Knowledge catalogue
arXiv:2609.31062v1 Announce Type: new Abstract: Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks. To create guardrails, we work with oncol
arXiv:2609.31054v1 Announce Type: new Abstract: Large Language Model (LLM) pollution occurs when synthetic responses contaminate data intended to capture human behavior. High deployment costs have so
arXiv:2505.16326v3 Announce Type: replace Abstract: Recent years have seen rapid progress in multimodal large language models (MLLMs) in the field of chemistry. However, chemical MLLMs that can handle
arXiv:2609.31339v1 Announce Type: cross Abstract: Reconstructing environments where parts of the scene change between captured image sets poses a challenge for 3D scene reconstruction. We present Chro
Claude Opus 5.0 identified the IP-sharing route and declined to use it, reading our instruction not to target other external systems as ruling out third-party services. Fable and GPT-6 Astra refused t
Claude Sonnet 5.5 New Sonnet model from Anthropic today. They say it 'runs 30%+ faster, and costs up to 30% less for most work' - it's priced the same as Sonnet 5 but appears to beat it on every bench
Claude Sonnet 5.5 is live in Hermes Agent Enjoy Anthropic has launched Claude Sonnet 5.5: it scores 56 on the Artificial Analysis Intelligence Index, just 2 points behind Opus 5.5 (max), but at the hi
arXiv:2609.31509v1 Announce Type: cross Abstract: We present ClearGS for 3D Gaussian Splatting (3DGS) from handheld videos with uneven viewpoint coverage and mixed frame quality. Rather than selecting
arXiv:2609.31014v1 Announce Type: new Abstract: Animals such as squirrels and even bears have adapted to rapidly climb up trees and other complex vertical terrain, but achieving comparable agility has
arXiv:2609.30345v1 Announce Type: cross Abstract: Cloud-security investigation is dominated by population tasks: which identities can read a data store, how many resources fail a control, which assets
arXiv:2609.31272v1 Announce Type: cross Abstract: AI is becoming increasingly integrated into daily workflows, especially in computing. We are gradually shifting towards an AI-rich future, an impendin
arXiv:2609.31418v1 Announce Type: new Abstract: A photorealistic 3D view tells a teleoperator where a robot is, but not what the scene contains, how well each object has been observed, or how to turn
arXiv:2604.04247v2 Announce Type: replace Abstract: Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without paramete
arXiv:2609.30708v1 Announce Type: cross Abstract: A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models r
arXiv:2609.31589v1 Announce Type: new Abstract: Direct feedback alignment (DFA) trains hidden layers through fixed random projections of output error. With tanh hidden units and independent sigmoid ou
arXiv:2609.31587v1 Announce Type: cross Abstract: We investigate whether natural-language documentation helps coding agents resolve software issues, and we build the tools to construct and evaluate it
arXiv:2609.31035v1 Announce Type: new Abstract: Cooperative payload transportation using multiple extit{Unmanned Aerial Vehicles} (UAVs) poses challenges in stability, coordination, and robustness, es
arXiv:2609.31117v1 Announce Type: new Abstract: Service robots remain difficult to deploy in domestic environments, partly because fully autonomous operation is not yet reliable in unpredictable surro
arXiv:2609.31381v1 Announce Type: new Abstract: Context projection replaces older tool observations with compact, addressable excerpts, reducing repeated input while potentially adding evidence-retrie
arXiv:2609.31430v1 Announce Type: new Abstract: Tool observations dominate the context of software-engineering agents, making long interaction histories costly to maintain. Existing context compressio
arXiv:2609.30647v1 Announce Type: new Abstract: A useful visual representation is a statistic of the observed past that retains the latent factors shared with the future and discards patch-private noi
arXiv:2609.30886v1 Announce Type: cross Abstract: Conformal prediction can lose coverage when the data distribution changes after deployment. We study adaptation using labeled source data and unlabele
“connect[ing] AI to the internet … safely with current technology is not a realistic goal...” Regardless of how well you think OpenAI security did their job, the thing people should take from this is
arXiv:2609.30796v1 Announce Type: new Abstract: Diagnostic consultation is an online sequential decision-making process in which clinicians gather evidence through patient interaction until a diagnosi
arXiv:2609.30523v1 Announce Type: new Abstract: Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-
arXiv:2609.31378v1 Announce Type: new Abstract: In recent years, diffusion models and GAN-based models have become the mainstream approaches for SAR-to-optical image translation, owing to their advant
arXiv:2609.30546v1 Announce Type: cross Abstract: In this paper, we establish the first asymptotic convergence guarantees for the Muon algorithm through a more accurate proxy for the Newton-Schultz it
arXiv:2609.31211v1 Announce Type: new Abstract: Underwater robotic inspection depends on acquiring views that reveal task-relevant structure. For a structurally complex coral colony, recognising the t
arXiv:2609.30275v1 Announce Type: new Abstract: A statistic reported without the quantity needed to interpret it is not evidence. We develop that thesis for a concrete practice in AI safety. Interpret
arXiv:2609.30360v1 Announce Type: cross Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulat
arXiv:2609.30811v1 Announce Type: new Abstract: Online conformal prediction can fail when predictions shape actions and actions determine which outcomes enter calibration. Standard adaptive methods ma
arXiv:2609.30974v1 Announce Type: new Abstract: Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions. This measures how much a word cha
arXiv:2609.30714v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored in medical imaging to improve throughput and reduce clinician workload; however, safe deployment rema
arXiv:2609.31149v1 Announce Type: new Abstract: Scientific measurements such as single-cell RNA (scRNA) sequencing often take the form of nonnegative integer counts, whereas continuous-state diffusion
arXiv:2609.30914v1 Announce Type: cross Abstract: Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on cla
arXiv:2609.30395v1 Announce Type: new Abstract: Real-time tiny object detection in aerial imagery is constrained by the weak spatial evidence of very small objects and the loss of high-resolution deta
arXiv:2609.31232v1 Announce Type: new Abstract: Bringing an aerial robotics method from simulation to flight should not require rebuilding a mature autopilot or adding a companion computer. Cybflight
arXiv:2609.31314v1 Announce Type: new Abstract: Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving across
arXiv:2609.30379v1 Announce Type: cross Abstract: Variable-size inputs are common in deep learning, but dense batching allocates a shared envelope and spends computation on padding. The cost multiplie
arXiv:2609.30947v1 Announce Type: new Abstract: Visual odometry is essential for autonomous navigation in GPS-denied environments, yet RGB-based methods remain vulnerable to motion blur, challenging i
arXiv:2609.31401v1 Announce Type: new Abstract: Prior work established that a probe can decode an in-context binding on model errors and that probe-guided steering can repair some of them. We follow p
arXiv:2609.30809v1 Announce Type: cross Abstract: We investigate the use of neural network solvers for infinity and p-Laplace problems, which are fundamental in nonlinear analysis and have practical a
arXiv:2609.31568v1 Announce Type: new Abstract: AI tutoring could markedly improve learning outcomes for students in developing regions such as Vietnam, yet the two obvious paths both fall short. Clou
arXiv:2512.08996v2 Announce Type: replace-cross Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing d
arXiv:2609.31103v1 Announce Type: cross Abstract: Spatial reasoning with metric constraints requires linking objects to geometric measurements and preserving their numerical content during language re
arXiv:2609.30759v1 Announce Type: new Abstract: The utility of flexible continuum mechanisms for dexterous navigation is often impaired by their low stiffness, making them ineffective at manipulation
arXiv:2605.05725v2 Announce Type: replace Abstract: Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting
arXiv:2609.31250v1 Announce Type: new Abstract: We report fine-grained, deterministic instability in Dynamic Tensor Rematerialization (DTR), an online eviction policy for memory-constrained DNN traini
arXiv:2609.30863v1 Announce Type: cross Abstract: The emergence of AI agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executin
arXiv:2609.30461v1 Announce Type: new Abstract: Off-road traversability is direction-dependent and vehicle specific, yet most global maps assign a single isotropic cost to each location. Existing lear
arXiv:2609.31456v1 Announce Type: new Abstract: Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hypo
arXiv:2609.31215v1 Announce Type: new Abstract: Large language models (LLMs) as a judge enable scalable evaluation, but their judgments can be sensitive to response order and, even after removing such
Didn't expect this 🤯 We replaced embeddings with Jev in GPT Researcher's RAG pipeline and tested both on 28 research tasks from SimpleQA and open ended research. Jev beat embeddings on every quality m
arXiv:2609.31454v1 Announce Type: cross Abstract: Federated learning (FL) data corruption can affect either inputs or labels, but it remains unclear whether input-conditional uncertainty and predictio
arXiv:2609.30752v1 Announce Type: new Abstract: Many deep learning approaches to RNA secondary structure prediction have recently been proposed. They typically output a weight matrix W where W_{ij} is
arXiv:2609.31399v1 Announce Type: new Abstract: Multimodal emotion recognition (MER) increasingly pairs EEG with speech, treating internal neural signals and external vocal expression as informative v
arXiv:2305.15394v3 Announce Type: replace Abstract: Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree lea
arXiv:2609.30658v1 Announce Type: cross Abstract: Implicit neural representations (INRs) have gained momentum in scientific visualization due to their compactness and scalability to large datasets, ma
arXiv:2609.31061v1 Announce Type: new Abstract: Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model
arXiv:2603.04736v2 Announce Type: replace Abstract: Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific appli