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    diagram-design
    38 editorial diagram types for Claude Code, Codex, and Pi. Self-contained HTML + SVG. No shadows. No Mermaid slop.cathrynlavery
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Anthropic
Research
16天前更新
  • 01
    Discovering cryptographic weaknesses with Claude
    Summary Using Claude Mythos Preview, researchers at Anthropic have discovered improved ways to attack cryptographic algorithms (the mathematical methods used to keep online data private). The first attack significantly weakens HAWK, a digital signature scheme that was built for a post-quantum world. The second identifies a new way to attack round-reduced AES, the most widely used symmetric cipher. These are substantial research advances, but they do not currently affect any production systems. T
  • 02
    Project Pilot: Can AI control a drone?
    Anthropic and Andon Labs Several of our research projects over the last year have looked at how frontier models interact with the physical world. In Project Vend , AI models ran a small shop; Project Fetch was an early look at robots as the intermediary between digital models and physical objects. As we recently noted in Project Fetch: Phase two , we’re already seeing improvements in model capability such that their ability to use off-the-shelf robots is on track to approach the ease with which
  • 03
    How Canada uses Claude: Findings from the Anthropic Economic Index
    Le français suit. Key findings Based on the latest release of the Anthropic Economic Index, Canada is at the forefront of Claude adoption. Canada represents 2.6% of global Claude.ai traffic and ranks 8th overall by total volume. Usage per capita is more than four times higher than would be expected given the size of its population. Canada’s high adoption rate is generally consistent with its high-income economy, but still stands out within its peer group. Among the top ten countries that collect
  • 04
    Claude’s values across models and languages
    When someone asks Claude a question with no universal right answer—say, whether to take a new job or how to handle conflict with a friend—how Claude responds inevitably reflects certain values. 1 The values we want Claude to reflect are outlined at a high level in Claude’s constitution , but no document can anticipate every value that might emerge across the millions of conversations that happen every day on Claude.ai . Instead, we seek to cultivate in Claude’s responses “good judgment and sound
  • 05
    Claude plays robotics
    Shmuel Berman, Michael Ilie, Jia Deng, and Daniel Freeman Do language models’ strengths transfer to robotics, a domain which requires the synthesis of logical skills and precise 3D understanding? Can a model perceive a scene, understand a particular robot’s state, and issue actions that reliably effect change in the physical world? We ran tests to find out. We gave several language models control over a range of robot bodies—including classic control toys, a simulated quadruped and humanoid, a r
  • 06
    An off switch for dual-use knowledge in AI models
    This post describes research conducted by AE Studio in collaboration with Anthropic. A frontier AI model is, among other things, a large store of knowledge. Some of that knowledge is dual use , meaning it can be used for good or for bad. For example, knowledge of cybersecurity can help patch critical security vulnerabilities, or it can be used to exploit them. Knowledge of virology can help a researcher create a vaccine, but it can also help a malicious actor design a deadly pathogen. Ideally, w
  • 07
    A global workspace in language models
    As you read this sentence, circuits in your brain are adjusting your posture, controlling your breathing, and transforming lines and curves on the screen into recognizable words. Most of this processing is invisible to you. But some of what takes place in your brain you do have access to—an image that pops into your head, or a deliberate plan you make about where to go shopping. Neuroscientists and philosophers sometimes refer to the latter type of brain activity as “consciously accessible,” to
  • 08
    Anthropic Economic Index report: Cadences
    Introduction One year ago, most Claude usage took the form of a conversation between a user and an assistant. With the rapid growth of Claude Code and Cowork, Claude sessions now increasingly consist of long-running agentic tasks. Chat transcripts no longer fully capture how people are using AI, and our methods for studying Claude’s economic impacts have had to adapt. To keep pace, we made several changes to our data pipeline for the Economic Index. In this version, we: Sample data at a higher r
  • 09
    Project Fetch: Phase two
    Michael Ilie, C. Daniel Freeman, and Kevin K. Troy In August 2025, we ran an experiment to see how much Claude could help Anthropic employees—who were not robotics experts—perform sophisticated (and amusing) tasks with an off-the-shelf robotic quadruped (henceforth, a robodog). We called this Project Fetch. We found that access to our state-of-the-art model at the time (Claude Opus 4.1) helped one team substantially outperform the other, who had to rely only on the internet and their own ingenui
  • 10
    Agentic coding and persistent returns to expertise
    Key findings Building on prior work , we introduce a framework for studying interactive agentic coding based on a privacy-preserving analysis of ~400,000 Claude Code sessions from between October 2025 and April 2026. We evaluate the composition of tasks, human-AI collaboration, and success rates. In a typical session, people make most of the planning decisions (what to do) and Claude makes most of the execution decisions (how to do it). The greater domain expertise a person brings to a session,
  • 11
    Paving the way for agents in biology
    Written by Laura Luebbert. Based on research by Ferdous Nasri, Sarah Gurev, Patrick Varilly, Krithik Ramesh, Nuala A. O’Leary, Jonah Cool, Bernhard Y. Renard, Pardis Sabeti, and Laura Luebbert. In this post, Laura Luebbert argues that we need to make biological data infrastructure more agent-friendly. As a case study, she and her team tasked scientific research agents (Claude, Biomni Open Source (Biomni OSS) 1 , Edison Analysis, 2 GPT) to retrieve the sequence data from NCBI Virus, a database vi
  • 12
    Measuring LLMs’ impact on N-day exploits
    Winnie Xiao, Tim Abbott, Nicholas Carlini, Newton Cheng, David Forsythe, Keane Lucas, Milad Nasr, and Shikhar Sakhuja For the last few months, we’ve been writing about large language models’ cybersecurity capabilities. For the most part, we’ve focused on zero-days—vulnerabilities that are unknown to the software’s maintainers. But a large fraction of real-world harm comes from N-days : vulnerabilities that have already been publicly disclosed, but only patched on some devices. Attackers exploit
  • 13
    Making Claude a chemist
    We’re working with world-class synthetic, computational, and analytical chemists to make Claude better at chemistry. In this post, we share our first work as part of this effort, in which Anthropic chemist, David Kamber, examines how Claude performs on a chemist’s most common analytical input, an NMR spectrum. When working with molecules, chemists move between hand-drawn structures on a whiteboard, instrument readouts, database query strings, and the technical notations of patents and publicatio
  • 14
    Mapping AI-enabled cyber threats: Insights from the LLM ATT&CK Navigator
    Kyla Guru, Alex Moix, and Jacob Klein We’ve spent the past year investigating how threat actors are weaponizing AI to conduct cyber operations. Today, we’re sharing a new analysis that maps these real-world attacks onto the MITRE ATT&CK® framework , a database of tactics and techniques used by cyberattackers. Doing so reveals patterns that challenge traditional assumptions about cybersecurity—for example, the level of risk a threat actor poses can be assessed via metrics like technical sophistic
  • 15
    What we learned mapping a year’s worth of AI-enabled cyber threats
    As AI transforms the nature of and methods behind cyberattacks, how well do the techniques and frameworks used by the security community hold up? In a new report, we seek to answer that question. We examine 832 accounts that were banned for malicious cyber activity between March 2025 and March 2026 and map them onto MITRE ATT&CK , a longstanding database of the tactics and techniques used by cyberattackers. We published some of these results in Verizon’s 2026 Data Breach Investigations Report (D
  • 16
    Coding agents in the social sciences
    Summary We present results from a survey of 1,260 social scientists about AI and coding agent use, fielded in February and March 2026. The vast majority of respondents (81%) have tried using AI chatbots in research, particularly for writing code and editing prose. But only 20% have adopted coding agents—tools like Claude Code that autonomously write and execute analysis code—into their work. There are sharp disparities in use of coding agents. Twice as many researchers with typically male names
  • 17
    Project Glasswing: An initial update
    Last month, we launched Project Glasswing , our collaborative effort to secure the world’s most critical software before increasingly capable AI models can be turned against it. Since then, we and our approximately 50 partners have used Claude Mythos Preview to find more than ten thousand high- or critical-severity vulnerabilities across the most systemically important software in the world. Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it
  • 18
    Measuring LLMs’ ability to develop exploits
    Newton Cheng, Keane Lucas, Winnie Xiao, Nicholas Carlini, and Milad Nasr Introduction Claude Mythos Preview ’s ability to develop exploits is a step-change over previous frontier models. This was one of our primary motivations for rolling out the model carefully through Project Glasswing rather than through a general release. Mythos Preview is capable of finding complex vulnerabilities, but what concerned us most in our internal testing was that Mythos Preview could both turn vulnerabilities int
  • 19
    2028: Two scenarios for global AI leadership
    We’re releasing a new paper that explains our views on the competition on AI between the US and China. It’s essential that the US and its allies stay ahead of authoritarian governments like the Chinese Communist Party, or CCP. AI will soon become powerful enough to be used to repress citizens at unprecedented scale, and even to alter the balance of power among nations . And since AI is advancing more quickly by the day, we have only a limited period of time to set the conditions of the competiti
  • 20
    Teaching Claude why
    Last year, we released a case study on agentic misalignment . In experimental scenarios, we showed that AI models from many different developers sometimes took egregiously misaligned actions when they encountered (fictional) ethical dilemmas. For example, in one heavily discussed example, the models blackmailed engineers to avoid being shut down. When we first published this research, our most capable frontier models were from the Claude 4 family. This was also the first model family for which w
  • 21
    Natural Language Autoencoders: Turning Claude’s thoughts into text
    When you talk to an AI model like Claude, you talk to it in words. Internally, Claude processes those words as long lists of numbers, before again producing words as its output. These numbers in the middle are called activations— and like neural activity in the human brain, they encode Claude’s thoughts. Also like neural activity, activations are difficult to understand. We can’t easily decode them to read Claude’s thoughts. Over the past few years, we’ve developed a range of tools (like sparse
  • 22
    Donating our open-source alignment tool
    In October 2025, we launched Petri , an open-source toolbox of alignment tests that can be applied to any large language model. Petri, which was developed as part of our Anthropic Fellows program, can be used to rapidly and easily test AI models for concerning tendencies like deception, sycophancy, and cooperation with harmful requests. It’s part of our efforts to develop alignment tools that are open and useful for the whole AI development community. Petri has been part of our alignment assessm
  • 23
    Focus areas for The Anthropic Institute
    At The Anthropic Institute (TAI), we’ll be using the information we can access from within a frontier lab to investigate AI’s impact on the world, and sharing our learnings with the public. Here, we’re sharing the questions that drive our research agenda. Our agenda focuses on four areas for research: Economic diffusion Threats and resilience AI systems in the wild AI-driven R&D In Core Views on AI Safety , we wrote that doing effective safety research required close contact with frontier AI sys
  • 24
    How people ask Claude for personal guidance
    People don’t just come to Claude for code reviews or meeting summaries. They ask whether to take the job, how to talk to their crush, if they should move halfway across the world. Using our privacy-preserving analysis tool on a random sample of 1 million claude.ai conversations, we found that roughly 6% were people coming to Claude for personal guidance—seeking not just information but perspective on what to do next. In this study, we looked at what types of guidance people ask of Claude. We exp
  • 25
    Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench
    In this post, Brianna , a researcher on the discovery team, shares results from a recent bioinformatics benchmarking effort. Almost as soon as large language models could hold a conversation, people started asking how they’d stack up against human experts. Could models pass the bar exam? Could they answer medical licensing questions, or solve Olympiad math problems? Such benchmarks —self-contained sets of human-vetted problems designed to evaluate a capability of a model—have now become a source
  • 26
    Announcing the Anthropic Economic Index Survey
    The Economic Research team is launching the Anthropic Economic Index Survey, a monthly survey conducted through Anthropic Interviewer . Understanding AI's economic impact requires moving beyond the quantitative data we have today. Usage and diffusion metrics tell us how AI is being deployed, and traditional labor market indicators—like employment rates, wage trends, and layoffs—track what has already happened, often with meaningful delay. Both are essential, but neither captures how people exper
  • 27
    What 81,000 people told us about the economics of AI
    Key findings: Our recent survey of 81,000 Claude users shows that people who work in roles that are more exposed to AI have more concerns about AI-driven job displacement. These concerns are also higher among early-career respondents. Those in the highest- and lowest-paid occupations report the largest productivity gains, most commonly from increases in scope (doing new tasks). Respondents experiencing the largest speedups from AI express higher concern about job displacement. In order to inform
  • 28
    Automated Alignment Researchers: Using large language models to scale scalable oversight
    Large language models’ ever-accelerating rate of improvement raises two particularly important questions for alignment research. One is how alignment can keep up. Frontier AI models are now contributing to the development of their successors. But can they provide the same kind of uplift for alignment researchers? Could our language models be used to help align themselves? A second question is what we’ll do once models become smarter than us. Aligning smarter-than-human AI models is a research ar
  • 29
    Trustworthy agents in practice
    AI “agents” represent the latest major shift in how people and organizations are using AI. A couple of years ago, AI models were only broadly available as chatbots—simple question-and-answer machines. Now, through products like Claude Code and Claude Cowork , AI models can do much more: they can write and execute code, manage files, and complete tasks that span multiple applications. This represents a new frontier for governance. Agents are already making real productivity gains for our customer
  • 30
    Assessing Claude Mythos Preview’s cybersecurity capabilities
    Nicholas Carlini, Newton Cheng, Keane Lucas, Michael Moore, Milad Nasr, Vinay Prabhushankar, Winnie Xiao Hakeem Angulu, Evyatar Ben Asher, Jackie Bow, Keir Bradwell, Ben Buchanan, David Forsythe, Daniel Freeman, Alex Gaynor, Xinyang Ge, Logan Graham, Kyla Guru, Hasnain Lakhani, Matt McNiece, Mojtaba Mehrara, Renee Nichol, Adnan Pirzada, Sophia Porter, Andreas Terzis, Kevin Troy Earlier today we announced Claude Mythos Preview , a new general-purpose language model. This model performs strongly a
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Apple
Machine Learning Research
17天前更新
  • 01
    Understanding Alignment in Multimodal LLMs: A Comprehensive Study
    Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by producing responses that are inconsistent with the image content. A primary objective of alignment for
  • 02
    Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph
    While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimensional space, before the distortion that UMAP’s 2D projection introduces. We demonstrate the untapped potential of this internal representation, showing how standard graph algorithms applied to this graph enhance data sen
  • 03
    MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
    To operate effectively across diverse contexts, robots must not only perform manipulation tasks accurately but also adapt how their actions unfold to the task, object, and interaction setting. We ask whether this execution-level variation can be learned as a reusable behavioral factor shared across tasks. We present MoMo, a two-stage imitation-learning framework consisting of a spatiotemporal action tokenizer and a behavior-cloning transformer that takes task and a continuous motion-mode conditi
  • 04
    Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers
    Siri Expressive Voices synthesize rich, configurable speech in real time and entirely on device, powered by AFM 3 Core Advanced, Apple’s most powerful on-device foundation model. This work presents the memory-efficient audio synthesis architecture behind that capability: a detokenizer that converts the semantic audio tokens emitted by the foundation model into high-fidelity audio within the tight compute and memory budget of the Apple Matrix Coprocessor (AMX). We convert semantic audio tokens to
  • 05
    GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks
    Systematic failures of vision models on semantically coherent subsets, known as error slices, reveal limitations in robustness and evaluation. Existing slice discovery approaches largely model slices as clusters in representation space or combinations of predefined attributes. While effective for image-level classification, such formulations are insufficient for instance-level tasks such as object detection and segmentation, where failures often arise from contextual relational and spatially gro
  • 06
    LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning
    Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposition is essential for stability, extreme decomposition creates a “no-recovery bottleneck”. We show that this bottleneck becomes critical due to highly non-uniform error distribution, where consistent errors on a few “hard” steps become irreversible. To address this, we propose Lookahead-Enhanced Atom
  • 07
    Environment-free Synthetic Data Generation for API-Calling Agents
    Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models. Given only API specifications, our method genera
  • 08
    Accelerating Text-to-Video Generation with Calibrated Sparse Attention
    Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-to-token connections consistently yield negligible scores across various inputs, and their patterns often repeat across queries. Thus, the attention computation in these cases can be skipped with little to no effect on the result. This ob
  • 09
    RayRoPE: Projective Ray Positional Encoding for Multi-View Attention
    We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can be adaptive to the geometry of the underlying scene. We find that prior (absolute or relative) encoding schemes for multi-view attention do not meet the above desiderata, and present RayRoPE to address this gap. RayRoPE represents patch positions based on associ
  • 10
    LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
    Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded. We introduce LVSum, a human-annotated benchmark for evaluating long-form video summarization with fine-grained temporal alignment. LVSum comprises 72 diverse videos spanning 13 domains with an average duration of 16 minutes, each annotated with up to
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  • 01
    Deep Divide-and-Reduce in Symbolic Regression
    arXiv:2608.02628v1 Announce Type: new Abstract: Symbolic regression (SR) is the task of discovering underlying patterns from data and representing them using mathematical expressions. Current machine learning approaches to SR often lack a profound understanding of the intrinsic mathematical and physical principles governing these expressions. While the pioneering AI Feynman method leverages the mathematical properties underlying the data, its expression simplification mechanism suffers from a naYusong Deng, Yanjie Li, Weijun Li
  • 02
    Multimodal Auto-regressive Transformer Surrogate for Modeling Variable Operations and Quantifying Uncertainty in Geological Carbon Storage
    arXiv:2608.02629v1 Announce Type: new Abstract: The use of variable well perforation and injection strategies can improve the efficiency of geological carbon storage operations. We develop a new multimodal auto-regressive transformer surrogate to model these operations under geological uncertainty. A modified SEAM CO2 geomodel, which involves a faulted system with three stacked aquifers, is considered. The two injection wells are perforated in stages, from bottom to top, with the stage durationsYifu Han, Louis J. Durlofsky
  • 03
    LLMs Can Annotate Attribution Graphs
    arXiv:2608.02632v1 Announce Type: new Abstract: Circuit tracing is an exciting technique for revealing the internal computation of language models, but it requires a time-intensive manual step of grouping individual features or MLP neurons into supernodes. We present a simple pipeline for automating this step: directly presenting feature descriptions to a language model that groups them into supernodes. Using automated interpretability metrics, we confirm that supernodes generated by our pipelinAmeen Patel, Max Zhang, Nathan Hu
  • 04
    GeoID-PINN: Identifiability-Aware Regional Epidemic Inference with Geographic Coupling
    arXiv:2608.02633v1 Announce Type: new Abstract: Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately. We introduce GeoID-PINN, a physics-informed neural network (PINN) for susceptible-infectious-recovered-deceased (SIRD) dynamics. The model represents spatial dependence with a row-stochastic source-composition matrix whose rows assign nonnegative source weights that sum to one. We regularize this maWeixiong Hua, Fan Bu
  • 05
    Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
    arXiv:2608.02662v1 Announce Type: new Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are prohibitively costly, and machine-learning surrogates can be opaque and encode assumptions about system dynamics, limiting generalizability. Pretrained transformers mapping synthetic ODE trajectories to equations offer interpretable alternatives, promising transfer without system-specific equation knowFarbod Faraji, Francesco Belardinelli
  • 06
    CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study
    arXiv:2608.02663v1 Announce Type: new Abstract: Accurate ICU mortality prediction requires modeling irregular clinical observations across heterogeneous entity types. Existing sequence models handle irregular sampling but ignore typed relational structure; existing graph models assume fixed-interval inputs. We introduce the Continuous-Time Heterogeneous EHR Graph (CT-HEG) schema and evaluate which architectural choices drive predictive performance. CT-HEG encodes each ICU stay as a typed, timestMohammad Nasir Uddin, Rahnuma Tabassum Orpita, Asaduzzaman Anik, Eklachur Rahman Bhuiyan, Marjahan Risalat, SM Wali Ullah, Asif Ahamed
  • 07
    Sphere Retraction Normalizations
    arXiv:2608.02668v1 Announce Type: new Abstract: Residual connections are the de facto mechanism for training deep neural networks stably. Geodesic Normalization (GeoNorm) recasts them on a Riemannian manifold, orthogonalizing each layer output against the current hidden state and applying the resulting update through the Riemannian exponential map. Every hidden state thus keeps a constant $\ell_{2}$-norm, confining the residual stream to a hypersphere. The exponential map, however, is only one mJie Zhang, Cheng-Fang Su, Yi-Jui Huang, Min-Te Sun
  • 08
    Learning Molecular Representations from Cellular Phenotypes with Structure Preservation
    arXiv:2608.02688v1 Announce Type: new Abstract: Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learning methods often optimize cross-modal alignment without considering the intrinsic organization of chemical space, resulting in distorted molecular representations and loss of structural information. We propose \textbf{PhenMol}, a structure-preserving framework for phenotypXuan Lin, Jingyu Sheng, Tengfei Ma, Li Sun, Dapeng Xiong
  • 09
    GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection
    arXiv:2608.02690v1 Announce Type: new Abstract: On-device training of deep neural networks is fundamentally constrained by the computational and memory costs of large-scale datasets. Coreset selection offers a practical solution by retaining only a compact subset of real training samples. However, existing gradient-based methods commonly rely on gradients computed at a single model snapshot and employ greedy or pursuit-based selection procedures, limiting their ability to capture evolving optimiHetian Liu, Jin Cui, Mengcheng Shi, Yanbin Hu, Xinyue Long, Boran Zhao, Pengju Pen
  • 10
    Output-Aware Rotation for INT2 KV-Cache Quantization
    arXiv:2608.02691v1 Announce Type: new Abstract: The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quantization increasingly important. However, existing rotation-based INT2 methods optimize cache statistics or proxy errors before the complete attention readout, even though the model is ultimately affected by the error propagated through attention and the output projection $W_O$. To address this mismatcVincent-Daniel Yun, Woosang Lim, Minsoo Cheong, Sunwoo Lee, Murali Annavaram, Sai Praneeth Karimireddy, Sungjoo Yoo
  • 11
    PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks
    arXiv:2608.02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources. However, the inherent heterogeneity and complexity of clinical real-world data pose significant challenges to structured data analysis and AI application. This heterogeneity includes missing values, multiple time points, diverse modalities, and inconsistent formats andJulia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan
  • 12
    Measuring Explainer Stability via Attribution Separability
    arXiv:2608.02697v1 Announce Type: new Abstract: Attribution methods (AMs) assign an importance score to each feature and are widely adopted to explain black-box models. However, most methods can produce variable attribution scores due to stochastic components in their definition. In this paper, we propose a distribution-based framework to capture the stability of attribution scores. In particular, our approach allows to understand the degree of separability in the ranked attribution vector and oEddie Conti, \'Alvaro Parafita, Axel Brando
  • 13
    NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory
    arXiv:2608.02700v1 Announce Type: new Abstract: Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit quantized models. Existing CIM-oriented quantization methods mainly minimize ideal quantization error, ignoring the hardware noise floor and thus causing inefficient precision allocation. We propose NANQ, a noise-aware mixed-precision non-uniform quantization framework for analog CIM. NANQ models magnituYizhe Chen, Wenshuai Yao, Saiya Wang, Yuannuo Feng, Wenbo Qi, Kechao Tang, Ngai Wong, Wenyong Zhou, Wang Kang
  • 14
    Can Training Logs Make Model Comparisons More Precise?
    arXiv:2608.02705v1 Announce Type: new Abstract: Comparing stochastically trained models requires estimating both a performance difference and its uncertainty from repeated runs. We study whether training logs from those same runs can make such comparisons more precise. Because training-log covariates are produced during training rather than measured before it, we use arm-specific covariate adjustment: each model is adjusted only with statistics from its own runs, and the raw mean difference remaWei-Jung Huang
  • 15
    Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures
    arXiv:2608.02709v1 Announce Type: new Abstract: Virtual nodes give message-passing neural networks a simple global communication route, but the standard node--VN--node pipeline compresses the graph into one homogeneous state and broadcasts it identically to every node. Building on the Two-Radius analysis of Mishayev et al., we ask how auxiliary virtual memory can relieve this finite-capacity bottleneck without self-attention. We identify two requirements. First, the global memory should be factoF\'elix Marcoccia
  • 16
    Neural Networks with Local Converging Inputs for Efficient Options Pricing Models
    arXiv:2608.02778v1 Announce Type: new Abstract: We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for pricing multi-asset options. The most concise input format for NNLCI has been introduced, offering substantial convenience and efficiency. NNLCI uses a neural network to locally correct solutions from a coarse mesh and a refined mesh (relative to the coarse one), requiring only a minimal amount of high-fHarris Cobb, Wenbo Hao, Yingjie Liu
  • 17
    Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from Training to Deployment
    arXiv:2608.02786v1 Announce Type: new Abstract: AI systems can fail silently. The failure propagates through training loops, evaluation pipelines, and production monitoring stacks until downstream harm makes it visible. This paper introduces evaluation blindness: a measurement function M exhibits evaluation blindness with respect to failure class F when it produces readings indistinguishable from a healthy state while the system is actually failing, with no auxiliary signal flagging the gap. ThePriyanka Bajaj (Independent Researcher)
  • 18
    Topological Simplification in Predictive Coding Networks
    arXiv:2608.02816v1 Announce Type: new Abstract: We study the topology of learned representations in predictive coding networks (PCNs), a neuro-inspired bidirectional architecture, using a quantitative layer-wise persistent homology analysis. We train well-performing PCNs on a synthetic classification dataset ($\geq 99.9\%$ test accuracy) and on MNIST ($\geq 95\%$ test accuracy), and measure how topological features change across layers for different architectures and activation functions. We finAdam Shaw, Jiayu Li, Michael Sperling, Michael Kim, Alvin Jin
  • 19
    Wiring Beats Blending: What Transfers Between Transformer Sizes -- and What Doesn't
    arXiv:2608.02829v1 Announce Type: new Abstract: Model families train every size from scratch. Can a pretrained large model be converted into a smaller sibling? We characterize the 1.4B->410M conversion in the Pythia family end-to-end: (i) representations align strongly across sizes (ridge R^2=0.84) while parameters align weakly; (ii) dense weight projection is functionally destructive -- provably not an assembly artifact -- because basis mixing breaks rotary, per-head, GELU, and LayerNorm structRavi Satya Durga Prasad Yenugula
  • 20
    NOMADD: Numerical Optimization of Models Adapting to Data Drift
    arXiv:2608.02845v1 Announce Type: new Abstract: Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively. These issues are challenging to mitigate in real time because labeled data may not be immediately available, or re-training a model could be impractical. While tools exist to reduce drift, they are typically bespoke to neural network architectSwapn Shah, Keith Burghardt
  • 21
    Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization
    arXiv:2608.02868v1 Announce Type: new Abstract: Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies that can be prohibitively expensive and struggle to adapt to dynamic post-disaster conditions. In this study, we propose a cost-aware Bayesian optimizaBoyang Xu, Mostafa Reisi Gahrooei, Mohammad Ilbeigi, Hao Yan
  • 22
    Contrast-invariant deep ptychography neural networks
    arXiv:2608.02869v1 Announce Type: new Abstract: Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability. We address this scaling mismatch using a factorization strategy which decouples the learned object texture from measurement scaling, enabling a single trained network to produce measurement-consistent reconstructions across varying illumination conditions. This requires predicting the learned object in real anAlbert Vong, Steven Henke, Oliver Hoidn, Hanna Ruth, Junjing Deng, Apurva Mehta, David Shapiro, Alexander Hexemer, Nicholas Schwarz
  • 23
    Maglev: Sliding Recurrent Memory
    arXiv:2608.02870v1 Announce Type: new Abstract: We introduce \ours{}, a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training. \ours{} consists of two coupled models: a prefiller $Q$, which leverages full attention\footnote{In practice, we use interleaved full and sliding-window attention for $Q$, as this yields stronger performance. The essential requirement is that $Q$ be more expressive than $P$, withBo Liu, Qiang Liu
  • 24
    GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits
    arXiv:2608.02877v1 Announce Type: new Abstract: Causal discovery recovers directed structure from observational data and is increasingly used in clinical settings to support mechanism reasoning and fairness audits of predictive models. Path-specific counterfactual fairness asks whether a protected attribute influences an outcome through illegitimate pathways, but these estimands are defined relative to a supplied causal graph and therefore inherit whatever errors the discovery step introduces. DNitish Nagesh, Elahe Khatibi, Thomas Dean Hughes, Mahdi Bagheri, Pratik Gajane, Amir M. Rahmani
  • 25
    Population-Robust Feature Selection via Generalized Welfare Optimization
    arXiv:2608.02887v1 Announce Type: new Abstract: Choosing which features to collect is a deployment decision: the same limited questionnaire, test panel, or sensor set may need to serve several heterogeneous populations. Standard feature-selection methods typically optimize for one large population, while existing robust approaches tend to learn one shared model for every population. We introduce PopFS, a method for learning one shared, deployable feature set that is robust to population differenRuiqi Lyu, Alistair Turcan, Bryan Wilder
  • 26
    Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models
    arXiv:2608.02893v1 Announce Type: new Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables. However, Markov Decision Processes (MDPs) are inherently stochastic. We address this by formalising counterfactual policy optimisation under probabilistic nondeterministic causal models, which properly separates latent confounding from irreducible stochasticity, and here propose a first pJessica Lally, Milad Kazemi, Nicola Paoletti, David Watson, Sander Beckers
  • 27
    AnchorKV: Anchor-Residual KV Cache Compression
    arXiv:2608.02901v1 Announce Type: new Abstract: The key-value (KV) cache is the primary memory bottleneck in long-context LLM inference. Existing approaches attack it from opposite ends: eviction methods permanently discard tokens, degrading performance whenever a discarded token later proves essential, while quantization methods retain all tokens at low precision but offer limited compression. We propose AnchorKV, a compression scheme that shrinks the cache by $20\times$ without discarding a siMalik Khalaf, Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster
  • 28
    Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering
    arXiv:2608.02907v1 Announce Type: new Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. However, existing contrastive training methods typically treat all unmatched query-document pairs as equally informative negatives, which is problematic because many unmatched documents may still be semantically relevant or partially useful. We propose Bayesian Data Reweighting, a probabilistic frameworkJingchen Sun, Shaobo Han, Ruiyi Zhang, Naresh Kumar Devulapally, Ming Liu, Yitao Long, Vishnu Suresh Lokhande, Changyou Chen
  • 29
    Forecasting Revenue with its Customer-Base Drivers: When and Why Coordination Helps
    arXiv:2608.02911v1 Announce Type: new Abstract: Revenue forecasts guide acquisition budgets, demand planning, and customer-based valuations, yet an aggregate forecast does not show whether change reflects acquisition, repeat purchasing, spending per order, or offsetting movements. Using weekly transaction panels for 966 companies in 25 industries, the authors develop the Customer-Based Multi-task Transformer (CBMT), which learns shared structure, retains separate primitive forecasts, and alignsKyeongbin Kim, Daniel McCarthy, Dokyun Lee
  • 30
    When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index
    arXiv:2608.02938v1 Announce Type: new Abstract: Graph attention normalizes neighborhood scores with softmax, the maximum-entropy choice under Shannon statistics. But homophilic and heterophilic graphs want different attention shapes, and one fixed normalization cannot serve both. We propose \textbf{LTGA} (\textbf{L}earnable \textbf{T}sallis \textbf{G}raph \textbf{A}ttention), a graph attention layer whose Tsallis entropic index $q$ is learned jointly with the weights, interpolating continuouslyKleyton da Costa, Bernardo Modenesi
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  • 30343
    TJX Call $145
    Call · 行权价 $145 · 到期 2026-09-25 · 量/OI：343.4 · 成交量：1,717 · 未平仓：5 · 最新价：$2.04 · 隐波：0.2139%
  • 31341
    TSLA Call $505
    Call · 行权价 $505 · 到期 2026-10-02 · 量/OI：341 · 成交量：341 · 未平仓：1 · 最新价：$1.12 · 隐波：0.5232%
  • 32337
    SBSW Call $15
    Call · 行权价 $15 · 到期 2026-09-18 · 量/OI：336.63 · 成交量：2,693 · 未平仓：8 · 最新价：$0.2 · 隐波：0.6136%
  • 33334
    EYPT Call $2.5
    Call · 行权价 $2.5 · 到期 2026-12-18 · 量/OI：334.33 · 成交量：10,030 · 未平仓：30 · 最新价：$3.3 · 隐波：1.9689%
  • 34319
    TTWO Call $290
    Call · 行权价 $290 · 到期 2026-10-16 · 量/OI：319.25 · 成交量：1,277 · 未平仓：4 · 最新价：$3.1 · 隐波：0.447%
  • 35313
    MSTR Call $128
    Call · 行权价 $128 · 到期 2026-08-28 · 量/OI：313.38 · 成交量：19,116 · 未平仓：61 · 最新价：$2.59 · 隐波：0.8766%
  • 36304
    DUOT Call $11
    Call · 行权价 $11 · 到期 2026-09-18 · 量/OI：304.1 · 成交量：3,041 · 未平仓：10 · 最新价：$0.75 · 隐波：0.8669%
  • 37303
    BEEM Call $2.5
    Call · 行权价 $2.5 · 到期 2026-09-18 · 量/OI：303.18 · 成交量：3,335 · 未平仓：11 · 最新价：$0.1 · 隐波：1.67%
  • 38301
    PSNL Call $22.5
    Call · 行权价 $22.5 · 到期 2026-09-18 · 量/OI：301 · 成交量：301 · 未平仓：1 · 最新价：$0.7 · 隐波：0.6732%
  • 39300
    CRCL Call $120
    Call · 行权价 $120 · 到期 2026-09-04 · 量/OI：299.67 · 成交量：899 · 未平仓：3 · 最新价：$0.46 · 隐波：1.0228%
  • 40294
    WULF Call $22
    Call · 行权价 $22 · 到期 2026-10-02 · 量/OI：293.71 · 成交量：6,168 · 未平仓：21 · 最新价：$0.37 · 隐波：0.9764%
  • 41293
    ASST Call $21
    Call · 行权价 $21 · 到期 2026-09-04 · 量/OI：293 · 成交量：879 · 未平仓：3 · 最新价：$0.64 · 隐波：1.0965%
  • 42287
    LYB Call $67.5
    Call · 行权价 $67.5 · 到期 2026-10-16 · 量/OI：287 · 成交量：287 · 未平仓：1 · 最新价：$4.2 · 隐波：0.4003%
  • 43280
    RUM Call $2
    Call · 行权价 $2 · 到期 2026-08-21 · 量/OI：280 · 成交量：560 · 未平仓：2 · 最新价：$6.93 · 隐波：0%
  • 44278
    WOLF Call $45
    Call · 行权价 $45 · 到期 2027-09-17 · 量/OI：277.78 · 成交量：5,000 · 未平仓：18 · 最新价：$7.4 · 隐波：1.0617%
  • 45275
    HOOD Call $108
    Call · 行权价 $108 · 到期 2026-09-11 · 量/OI：275 · 成交量：275 · 未平仓：1 · 最新价：$6.82 · 隐波：0.644%
  • 46266
    ASML Call $2180
    Call · 行权价 $2180 · 到期 2026-09-04 · 量/OI：265.8 · 成交量：1,329 · 未平仓：5 · 最新价：$0.85 · 隐波：0.4983%
  • 47264
    RUM Call $3.5
    Call · 行权价 $3.5 · 到期 2026-08-21 · 量/OI：263.5 · 成交量：527 · 未平仓：2 · 最新价：$5.5 · 隐波：10.512%
  • 48263
    NVDA Call $212.5
    Call · 行权价 $212.5 · 到期 2026-09-02 · 量/OI：262.83 · 成交量：1,577 · 未平仓：6 · 最新价：$8.5 · 隐波：0.4761%
  • 49252
    ORBS Call $1.5
    Call · 行权价 $1.5 · 到期 2026-09-25 · 量/OI：252 · 成交量：252 · 未平仓：1 · 最新价：$0.07 · 隐波：1.9983%
  • 50251
    SLB Call $52.5
    Call · 行权价 $52.5 · 到期 2026-10-16 · 量/OI：251 · 成交量：502 · 未平仓：2 · 最新价：$3.5 · 隐波：0.3254%
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