Knowledge catalogue
arXiv:2609.29730v1 Announce Type: new Abstract: Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling
arXiv:2609.29530v1 Announce Type: new Abstract: Validating a model on a time series asks for three things at once: each training run should use most of the sample (sufficiency), the test sets should t
arXiv:2609.29674v1 Announce Type: cross Abstract: Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an
arXiv:2609.29664v1 Announce Type: new Abstract: Reinforcement learning (RL) has proven effective in enhancing the reasoning performance of large language models (LLMs), particularly in complex mathema
arXiv:2609.29103v1 Announce Type: new Abstract: Accurate state estimation (tracing) of Deformable Linear Objects (DLOs) such as cables is a critical challenge for data centers, manufacturing, construc
arXiv:2609.30134v1 Announce Type: new Abstract: Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly. Retrieval p
arXiv:2609.29405v1 Announce Type: cross Abstract: We adapt Reinforce Adjoint Matching (RAM), a reward-based post-training method, to generative speech enhancement (SE). Starting from a pretrained SE m
arXiv:2609.29633v1 Announce Type: cross Abstract: We present TTLab's submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as to
At Google Cloud, we are committed to delivering the best managed experience backed by open source software. Today, we’re announcing the general availability of Memorystore for Valkey 9.1, which achiev
arXiv:2609.29118v1 Announce Type: new Abstract: LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor retrieval remains difficult when attitude or sensor mo
arXiv:2609.28792v1 Announce Type: new Abstract: Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have o
arXiv:2609.29547v1 Announce Type: cross Abstract: Agentic AI is sold on a simple promise: the system keeps acting when the user stops watching. That promise creates an accountability inversion. As ste
arXiv:2609.29189v1 Announce Type: new Abstract: Scalable oversight aims to verify the behaviour of agents whose capabilities exceed those of their overseers. AI debate has been proposed as an oversigh
arXiv:2609.29387v1 Announce Type: new Abstract: Supervised synthetic CT (sCT) generation is commonly trained and evaluated as voxel-wise regression against registered reference CT images. In practice,
arXiv:2609.29095v1 Announce Type: cross Abstract: When a tool-using agent's write times out or returns a server error, the action may already have taken effect. Retrying blindly duplicates it -- a sec
arXiv:2609.29964v1 Announce Type: cross Abstract: General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them
WTAF - in literally the last hour, three new distinct insane OpenAI stories just broke: 1. OpenAI said they notified 'dozens of third parties' in safety and security incidents (likely similar to what
1. fully agree w @JeffLadish that AI companies aren’t ready to handle superintelligent AI. 2. OpenAI hasn’t even handled the current breed of agents (far from superintelligent) well. 3. the US governm
arXiv:2609.28044v1 Announce Type: new Abstract: Contact-rich aerial manipulation remains fundamentally challenging because interaction forces are directly transmitted to the aerial platform, often lea
arXiv:2609.27729v1 Announce Type: cross Abstract: In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or a
arXiv:2609.27824v1 Announce Type: new Abstract: Generative AI is rapidly entering adolescents' everyday lives during a critical period of cognitive, social and emotional development. Yet its adoption
arXiv:2609.27763v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on explicit chain-of-thought (CoT) reasoning and large context windows to achieve strong performance on complex tasks
Enterprises rely on mission-critical operational databases where performance slowdowns simply aren’t an option. Yet when even a few agents execute dense reasoning loops, the unpredictable surge in que
arXiv:2609.27180v1 Announce Type: cross Abstract: Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up. S
arXiv:2609.27753v1 Announce Type: new Abstract: Vision-language-action (VLA) models have become a powerful paradigm for generalist robotic manipulation, yet they are often reactive: the policy maps th
arXiv:2609.27269v1 Announce Type: new Abstract: Humanoid kicking requires coordinated whole-body motion and precise contact, while a banana kick demands contact mechanics that generate ball spin and a
arXiv:2609.27450v1 Announce Type: cross Abstract: Vision-language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale err
arXiv:2609.27513v1 Announce Type: new Abstract: Autoregressive robot policies learn continuous control by predicting discrete action tokens from observations. Different tasks often share local motions
arXiv:2608.24521v3 Announce Type: replace Abstract: Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches comm
arXiv:2609.27773v1 Announce Type: cross Abstract: As Large Language Models (LLMs) move from conversational assistants to advanced agentic systems, guardrail failures can convert adversarial intents in
arXiv:2609.28281v1 Announce Type: new Abstract: Interlocking brick assembly provides a representative testbed for evaluating real-world robotic manipulation capabilities, where diverse structural desi
arXiv:2609.27681v1 Announce Type: new Abstract: Automated assessment of the Critical View of Safety (CVS) in laparoscopic cholecystectomy requires both recognition of the three CVS criteria and anatom
arXiv:2609.27825v1 Announce Type: new Abstract: Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing tim
Cigarette companies approach Marlboro Man to create internal standards (to avoid the imposition of government standards). Andddd we're back to it. The new standards body Google, OpenAI & Anthropic are
arXiv:2609.27632v1 Announce Type: cross Abstract: We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic rule
arXiv:2609.27690v1 Announce Type: new Abstract: Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic
arXiv:2609.28004v1 Announce Type: cross Abstract: Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coheren
arXiv:2609.26853v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in
arXiv:2605.06043v2 Announce Type: replace Abstract: Domain generalization requires identifying stable representations that support reliable classification across domains. Domains may differ in low-lev
arXiv:2609.26920v1 Announce Type: cross Abstract: Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk s
arXiv:2609.27955v1 Announce Type: new Abstract: Prediction sets built from an LLM judge can undercover when deployment traffic changes the prevalence of task or policy groups. Weighted conformal predi
arXiv:2609.28085v1 Announce Type: cross Abstract: The job shop scheduling problem is a challenging combinatorial optimization problem, and recent reinforcement learning approaches using graph neural n
arXiv:2609.28175v1 Announce Type: new Abstract: Humanoid soccer contact skills require more than producing high-impact foot-ball contacts: the robot must close the loop over perception, approach, alig
arXiv:2609.27572v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing
arXiv:2609.28235v1 Announce Type: new Abstract: Image registration and fusion aim to establish spatial correspondences from misaligned multi-modal source images, and integrate complementary informatio
arXiv:2509.14959v4 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and a
arXiv:2609.28161v1 Announce Type: new Abstract: Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However,
arXiv:2609.28107v1 Announce Type: cross Abstract: Advances in generative modeling have recently been extensively employed in robotics for policy learning. In particular, Conditional Flow Matching (CFM
arXiv:2609.27794v1 Announce Type: new Abstract: 2D-3D registration remains brittle in challenging scenarios such as low overlap, occlusion, repetitive structures, and severe cross-modal ambiguity. A k
arXiv:2609.27150v1 Announce Type: new Abstract: Multi-agent debate (MAD) has emerged as a promising paradigm for improving the reasoning accuracy of large language models (LLMs) through iterative peer
arXiv:2609.27276v1 Announce Type: new Abstract: Long-horizon language-model agents accumulate reasoning traces, tool exchanges, and observations whose relevance changes with the current decision. Exis
arXiv:2609.28236v1 Announce Type: new Abstract: Long-horizon embodied interaction requires agents to retain and continually update information about the environment as they observe, act, and encounter
arXiv:2609.27860v1 Announce Type: new Abstract: A pointwise-unbiased one-bit compressor reconstructs every real input in expectation while transmitting one bit. For a scalar source P with CDF F, mean
arXiv:2609.27309v1 Announce Type: cross Abstract: Multi-agent Reinforcement Learning (MARL) trains a team of agents that share one environment and learn their policies together. Training maximizes the
arXiv:2507.16761v3 Announce Type: replace Abstract: Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical image analysis.
arXiv:2609.27710v1 Announce Type: new Abstract: Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high c
arXiv:2609.28182v1 Announce Type: new Abstract: Coordinating large population of flexible grid-edge devices can alleviate the need for time-consuming and capital-intensive network upgrades, and AI-bas
arXiv:2609.28378v1 Announce Type: cross Abstract: Humanoid control, leveraging human demonstrations, has achieved diverse, agile, and natural locomotion behaviors through reinforcement learning (RL).
Former Anthropic researcher Jacob Coxon became a media superstar after going public with his AI fears. He insisted that he wasn’t working with any third party organizations. Familiar sources told us a
arXiv:2609.28216v1 Announce Type: cross Abstract: Agentic engineering systems can edit repositories, run tools and tests, build firmware, synthesize schematics, and prepare deployable or manufacturabl