Knowledge catalogue
arXiv:2610.09590v1 Announce Type: cross Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefin
arXiv:2610.09045v1 Announce Type: cross Abstract: We study the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models, with the goal of
arXiv:2610.09350v1 Announce Type: cross Abstract: Learning from demonstrations (LfD) provides a framework for inferring unknown constraints from locally optimal, constraint-satisfying expert behavior.
arXiv:2610.10125v1 Announce Type: cross Abstract: Household robots need to continually navigate to different objects in the same environment, many of which are small and portable, such as tools and to
arXiv:2610.09511v1 Announce Type: new Abstract: Aerial object detection faces substantial scale and density variations. Small objects are easily degraded by downsampling and feature compression, while
LightOn OCR-3 is now available on OpenDocRouter, priced at 0.28 / 1m input and 1.40 / 1m output (it is ~$3.19 / 1k pages on ParseBench). We benchmarked it on ParseBench. It is on the pareto frontier f
arXiv:2610.09604v1 Announce Type: new Abstract: This paper presents a lightweight and versatile learned optimizer that dynamically recombines gradient history, represented as averages over disjoint ti
arXiv:2610.09872v1 Announce Type: cross Abstract: Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, wh
arXiv:2610.10138v1 Announce Type: new Abstract: Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication
arXiv:2610.06647v2 Announce Type: replace Abstract: Reinforcement learning has greatly advanced the capabilities of large language models, but its memory demands remain a barrier to broader adoption.
arXiv:2610.08858v1 Announce Type: new Abstract: Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventio
arXiv:2508.10838v3 Announce Type: replace Abstract: Learning disparity in occluded regions remains difficult for unsupervised stereo matching. Photometric supervision lacks valid target-view correspon
arXiv:2605.24928v2 Announce Type: replace Abstract: Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel covera
arXiv:2607.18130v2 Announce Type: replace Abstract: Finetuning methods for foundation models usually change weights, prompts, or hidden states, while leaving the residual topology fixed. We ask whethe
arXiv:2610.09092v1 Announce Type: new Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generati
arXiv:2610.10351v1 Announce Type: cross Abstract: Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational
arXiv:2610.09652v1 Announce Type: cross Abstract: Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the t
Meta published a paper that might end the transformer era. For the last seven years, every major AI, ChatGPT, Claude, Gemini, has been built on the exact same architecture. Transformer. But Transforme
arXiv:2606.22272v2 Announce Type: replace Abstract: Applying pre-trained language models to new domains through full fine-tuning is computationally expensive and prone to catastrophic forgetting. To a
Lunara introduces a novel Diffusion Mixture Transformer architecture with fewer than 10B active parameters for modeling artistic intelligence in image generation. Its CAT training algorithm iterativel
arXiv:2610.09830v1 Announce Type: new Abstract: Video deepfakes targeting a specific individual, the Person-of-Interest (POI), are the most harmful ones, and, since a public figure is abundantly recor
arXiv:2610.09063v1 Announce Type: cross Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability
arXiv:2610.10359v1 Announce Type: cross Abstract: We introduce MultiFly, a real-world, low-altitude UAV dataset for semantic perception across RGB, thermal, LiDAR, and radar modalities. MultiFly provi
arXiv:2602.03317v2 Announce Type: replace-cross Abstract: Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating
arXiv:2610.10459v1 Announce Type: new Abstract: Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of hetero
arXiv:2610.10318v1 Announce Type: new Abstract: Social media is a rich source of real-time public sentiment, but widely used sentiment analysis tools are often applied without understanding their limi
Here is the story: Nvidia has published this work (with source code available) called dreamDojo which is a world model for robotics based of their prior work Cosmos 2.5 which is cited about 100 times
arXiv:2610.09084v1 Announce Type: cross Abstract: In neutral-atom quantum computers, atoms are moved to target positions along paths of empty positions, and a target position may be reserved for one s
arXiv:2610.10303v1 Announce Type: new Abstract: The standard Transformer architecture relies on a rigid pattern that alternates Attention and Feed-Forward Network (FFN) layers. Despite its widespread
arXiv:2610.09185v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electroca
arXiv:2610.09397v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame ac
arXiv:2610.09577v1 Announce Type: new Abstract: We study finite-horizon online resource allocation with i.i.d. requests and an endogenous Markov state on a finite state space: each action affects the
tldr: Open sourced my prompt to game suite: https://github.com/ndamiano/ai-agent-test, it's MIT licensed, and this runs well on my 5090 with 64gb ram, but any model that can handle tool calls can mana
arXiv:2610.10384v1 Announce Type: new Abstract: Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world.
arXiv:2610.10499v1 Announce Type: new Abstract: Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially
arXiv:2610.09040v1 Announce Type: new Abstract: Constraint handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, a
arXiv:2610.10361v1 Announce Type: new Abstract: Personalized federated learning combines shared representations with client-specific predictors, but the contribution of a server weighting rule can be
arXiv:2610.09837v1 Announce Type: cross Abstract: Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed
arXiv:2610.08826v1 Announce Type: new Abstract: Full-sphere panoramic cameras let fixed monitoring systems and mobile robots track people in every direction, but a planar bounding box does not fully d
arXiv:2610.10276v1 Announce Type: cross Abstract: An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak repairs, where the goal is to co
arXiv:2610.10003v1 Announce Type: new Abstract: Style transfer lacks a reliable evaluation standard: ground truth is inherently ill-defined, and existing automatic metrics often fail to reflect human
arXiv:2610.10324v1 Announce Type: new Abstract: Pretrained models for cell and nuclear instance segmentation differ substantially in architecture, pretraining data and objectives, parameter count, inf
arXiv:2610.10455v1 Announce Type: new Abstract: Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post
arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric infor
arXiv:2610.09956v1 Announce Type: cross Abstract: Probing the hypothesis space after seeing the data remains valid under possibilistic inferential models (IMs), provided the significance level stays f
arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters t
arXiv:2610.08813v1 Announce Type: new Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potenti
arXiv:2610.09709v1 Announce Type: new Abstract: Can we determine whether a foundation model will generalize out-of-distribution (OOD) before any target data is available? Existing diagnostics require
arXiv:2511.17776v2 Announce Type: replace Abstract: We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-m
arXiv:2603.11938v2 Announce Type: replace-cross Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must
arXiv:2610.09223v1 Announce Type: new Abstract: Lip movements can match the timing of speech without matching the spoken sounds. We introduce PVSync, a unified model for audio-visual offset estimation
arXiv:2610.09033v1 Announce Type: new Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment rout
arXiv:2610.10497v1 Announce Type: new Abstract: We introduce QuadTok, a novel framework for visual tokenization and autoregressive image generation. Compared to traditional approaches using 2D grids o
arXiv:2610.09392v1 Announce Type: new Abstract: Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated unc
arXiv:2610.09670v1 Announce Type: new Abstract: Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstru
Having a lot of fun experimenting with three.js. Still very far from even a demo but its tons of fun. After this project I'll want to try a godot workflow. 3.8FN truly feels like Claude 4.6 at home. U
Gears of War: E-Day leads the charge on GeForce NOW this week, bringing Marcus Fenix and Dom Santiago’s first fight against the Locust Horde to the cloud with GeForce RTX-powered performance. A new wa
arXiv:2610.09780v1 Announce Type: new Abstract: Diffusion models excel at image synthesis, but they remain limited in their ability to reliably satisfy structured spatial reasoning constraints. In con
arXiv:2610.09004v1 Announce Type: new Abstract: Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot univ
arXiv:2610.09778v1 Announce Type: cross Abstract: Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system stat