Knowledge catalogue
arXiv:2603.26556v3 Announce Type: replace-cross Abstract: Converting a pretrained Transformer into a more efficient hybrid model through distillation offers a promising approach to reducing inference
arXiv:2609.17562v1 Announce Type: cross Abstract: Hybrid Spiking Neural Network (SNN)-Artificial Neural Network (ANN) architectures combine the energy efficiency of SNNs with the superior detection ac
arXiv:2609.18323v1 Announce Type: new Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3
arXiv:2609.18928v1 Announce Type: cross Abstract: In particle collider experiments, event reconstruction is the task of inferring the kinematics of short-lived particles produced in the hard scatter f
arXiv:2609.17983v1 Announce Type: new Abstract: KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved know
arXiv:2609.18395v1 Announce Type: new Abstract: Policies for robotic manipulation are produced by training on large teleoperated datasets. These datasets typically consist of free-space trajectories,
arXiv:2609.18461v1 Announce Type: new Abstract: Personalized agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. Whil
arXiv:2609.17696v1 Announce Type: new Abstract: Document repositories evolve continuously. Guidelines and policies are revised, superseded, and re-uploaded, so the same content recurs in different wor
arXiv:2609.17888v1 Announce Type: cross Abstract: We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This pro
arXiv:2609.18861v1 Announce Type: new Abstract: Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-l
arXiv:2609.18125v1 Announce Type: cross Abstract: Humans often observe others before interacting and adjust their behavior accordingly. Robot navigation in crowds, however, often represents pedestrian
arXiv:2609.17728v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving
arXiv:2609.19070v1 Announce Type: new Abstract: This paper introduces ``question archaeology'', a specific evaluation task focused on inferring the single, authentic 'genesis question' that motivated
arXiv:2609.17639v1 Announce Type: cross Abstract: Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these sign
arXiv:2609.18108v1 Announce Type: new Abstract: One-step action generation can substantially reduce the inference cost of vision-language-action (VLA) policies, but its effect on closed-loop task succ
Operating large data pipelines requires an orchestration layer that scales smoothly as workloads expand. When your pipelines process millions of complex data points every day to feed predictive models
Anyone interested in trying out a new inference provider? We're testing Gemma 4:26b and Qwen 3.8:27B behind an OpenAI-compatible API and looking for a few users to give them a spin and tell us how it
arXiv:2609.17443v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong visual question answering (VQA) performance, but processing large cluttered images is computationally expen
arXiv:2609.16814v1 Announce Type: new Abstract: While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable st
arXiv:2609.16517v1 Announce Type: cross Abstract: Resume screeners must infer job-relevant competence from resumes whose presentation can vary substantially in wording, structure, stylistic polish, an
arXiv:2609.17420v1 Announce Type: cross Abstract: Audio-visual embodied navigation equips robots with the capability to infer the locations of sound sources by integrating visual inputs and acoustic i
arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impra
arXiv:2609.17210v1 Announce Type: cross Abstract: Vision-language-action (VLA) models, world-action models (WAMs), and offline reinforcement learning methods are rapidly expanding the design space of
arXiv:2609.16366v1 Announce Type: cross Abstract: When foundation models describe people, recent work in AI fairness, accessibility, and ethics recommends avoiding inferred identity labels (e.g., 'she
arXiv:2601.10511v2 Announce Type: replace-cross Abstract: Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network rel
arXiv:2609.17019v1 Announce Type: new Abstract: While Chain-of-Thought (CoT) reasoning has been proven to be effective, it often leads to overthinking, resulting in computational overhead, inference l
Follow up post of https://www.reddit.com/r/LocalLLaMA/s/nGMyKswrch. Thanks everyone who replied. I didn't change the specs. Might be loosing some of the memory bandwidth but will scale in future if I
arXiv:2404.03028v4 Announce Type: replace Abstract: Modern language models (LMs) can learn to perform new tasks in different ways: in instruction following, the target task is described explicitly in
Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first ty
arXiv:2609.14724v1 Announce Type: new Abstract: Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for
arXiv:2609.15726v1 Announce Type: cross Abstract: Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not conver
arXiv:2609.14279v1 Announce Type: cross Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geom
arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them
arXiv:2609.15671v1 Announce Type: cross Abstract: Visual Question Answering (VQA) with Vision-Language Models (VLMs) is increasingly used in privacy-sensitive and bandwidth-constrained settings. Feder
arXiv:2609.13680v1 Announce Type: new Abstract: Fine-tuning instruct models often improves target performance while inducing behavioral drift from the reference model, which can degrade existing capab
During an outage, every second matters. That is one of the reasons OpenAI is using @cerebras internally. @seanlie describes how fast inference is being used for incident response and critical research
arXiv:2609.14466v1 Announce Type: new Abstract: Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the obser
arXiv:2609.15759v1 Announce Type: new Abstract: Full-duplex speech-to-speech (S2S) models enable natural conversational AI by allowing simultaneous listening and speaking. However, these models typica
arXiv:2609.13295v1 Announce Type: cross Abstract: Diffusion learning leverages the statistical mechanism of diffusion processes for learning, reasoning, and inferring complex distributions from data.
arXiv:2609.14896v1 Announce Type: cross Abstract: A notable byproduct of LLM alignment training is mode collapse: the progressive loss of output diversity that narrows a model's expressivity at infere
arXiv:2609.13206v1 Announce Type: cross Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such
arXiv:2511.09665v1 Announce Type: cross Abstract: Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of (x,y) pairs as context and predic
arXiv:2609.13285v1 Announce Type: cross Abstract: The KV cache is a primary bottleneck for Transformer decoding: its memory footprint and cache-read traffic grow with sequence length. Grouped-query at
arXiv:2609.14711v1 Announce Type: new Abstract: Reward-based learning can alter the very posterior distribution that scientific inference aims to estimate. GRPO-QM sidesteps this by learning only an e
arXiv:2609.14783v1 Announce Type: cross Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer f
arXiv:2609.14268v1 Announce Type: new Abstract: In this work, we propose a decentralized communication-conditioned generative framework for multi-agent collision avoidance. Agents generate short-horiz
arXiv:2609.15851v1 Announce Type: new Abstract: Language models can learn from experience, but raw solution trajectories are often too long and noisy to provide effective guidance. In this work, we pr
arXiv:2609.14708v1 Announce Type: new Abstract: A core goal of efficient reasoning is to improve the accuracy-efficiency frontier. However, jointly improving reasoning accuracy and inference efficienc
arXiv:2609.13879v1 Announce Type: new Abstract: An acquired representation can enlarge a system's cognitive repertoire without transferring the capacities exercised in producing that representation. T
arXiv:2609.15743v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-o
arXiv:2609.13328v1 Announce Type: cross Abstract: Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging cond
arXiv:2504.08075v2 Announce Type: replace-cross Abstract: We develop a correspondence between the structure of Turing machines and the structure of singularities of real analytic functions, based on c
arXiv:2609.15296v1 Announce Type: new Abstract: Universal multimodal embedding (UME) learns unified representations across modalities, enabling a single model to support diverse retrieval tasks. Recen
The telecommunications industry is currently undergoing a paradigm shift, moving from traditional manual human-driven operations to fully Autonomous Network Operations. Modern networks have grown incr
arXiv:2609.15137v1 Announce Type: new Abstract: 3D Gaussian language fields provide an explicit, spatially grounded representation for 3D visual question answering (VQA), but their dense semantic feat
arXiv:2609.15160v1 Announce Type: new Abstract: Looped Transformers have recently demonstrated strong performance in both reasoning and language tasks by reusing a shared set of parameters across mult
arXiv:2606.12721v3 Announce Type: replace Abstract: Inferring another person's beliefs requires reconstructing their information access history: what they encountered, in what order, from whom, and wi
arXiv:2609.15528v1 Announce Type: new Abstract: Multilingual Large Language Models (LLMs) traditionally rely on a single vocabulary shared by all supported languages, which can lead to uneven compress
arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect patie
arXiv:2609.14146v1 Announce Type: cross Abstract: Vision-language-action (VLA) deployment can reduce inference latency while changing closed-loop task behavior. We evaluate HuggingFaceVLA/smolvla_libe