Knowledge catalogue
arXiv:2610.10633v1 Announce Type: cross Abstract: Screening in systematic reviews (SRs) is manual and time-consuming. Prior work has explored large language models (LLMs) for automating this step, but
arXiv:2511.07106v2 Announce Type: replace Abstract: Three-dimensional feature extraction and multi-task perception are fundamental components of modern autonomous driving systems. Although large image
arXiv:2506.15971v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) effectively bridges the domain gap between a labeled source domain and an unlabeled target domain, but as
arXiv:2610.10899v1 Announce Type: new Abstract: Speech quality predictors are increasingly used as rewards, yet no agreed measure of their hackability exists. The usual measurement has two flaws. Firs
arXiv:2610.11005v1 Announce Type: new Abstract: Safety-aligned language models often refuse a harmful request stated directly but answer the same request inside a role-play or narrative wrapper. We me
arXiv:2610.12393v1 Announce Type: new Abstract: Self-supervised multimodal representation learning has achieved remarkable success across diverse domains, yet capturing synergistic information remains
arXiv:2610.08966v2 Announce Type: replace Abstract: Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek det
I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter. Some results from my current benchmarks: M
Last month I created a portable memory format as a .txt file as a small project. I named it as: Memory Journal, in a shape of a skill card. It's essentially a large prompt wrapped in a text file. Init
I’m in Seoul building Mistral’s founding Korea team. Looking for kind, ambitious researchers and engineers to build and customise LLMs for real problems, from data and evals to RL and training at scal
arXiv:2610.10905v1 Announce Type: new Abstract: Sampling-based system identification estimates physically meaningful parameters by tuning a simulator to reproduce the target system dynamics, providing
arXiv:2610.12157v1 Announce Type: new Abstract: A robot that assists people must often act on what a person has shown rather than said: which of several identical cartons was pointed at, or which box
arXiv:2610.11174v1 Announce Type: new Abstract: While recent video generation models synthesize highly realistic visuals, they lack a genuine understanding of intrinsic real-world logic. Existing meth
arXiv:2610.11715v1 Announce Type: new Abstract: Hypernetworks that map a context directly to a LoRA adapter let a large language model carry that context in its weights, but prior work has demonstrate
arXiv:2610.12310v1 Announce Type: new Abstract: A single multimodal large language model (MLLM) struggles to excel simultaneously at detection, localization, description, and reasoning in multimodal i
It’s Friday 🎉 Here’s a look at this week’s announcements: — http://SynthID.com is now available globally in English, allowing anyone to use our detector portal to easily verify if images, video, or au
Live results, coding benchmarks included, agentic benchmarks included, domains and other classifications filters included. (Spoiler: DeepSeek-V4-Vision-Exp rules, but other models have their rule area
arXiv:2610.10854v1 Announce Type: new Abstract: Knowledge distillation is an established technique for improving the capabilities of small, efficient student models by training them with the represent
arXiv:2610.11160v1 Announce Type: new Abstract: Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, w
arXiv:2610.11538v1 Announce Type: cross Abstract: Deep randomized models fix hidden-layer parameters through random initialization and learn only closed-form readouts, typically adding depth by stacki
arXiv:2610.11599v1 Announce Type: cross Abstract: Journals and conferences have begun to screen submitted manuscripts for text written using large language models (LLMs). The reliability of this scree
arXiv:2610.12148v1 Announce Type: new Abstract: Quantum machine learning, and quantum neural networks (QNNs) in particular, are advancing fields with growing potential. Although systematic comparisons
OpenAI released 719 mathematics manuscripts on October 6, 2026 that were generated by an internal frontier model (not publicly available), covering 372 topic families—including a solution to the Navie
arXiv:2610.11546v1 Announce Type: cross Abstract: Solving high-dimensional structural model updating problems requires an algorithm capable of navigating complex, non-convex landscapes with correlated
arXiv:2610.12069v1 Announce Type: new Abstract: Realistic household simulation must capture not only diverse environments but also the lived-in object arrangements and spatial constraints that shape r
arXiv:2610.11253v1 Announce Type: new Abstract: Large language model agents are being increasingly deployed as autonomous scientists, designing experiments and inferring mechanistic world models with
arXiv:2512.05414v4 Announce Type: replace Abstract: Spell correction is still a challenging problem for many languages, especially low-resource languages (LRLs). While pre-trained language models (PLM
arXiv:2510.12843v2 Announce Type: replace Abstract: Spiking neural networks (SNNs) promise energy-efficient artificial intelligence on neuromorphic hardware but struggle with tasks requiring both fast
arXiv:2610.11954v1 Announce Type: new Abstract: CAD reconstruction methods assume a luxury reality rarely grants: unrestricted visual access to the object, photographed from any desired angle. Real ob
arXiv:2610.11264v1 Announce Type: new Abstract: Given the inherent unpredictability of packet loss in vehicular wireless communications, V2X collaborative perception can yield practical benefits only
arXiv:2610.12089v1 Announce Type: new Abstract: Long-horizon mobile manipulation requires a robot to navigate multi-room environments and execute a sequence of manipulation skills under a single natur
arXiv:2607.16204v2 Announce Type: replace Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments. Hand-curated environments with fixe
arXiv:2610.12067v1 Announce Type: cross Abstract: Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spe
arXiv:2610.11064v1 Announce Type: cross Abstract: A language model (LM) can usually answer the same question in more than one way, but reinforcement learning with verifiable rewards (RLVR) is indiffer
arXiv:2610.11312v1 Announce Type: new Abstract: Large-scale construction of medical vision-language model (VLM) benchmarks is increasingly feasible with richly annotated imaging datasets and large lan
🚀 Meet Qwen-Image-2.1-Turbo — create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation
arXiv:2610.11732v1 Announce Type: new Abstract: Large language model (LLM) agents for portfolio management learn from experience: they credit each experience in their memory with the outcome of the de
arXiv:2610.10759v1 Announce Type: new Abstract: Scene flow can capture low-level 3D motion displacements in dynamic scenarios. Early pairwise estimators relying on instantaneous two-frame motion lack
arXiv:2610.11316v1 Announce Type: cross Abstract: System One models output constrained decisions and probability distributions rather than free-form text generation. While prevailing paradigms rely on
arXiv:2610.09484v2 Announce Type: replace Abstract: Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and diffic
arXiv:2610.11328v1 Announce Type: new Abstract: Advances in foundation models are driving efforts to introduce agents to assist people in the physical world. Such agents require agentic spatial intell
arXiv:2610.11628v1 Announce Type: cross Abstract: Machine unlearning updates a trained model after records are deleted, aiming to match exact retraining without repeating the full training procedure.
arXiv:2610.11233v1 Announce Type: cross Abstract: A core challenge in short-video fact-checking is identifying which evidence is sufficient to support a verification conclusion. Existing approaches ei
arXiv:2602.00811v2 Announce Type: replace Abstract: Current Multimodal Affective Computing (MAC) systems heavily rely on the completeness of multiple modalities to accurately understand human's affect
arXiv:2512.14014v2 Announce Type: replace Abstract: World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world mo
Alteryx One Live Query and Google Cloud BigQuery redefine how enterprises handle complex, unstructured data at scale by reducing reliance on disconnected tools, minimizing data movement, and enabling
arXiv:2610.11228v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong potential for sequential recommendation through their ability to reason over complex m
arXiv:2610.11723v1 Announce Type: new Abstract: Multiplayer world models must ensure that independently controlled views remain consistent with one shared and persistent world. We introduce MultiWorld
It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu
arXiv:2610.11605v1 Announce Type: cross Abstract: We introduce NanoProof, to our knowledge the first factorized execution-guided theorem prover in Lean 4 whose training data, extraction tooling, train
arXiv:2610.11768v1 Announce Type: cross Abstract: Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about th
arXiv:2610.11631v1 Announce Type: cross Abstract: Deploying intelligent robotic systems that interact with humans through gestures requires neural networks capable of recognizing diverse temporal patt
arXiv:2610.11030v1 Announce Type: cross Abstract: Tool-using LLM agents violate the policies they are deployed to enforce, often silently. Prior defenses hand-write rules, query an LLM verifier per ac
arXiv:2610.10655v1 Announce Type: cross Abstract: Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning
arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalizat
arXiv:2610.12458v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that ex
arXiv:2610.11194v1 Announce Type: cross Abstract: Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models. As real-world data collection is expen
arXiv:2610.10697v1 Announce Type: cross Abstract: Accurately simulating chaotic quantum many-body dynamics remains a major computational challenge for classical methods, due to the rapid buildup and s
arXiv:2610.12448v1 Announce Type: new Abstract: In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inferenc
arXiv:2603.07980v2 Announce Type: replace-cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks