Knowledge catalogue
arXiv:2610.02516v1 Announce Type: cross Abstract: Zero-shot classifiers are useful for routing user requests to specialized LLM tasks, but scoring every request against a large candidate set is expens
arXiv:2610.02417v1 Announce Type: new Abstract: Advances in experimental instrumentation and automation generate increasingly rich datasets, but turning experimental observations into microscopic unde
arXiv:2610.02247v1 Announce Type: cross Abstract: Artificial intelligence increasingly serves as a natural-language interface to complex technical systems, letting people accomplish sophisticated task
arXiv:2610.03473v1 Announce Type: new Abstract: We propose UniDynamics, a diffusion-based framework for future 4D dynamic scenes (RGB, depth, and optical flow) generation from a single event-RGB pair,
I have been running the same midrange card for a couple of years now, and every model release sends me back to the price sheets. The performance gains are real but they keep landing next to price tags
I just sent the September edition of my sponsors-only monthly newsletter. If you are a sponsor (or start a sponsorship now) you can access it here. This month: More Fable class models A pricing war 3D
I use ChatGPT a lot. Probably a lot more than is reasonable. I read David Robinson’s Atlantic piece today about leaving OpenAI and his criticism of its culture of trial-and-error / “iterative deployme
arXiv:2610.02116v1 Announce Type: new Abstract: A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disa
arXiv:2610.00926v1 Announce Type: cross Abstract: Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative
arXiv:2610.00870v1 Announce Type: new Abstract: We present an educator-guided LLM pedagogical agent for scaffolded feedback in conceptual database design. Integrated into an entity--relationship diagr
arXiv:2610.01138v1 Announce Type: new Abstract: Concurrent actions in large language model (LLM) agent environments require arbitration even when each proposal is individually valid. We implement a ty
arXiv:2610.01645v1 Announce Type: new Abstract: Fairness conclusions in clinical prediction can depend strongly on both the metrics reported and the demographic resolution at which performance is eval
arXiv:2610.01834v1 Announce Type: new Abstract: Judgment models such as jev{} return, in a single call and without reasoning text, a probability for each described option. This makes them attractive a
arXiv:2610.00840v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) such as RoBERTa represent text using contextual word embeddings (CWEs), which alter the embeddings asso
Cool eval. Simply ask an LLM “Land or Water?” and give it a latitude and longitude coordinate as text. Ask 16,200 times, plot as image. The models know. From compressing the internet. results for all
arXiv:2610.00360v1 Announce Type: cross Abstract: Reinforcement learning (RL) for dexterous manipulation must discover finger-object contacts and then control the object precisely; the action noise th
arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active d
arXiv:2610.00006v1 Announce Type: new Abstract: Pretrained Vision Transformers encode whether two image patches belong to the same object. This IsSameObject signal is decodable from frozen patch embed
arXiv:2610.00913v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models provide strong behavioral priors for robotic manipulation, yet efficiently adapting them to downstream tasks remains
arXiv:2610.01619v1 Announce Type: cross Abstract: The environmental impact of deep learning has attracted increasing attention over the past decade. Existing studies mainly focus on the energy and car
arXiv:2610.00693v1 Announce Type: new Abstract: Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentra
arXiv:2610.00573v1 Announce Type: new Abstract: Query-aware keyframe selection enables multimodal large language models (MLLMs) to process long videos using only a small set of question-relevant frame
arXiv:2610.01637v1 Announce Type: new Abstract: In recent decades, artificial intelligence has made significant progress in understanding and interacting with images. One of the important applications
arXiv:2610.01076v1 Announce Type: new Abstract: Structured electronic health records (EHRs) contain a patient's clinical trajectory as a sequence of clinical codes. Answering clinical questions from s
arXiv:2610.00061v1 Announce Type: new Abstract: Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Dir
arXiv:2606.16914v2 Announce Type: replace Abstract: Safety evaluations test a policy on prompts that omit the incentive information deployment supplies: a commission, a performance score, a dashboard
arXiv:2610.01712v1 Announce Type: new Abstract: In-context learning (ICL) enables a pretrained model to infer a task from demonstrations without updating its parameters. While much of the existing the
arXiv:2601.22169v2 Announce Type: replace-cross Abstract: Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e
arXiv:2610.00035v1 Announce Type: new Abstract: Predicting student performance from educational interaction data requires models that are both accurate and sufficiently transparent to support meaningf
arXiv:2610.02196v1 Announce Type: cross Abstract: We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills,
arXiv:2602.20294v2 Announce Type: replace-cross Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approac
Cloudflare AI Gateway now supports native web search API integration in partnership with Ceramic.ai, Exa, and Linkup. Developers can now inject real-time web context into model inference calls via AI
arXiv:2610.01201v1 Announce Type: new Abstract: Object permanence, keeping track of an object's identity and position while it is occluded, is central to video representations that track, predict and
arXiv:2610.01231v1 Announce Type: cross Abstract: Generative artificial intelligence has reduced the cost of producing plausible symbolic artefacts, leading recent organisation scholarship to identify
arXiv:2610.01668v1 Announce Type: new Abstract: Bio-inspired tendon-driven rigid-soft coupled dexterous fingers exhibit strong nonlinearity and configuration-dependent sensitivity, making accurate mod
arXiv:2610.00778v1 Announce Type: new Abstract: In goal-conditioned reinforcement learning (GCRL), quasimetric learning models goal-reaching costs as quasimetric distances, connecting local constraint
arXiv:2610.00422v1 Announce Type: cross Abstract: Neural combinatorial optimization typically assumes a centralized solver that reads the whole instance. We study the opposite: combinatorial optimizat
arXiv:2610.00318v1 Announce Type: cross Abstract: Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction.
arXiv:2610.00881v1 Announce Type: new Abstract: Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation s
arXiv:2610.00574v1 Announce Type: new Abstract: Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as
arXiv:2610.00902v1 Announce Type: cross Abstract: One way to make AI systems safe is to shape what the system is: its objective and dispositions. We take a complementary route: treat the agents' chara
arXiv:2610.00430v1 Announce Type: cross Abstract: Autonomous large language model (LLM) agents increasingly interact in network environments where adversarial content can propagate between agents. Kno
arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memor
arXiv:2610.00620v1 Announce Type: cross Abstract: Grokking refers to the delayed emergence of validation-set generalization after a model has already overfit the training set. Although first observed
arXiv:2610.01917v1 Announce Type: cross Abstract: Latent visual reasoning equips vision--language models with continuous intermediate states that can process visual evidence without explicit textual r
arXiv:2610.00268v1 Announce Type: new Abstract: Multimodal graph learning faces a fundamental challenge: new classes may emerge after deployment, while models are trained with a fixed label space. Exi
arXiv:2610.00538v1 Announce Type: cross Abstract: A real-time auditory scene analyzer (ASA) aims to carry out the tasks of locating, separating and classifying the sound sources present in a given aco
arXiv:2603.11546v2 Announce Type: replace Abstract: Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multi
arXiv:2610.01098v1 Announce Type: new Abstract: Illusory matches between distinct yet visually similar 3D surfaces--doppelgangers--remain a fundamental obstacle for large-scale, in-the-wild 3D reconst
Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more
arXiv:2610.01291v1 Announce Type: new Abstract: Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world p
arXiv:2610.00682v1 Announce Type: new Abstract: Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering
arXiv:2610.01356v1 Announce Type: new Abstract: Stable port-Hamiltonian neural networks certify asymptotic stability by construction. Yet, their Hamiltonian is a global Lyapunov function with a single
arXiv:2610.00730v1 Announce Type: new Abstract: Mixed-integer linear programs (MILP) model many real-world decision problems, motivating machine-learning methods that exploit recurring structure to ac
arXiv:2610.00970v1 Announce Type: cross Abstract: Recent advances in 3D reconstruction have progressed from per-scene optimization to feed-forward inference, and semantic scene understanding has follo
arXiv:2610.00400v1 Announce Type: cross Abstract: Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or
arXiv:2610.00002v1 Announce Type: new Abstract: We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent 'subjects' with resistance
arXiv:2610.00233v1 Announce Type: new Abstract: When an informed adversary shares the audience of a constrained signalling channel, the signal that best protects the truth is the signal that best desc
arXiv:2110.14961v4 Announce Type: replace Abstract: Modelling interactions is critical in learning complex dynamical systems, namely systems of interacting objects with highly non-linear and time-depe
arXiv:2610.00031v1 Announce Type: new Abstract: Street-view imagery is increasingly used to infer urban attributes, but predictive accuracy alone does not reveal how much a photograph contributes beyo