQifan Yu et al.

Weight-normalized (WN) training is emerging as a compelling alternative to conventional weight decay (WD), with recent studies reporting substantial pretraining loss gains. We show that these gains are driven primarily by the induced effective learning rate (ELR) trajectory rather than by normalization alone. Matching ELR trajectories largely collapses the training loss dynamics of WN and WD. Schedule design should therefore target ELR rather than the nominal learning rate (LR) alone. Building on this principle, we propose AIR (Affine Inverse-square-Root decay), which combines a theoretically

https://stacks.braintank.ai/p/2610.39287.html2026-10-30T00:00:00ZJunli Liu et al.

Human cognition flexibly draws on a shared perceptual foundation to understand environments, attend to referenced entities, reason about spatial relationships, and act. Endowing aerial robots with an analogous functional organization remains challenging, as existing systems are often specialized for individual capabilities, dependent on offboard computation, or unable to sustain closed-loop operation on resource-constrained platforms. We present OpenFlyV2, a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding,

https://stacks.braintank.ai/p/2610.38174.html2026-10-30T00:00:00ZDebargha Ganguly et al.

We present EasyProver, a Lean proving system built from recursive agents with isolated contexts, asynchronous search, parallel tactic probing, and an elastic elaboration service. Using an off-the-shelf frontier model with no additional training, it proves 669/672 PutnamBench problems and all six IMO 2026 formalizations and resolves 597/600 NTP4VC verification conditions and all 189 VERINA tasks by proof or refutation. Because these benchmarks are close to saturation, we also introduce Aegis, which converts competitive programming problems into much harder verification tasks over real implement

https://stacks.braintank.ai/p/2610.39663.html2026-10-30T00:00:00ZBin Xu et al.

Video diffusion transformers generate high-quality videos, but the quadratic cost of 3D full attention dominates their inference. Dynamic block-sparse attention reduces this cost by computing exact attention only on key/value blocks selected from current activations. However, its fidelity degrades under aggressive sparsity: blocks are scored from coarse summaries or formed by costly global clustering, pruned blocks vanish from the softmax, and whole tiles are kept or dropped even when only a few of their keys matter. To address these limitations, we propose Accurate Block Attention (AB-Attenti

https://stacks.braintank.ai/p/2609.05881.html2026-10-30T00:00:00ZAditi Patodiya

Developers download quantized large language models (LLMs) from public registries to run them locally. None of the registries we examined documented functional testing of their files. We tested all 305 eligible code-capable GGUF tags (the llama.cpp format) across 15 model lines in the official Ollama library, plus 22 popular Hugging Face community files. A 15-task code screen identified suspects, which we retested on all 164 HumanEval+ tasks. Seven official tags (2.3%) solved at most six of them. All were low-bit quantizations of models with at most four billion parameters. We rebuilt five sus

https://stacks.braintank.ai/p/2610.36420.html2026-10-30T00:00:00ZTruman Hickok

Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, and existing SO-101 benchmarks run only on physical hardware with simple tasks and few held-out objects. We introduce SO-101 Bench, a simulated benchmark for language-conditioned spatial manipulation built on an Isaac Lab digital twin of a real SO-101 setup. It has four tasks scored as five families, 138 objects in seen, unseen-instance and unseen-class splits, 916 evaluation episodes with subsets f

https://stacks.braintank.ai/p/2610.34298.html2026-10-30T00:00:00ZZhenning Shi et al.

Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabilities part of the decision process. Model Context Protocol (MCP) and command-line interfaces (CLI) can expose the same semantic operations through markedly different model-facing representations. We study whether these representational differences change agent behavior when executable capabilities are matched. We introduce Matched Interface Comparison and Analysis (MICA), which renders MCP and CLI from a shared semantic action contract and route

https://stacks.braintank.ai/p/2610.23656.html2026-10-30T00:00:00ZXiaohang Yu et al.

Blockchain forensics is adversarial and open-ended: money launderers split, swap, and bridge funds, cut their trail at mixers or custodial services, and change tactics from one incident to the next. Investigators must keep adapting their tracing in response. Yet the immutability of public ledgers preserves every recorded movement in public view, making much of the investigation independently verifiable. We introduce ABF-Bench, an interactive benchmark that makes evidence-backed fund tracing a verifiable agent task, in which every claimed movement must cite ledger records the agent retrieved. A

https://stacks.braintank.ai/p/2610.25276.html2026-10-30T00:00:00ZYushi Lan et al.

Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible. Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content. We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold. We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diff

https://stacks.braintank.ai/p/2610.38293.html2026-10-30T00:00:00ZYanbin Yin et al.

Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 firm-quarters from 86 US companies. Agents read a fixed collection of filings, earnings-call transcripts, and news, then estimate company-specific income-statement drivers with cited evidence. A shared accounting calculator converts these estimates into GAAP diluted earnings per share (EPS), providing a common basis for comparing forecast assumptions. We evaluate six

https://stacks.braintank.ai/p/2610.38681.html2026-10-30T00:00:00ZYecheng Wu et al.

Self-distillation offers a way to improve reasoning models when a suitable external teacher is unavailable. However, common approaches rely on privileged information to construct a stronger self-teacher, limiting their use when only unlabeled prompts are available. In this paper, we introduce Weakening-Induced On-Policy Self-Distillation (WISD), which instead constructs a weaker counterpart of the original model and uses the contrast between the two as a training signal without requiring privileged information. We explore two ways to construct the weaker model: low-bit quantization and magnitu

https://stacks.braintank.ai/p/2610.39293.html2026-10-30T00:00:00ZRishav Pramanik et al.

Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time. Existing solutions, including the modern discrete-diffusion family, train from scratch on a mixture of token orderings, paying roughly 3x the compute of standard raster-scan training; discrete diffusion further sacrifices compatibility with the AR inference ecosystem (KV caching, vLLM) by construction. We argue that this cost

https://stacks.braintank.ai/p/2610.37695.html2026-10-30T00:00:00ZBo Zou et al.

In September 2026, a new large language model was released. Soon after, a new way of making explainer videos became very popular: people asked the model to write a program, and the program produced the video. "One-click video" content then appeared in large amounts on many platforms. On the surface, this is a rise in creative power at the level of the individual. We argue that, at a deeper level, it changes the structure of the content industry. This judgment rests on two observations. First, this wave of content is service-type content. People watch it to meet a need, not to enjoy the content

https://stacks.braintank.ai/p/2610.39423.html2026-10-30T00:00:00ZHina Otake et al.

Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in the generative process. We propose Formula-Driven Supervised Metric Learning (FDSML), which derives relational supervision from image-generation parameters beyond discrete class labels. FDSML combines FineFractalDB, our refined fractal dataset with higher intra-class variation and more stable structures, with a Parameter-Aware Triplet (PA-Triplet) Loss that assigns positives and negatives according to

https://stacks.braintank.ai/p/W7224744201.html2026-10-30T00:00:00ZKosar Abdollahi et al.

PLoS ONE

https://stacks.braintank.ai/p/W7221179570.html2026-10-30T00:00:00ZKeyin Chen et al.

Scientific Reports

https://stacks.braintank.ai/p/W7223204242.html2026-10-30T00:00:00ZEmma Sarfati et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224481444.html2026-10-30T00:00:00ZHanbing Nie et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224836018.html2026-10-30T00:00:00ZAbu Yousuf Md Abdullah et al.

Scientific Reports

https://stacks.braintank.ai/p/W7131891388.html2026-10-30T00:00:00ZRyan Tiew et al.

Physical Review A

https://stacks.braintank.ai/p/W7162118427.html2026-10-30T00:00:00ZZhaoyi Li et al.

Physical Review A

https://stacks.braintank.ai/p/W4406231517.html2026-10-30T00:00:00ZNicolò Botteghi et al.

Computer Methods in Applied Mechanics and Engineering

https://stacks.braintank.ai/p/W7224803771.html2026-10-30T00:00:00ZMarina D'Amato et al.

BMC Medicine

https://stacks.braintank.ai/p/W7224805465.html2026-10-30T00:00:00ZLeonel De La Cruz Bixano et al.

New Journal of Physics

https://stacks.braintank.ai/p/W7224757932.html2026-10-30T00:00:00ZFengrui Tian et al.

Applied Sciences

https://stacks.braintank.ai/p/W7224781355.html2026-10-30T00:00:00ZFeihong Liu et al.

Applied Sciences

https://stacks.braintank.ai/p/W7224786985.html2026-10-30T00:00:00ZShakhnoza Muksimova et al.

Applied Sciences

https://stacks.braintank.ai/p/W7224796159.html2026-10-30T00:00:00ZQiang Wang et al.

Applied Sciences

https://stacks.braintank.ai/p/W7224853653.html2026-10-30T00:00:00ZZengjie Luo et al.

PLoS ONE

https://stacks.braintank.ai/p/W7224830066.html2026-10-30T00:00:00ZIsabella Schlattner et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224943923.html2026-10-30T00:00:00ZCarlos R. P. Hartmann et al.

Nature Energy

https://stacks.braintank.ai/p/W7224742153.html2026-10-30T00:00:00ZPankaj Singh et al.

Sensors

https://stacks.braintank.ai/p/W7224749438.html2026-10-30T00:00:00ZJunxi Qiang et al.

Sensors

https://stacks.braintank.ai/p/W7224784668.html2026-10-30T00:00:00ZGuoqiang Pang et al.

Sensors

https://stacks.braintank.ai/p/W7224853189.html2026-10-30T00:00:00ZLaihu Peng et al.

Sensors

https://stacks.braintank.ai/p/W7224867589.html2026-10-30T00:00:00ZFuchao Xie et al.

Sensors

https://stacks.braintank.ai/p/W7224871004.html2026-10-30T00:00:00ZHuawen Li et al.

Sensors

https://stacks.braintank.ai/p/W7224734487.html2026-10-30T00:00:00ZJiaqi Li et al.

Remote Sensing

https://stacks.braintank.ai/p/W7224740315.html2026-10-30T00:00:00ZBo Wang et al.

Remote Sensing

https://stacks.braintank.ai/p/W7224775840.html2026-10-30T00:00:00ZRedha Touati et al.

Physics in Medicine and Biology

https://stacks.braintank.ai/p/W7224814814.html2026-10-30T00:00:00ZAmir Jabbarpour et al.

Physics in Medicine and Biology

https://stacks.braintank.ai/p/W7224824287.html2026-10-30T00:00:00ZGemma C. Solomon et al.

Proceedings of the National Academy of Sciences

https://stacks.braintank.ai/p/W7117307079.html2026-10-30T00:00:00ZQingming Huang et al.

Physical Review Letters

https://stacks.braintank.ai/p/W7224827137.html2026-10-30T00:00:00ZYusheng Zhang et al.

Advanced Materials

https://stacks.braintank.ai/p/W7224804756.html2026-10-30T00:00:00ZHangbing Guo et al.

Nature Communications

https://stacks.braintank.ai/p/W7224827701.html2026-10-30T00:00:00ZStefan Sutorius et al.

Angewandte Chemie International Edition

https://stacks.braintank.ai/p/W7224806679.html2026-10-30T00:00:00ZXiaolin Zhang et al.

PLoS ONE

https://stacks.braintank.ai/p/W7224766628.html2026-10-30T00:00:00ZJi‐Chi Liu et al.

Advanced Functional Materials

https://stacks.braintank.ai/p/W7224925127.html2026-10-30T00:00:00ZTenzin Desel et al.

Nature Communications

https://stacks.braintank.ai/p/W7224843773.html2026-10-30T00:00:00ZJohn P. Kasarda et al.

PLoS ONE

https://stacks.braintank.ai/p/W7224914178.html2026-10-30T00:00:00ZLucas A. Saavedra et al.

PLoS ONE

https://stacks.braintank.ai/p/W7153517455.html2026-10-30T00:00:00ZStijn Voerman et al.

The Journal of Physiology

https://stacks.braintank.ai/p/W7224774922.html2026-10-30T00:00:00ZAiden Houcek et al.

The Journal of Physiology

https://stacks.braintank.ai/p/W7224782235.html2026-10-30T00:00:00ZJolene K. Berg et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224788819.html2026-10-30T00:00:00ZS. Saeed Ahmadi et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224923331.html2026-10-30T00:00:00ZOu Xu et al.

Scientific Reports

https://stacks.braintank.ai/p/W7224816718.html2026-10-30T00:00:00ZA.A. Ermoshina et al.

International Journal of Molecular Sciences