Knowledge catalogue
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintai
arXiv:2610.07561v1 Announce Type: new Abstract: Image-guided surgery (IGS) depends on accurate tracking of surgical instruments to provide real-time navigation relative to anatomical structures. Comme
arXiv:2610.08559v1 Announce Type: cross Abstract: Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports t
arXiv:2610.07758v1 Announce Type: cross Abstract: Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at
arXiv:2610.08765v1 Announce Type: new Abstract: Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificat
arXiv:2610.08246v1 Announce Type: new Abstract: Frontier large language models (LLMs) can generate heuristic functions that guide search to achieve state-of-the-art performance in satisficing planning
arXiv:2610.07015v1 Announce Type: new Abstract: Privileged 3D supervision uses additional geometric information during training to guide RGB-based visuomotor policy learning, without requiring geometr
Google AI has published a new blog post titled “Learn more about identifying AI‑generated content,” announcing tools that make it easier to spot machine‑created text on a global scale. The article – r
arXiv:2610.08215v1 Announce Type: new Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for
arXiv:2610.07419v1 Announce Type: cross Abstract: Implicit neural representations (INRs) are shaped by the spectral structure induced by their input encodings and activation functions. Existing method
arXiv:2610.07884v1 Announce Type: cross Abstract: Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are us
arXiv:2610.07562v1 Announce Type: cross Abstract: Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space explorat
arXiv:2610.07973v1 Announce Type: new Abstract: We study the problem of recovering the ranking of a fixed set of items according to their unknown numerical utilities. At each interaction with the envi
arXiv:2610.08020v1 Announce Type: cross Abstract: Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initi
arXiv:2610.07074v1 Announce Type: cross Abstract: Estimating a decision threshold requires locating observations near an unknown boundary. We study how gradient-based pretraining learns this statistic
arXiv:2610.07196v1 Announce Type: cross Abstract: Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery
arXiv:2610.07638v1 Announce Type: new Abstract: As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules
arXiv:2602.10013v2 Announce Type: replace Abstract: Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task
arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E
arXiv:2509.26513v2 Announce Type: replace Abstract: Generating large and diverse obstacle datasets to learn motion planning in environments with dynamic obstacles is challenging due to the vast space
arXiv:2607.09042v2 Announce Type: replace Abstract: Reinforcement learning is increasingly used to fine-tune vision-language-action (VLA) models, but robot interaction is expensive and learning become
arXiv:2610.07979v1 Announce Type: new Abstract: As agents continuously improve by generating and revising Skills, the process that discovers and refines those Skills becomes a learnable object in its
arXiv:2610.06918v1 Announce Type: new Abstract: We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central serv
arXiv:2610.07613v1 Announce Type: cross Abstract: In mill yards, log loaders clear dense piles by a sequence of bundle grasps: hundreds of logs rest in contact, and each removal changes the pile avail
arXiv:2610.08033v1 Announce Type: new Abstract: World models are usually judged from the inside: by prediction loss, by the return a policy earns in imagination, or by how convincing their frames look
arXiv:2610.06958v1 Announce Type: new Abstract: Object-goal navigation (ObjectNav) in multi-floor scenarios presents a challenge due to sparse rewards caused by long-horizon decision-making. In this p
arXiv:2610.08475v1 Announce Type: new Abstract: In many-query scenarios, data-driven surrogate models provide an efficient alternative to high-fidelity solvers for simulating physical systems governed
arXiv:2610.07184v1 Announce Type: cross Abstract: A key challenge in building AI systems for scientific research is enabling extit{scientific exploration}: the systematic process of investigating unkn
arXiv:2610.07008v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typi
arXiv:2610.06950v1 Announce Type: cross Abstract: Injecting skills into a frozen language model currently costs a million parameters and a reinforcement-learning pipeline. We introduce method{}, a Sys
arXiv:2610.07627v1 Announce Type: new Abstract: Scientific discovery systems typically optimize experiments within a fixed hypothesis space. This creates a failure mode when all available candidates o
arXiv:2610.06942v1 Announce Type: new Abstract: Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time series
arXiv:2610.07731v1 Announce Type: cross Abstract: Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval m
arXiv:2610.07062v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses
arXiv:2610.07247v1 Announce Type: new Abstract: Large language models have shown strong reasoning capabilities, but their high inference costs make knowledge distillation an important approach for tra
arXiv:2610.06883v1 Announce Type: cross Abstract: Neural operators provide fast surrogates for time-dependent PDEs, but autoregressive deployment creates a refinement-allocation problem: prediction er
arXiv:2610.07319v1 Announce Type: new Abstract: Compliant mechanisms provide compact and intrinsic structural compliance for regulating physical interactions between mechanisms and environments. Howev
arXiv:2610.08570v1 Announce Type: cross Abstract: Handcrafted preprocessing is widely employed in automated dermoscopic analysis to suppress imaging artifacts and enhance lesion visibility. Neverthele
Maxwell Zeff / Wall Street Journal: Letter: three fired OpenAI researchers urge AI labs to halt work that could impair AI monitoring and say their firings are “chilling those who remain at OpenAI” — I
arXiv:2610.07937v1 Announce Type: new Abstract: Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context Pr
arXiv:2610.08176v1 Announce Type: new Abstract: Decentralized learning is highly sensitive to communication topology under non-IID data. Adaptive peer-selection methods can exploit local model informa
arXiv:2610.08620v1 Announce Type: new Abstract: High-beam-count LiDAR sensors are costly, yet many perception pipelines require dense angular sampling. Using a pretrained Stable Diffusion model as the
arXiv:2610.07111v1 Announce Type: cross Abstract: UAVs that act as relays or base stations need accurate air-to-ground path-loss predictions for rate adaptation and placement, but propagation conditio
arXiv:2610.07258v1 Announce Type: cross Abstract: Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester
arXiv:2610.08745v1 Announce Type: new Abstract: We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set. In th
arXiv:2610.07607v1 Announce Type: cross Abstract: Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide r
llama.cpp can distribute inference on heterogeneous devices through the ggml RPC backend It's an advanced setting but I think with time we'll make it more accessible to regular users. It's crazy that
arXiv:2610.08704v1 Announce Type: cross Abstract: Image stylization with latent-diffusion models entangles two independently refined axes: what a region depicts and how it is depicted. Such pipelines
Running a model on your own machine used to be a privacy story with a quality tax. On this generation that trade has narrowed to where we can state it plainly: for daily work, the local model is good
arXiv:2610.07326v1 Announce Type: new Abstract: Universal object localization in X-ray security inspection is critical for automated threat detection in safety-critical venues. However, unlike everyda
arXiv:2610.07283v1 Announce Type: new Abstract: Analog hardware platforms offer the potential to reduce energy consumption over digital architectures, but in order to succeed, large-scale analog syste
arXiv:2610.07338v1 Announce Type: cross Abstract: Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies. To close this gap, we intro
arXiv:2610.07580v1 Announce Type: new Abstract: Aerospace electrical-design revisions can contain multiple genuine changes, although an engineering request may authorize only a subset. Propagating eve
Thursday, October 8 — 15:00–19:00 UTC Access is free and the API is OpenAI-compatible. During those 4 hours, everyone will be asked to use it actively at the same time—coding, chatting, agents, long c
I’m hitting rate limits on Together AI. For context, I’ve been working on an agentic repository indexing and benchmark generation tool, and I’m running multiple agents in parallel across models like L
Hi everyone, I’m exploring AI filmmaking and looking for good AI video-generation models that are either free, open-source/open-weight, or affordable. I’m particularly interested in models who allow u
Looks like you copied Chandra OCR 2 into your repo, renamed it, changed the license, and made the output worse (your olmocr score is 83.8 vs our 85.8). Introducing interfaze-1-lite, the first general
arXiv:2610.07853v1 Announce Type: cross Abstract: Ternary language models such as BitNet b1.58, Falcon-E and BitCPM are fine-tuned with higher-precision latent weights and deployed as ternary codes pr
arXiv:2610.07647v1 Announce Type: cross Abstract: Speech becomes less intelligible in noisy environments, and humans naturally adapt their voice to compensate. Inspired by this behavior, we investigat
arXiv:2610.06930v1 Announce Type: cross Abstract: Multivariate functional data arise in many modern manufacturing systems, where multiple sensors record densely sampled process trajectories. Monitorin