Knowledge catalogue
arXiv:2511.11232v2 Announce Type: replace Abstract: Constructing a unified 3D scene understanding model has long been hindered by the significant topological discrepancies across different sensor moda
arXiv:2603.06003v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (SMoE) language models achieve strong capability at low per-token compute, yet deployment remains constrained by memory fo
arXiv:2604.09552v1 Announce Type: cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retri
arXiv:2604.11645v1 Announce Type: cross Abstract: Frequency-selective wireless power transfer provides a feasible route to enable independent actuation and control of multiple untethered robots in a c
arXiv:2602.19509v3 Announce Type: replace-cross Abstract: We observe that LLM cascading and routing implicitly solves an anytime computation problem -- a class of algorithms, well-studied in classical
Manifest is a tool or platform that has added support for Ollama Cloud, enabling users to route AI model inference to cloud-hosted Ollama instances rather than relying solely on local GPU resources. T
arXiv:2604.09562v1 Announce Type: cross Abstract: Efficient LLM serving must balance throughput and latency across diverse, bursty workloads. We introduce StreamServe, a disaggregated prefill decode s
arXiv:2604.08565v1 Announce Type: cross Abstract: At typical context lengths, the feed-forward MLP block accounts for a large share of a transformer's compute budget, motivating sparse alternatives to
arXiv:2604.08800v1 Announce Type: cross Abstract: Stepping-stone intrusions (SSIs) are a prevalent network evasion technique in which attackers route sessions through chains of compromised intermediat
Jeff Tomich / Politico: Voters in Festus, Missouri, ousted all four incumbent council members running for reelection last week, days after the council's approval of a $6B data center — The rout of hal
A developer shared on r/ollama how they built a custom VS Code extension that routes Claude API requests through locally-run models via Ollama, dramatically reducing cloud API costs to approximately $
MiniMax-M2 is a large-scale mixture-of-experts (MoE) language model available for inference on Together AI's platform, accessible via both serverless and dedicated infrastructure options. The model ca
A Reddit post from r/ollama describing how a user dramatically reduced their Claude Code token consumption by 178x, likely by routing simpler or lower-stakes tasks to a locally-run model via Ollama in
Cloudflare's global network has officially crossed 500 Tbps of external capacity — enough to route more than 20% of the web and absorb the largest DDoS attacks ever recorded. This milestone, reache...
arXiv:2604.08133v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models due to their sparse activation mechanism. However, the s
arXiv:2604.06515v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input. While
arXiv:2604.08535v1 Announce Type: cross Abstract: Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe an
arXiv:2604.06715v1 Announce Type: cross Abstract: Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scen
arXiv:2604.06956v1 Announce Type: cross Abstract: Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from
arXiv:2604.07098v1 Announce Type: new Abstract: Large language models often fail on tasks they seem to already understand. In our experiments, this appears to be less about missing knowledge and more
Building and serving models on infrastructure is a strong use case for businesses. In Google Cloud, you have the ability to design your AI infrastructure to suit your workloads. Recently, I experiment
model : add Kolibri-1 (Aleph-Alpha) support (#30673) model : add Kolibri-1 (Aleph-Alpha) support Kolibri-1 is a bilingual German/English MoE reasoning model with 78B parameters and 3.46B active per to
arXiv:2610.21243v1 Announce Type: cross Abstract: Centralized orchestration provides comprehensive multi-agent coordination but creates bottlenecks in scalability, adaptability, and collective perform
arXiv:2610.22065v1 Announce Type: new Abstract: Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy o
tests : catch vanished rows in the fusion baseline (#30618) tests : update the Metal fusion baseline Re-record the MTL fusion baseline with the current build: 276 rows over 131 dummy models. Assisted-
arXiv:2610.22091v1 Announce Type: new Abstract: Whole-slide images may contain coexisting lesions, yet weak supervision typically provides only the most severe diagnosis. We formulate this setting as
arXiv:2610.21565v1 Announce Type: cross Abstract: On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models t
arXiv:2610.21918v1 Announce Type: cross Abstract: Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods
arXiv:2610.20668v1 Announce Type: cross Abstract: We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read se
arXiv:2610.21234v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently been introduced as a promising paradigm for continual learning, where a student policy can be supervised at th
arXiv:2610.21195v1 Announce Type: new Abstract: On-policy distillation (OPD) improves a student language model by training on its own generated trajectories with supervision from a stronger teacher. H
arXiv:2610.22127v1 Announce Type: cross Abstract: Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, wh
The real challenge of building an agentic system isn't the construction, it's the measurement. Evaluation engineering is a critical foundation for building reliable AI systems. Setting up systematic,
arXiv:2610.22014v1 Announce Type: cross Abstract: Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health. However, irregularity,
arXiv:2610.21001v1 Announce Type: new Abstract: Column generation folklore warns that initial columns hurt: a warm start may bias the dual and slow the root of branch-price-and-cut. We show the opposi
arXiv:2610.21411v1 Announce Type: new Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require
arXiv:2603.13467v2 Announce Type: replace-cross Abstract: Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialize
arXiv:2610.22086v1 Announce Type: cross Abstract: Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprie
arXiv:2610.22236v1 Announce Type: cross Abstract: On-policy distillation consolidates specialist models into one student at the price of rollouts, teacher passes and gradient steps for every teacher.
arXiv:2607.14138v2 Announce Type: replace-cross Abstract: The most prevalent distributed systems today include people and their personal machines (smartphones), and computations in them are driven by
arXiv:2610.19624v1 Announce Type: new Abstract: Visual recognition models usually process a fixed view under a fixed computation budget, although images often benefit from different scales and amounts
arXiv:2610.15788v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed as teams, yet they fail in characteristically social ways: information holders stay silent,
arXiv:2610.18007v1 Announce Type: cross Abstract: LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf
arXiv:2610.17816v1 Announce Type: new Abstract: Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-
arXiv:2610.19855v1 Announce Type: new Abstract: As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) mo
arXiv:2610.19683v1 Announce Type: cross Abstract: World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, comp
arXiv:2610.14576v1 Announce Type: new Abstract: As frontier LLMs gain more capabilities, the cost to run them also goes up. At the same time, small language models (SLMs) also gain more capabilities b
arXiv:2602.02326v3 Announce Type: replace Abstract: If multilingual language model representations contain separable language-associated structure, then shifting between languages may require only sim
arXiv:2610.14552v1 Announce Type: cross Abstract: Protocol specifications rarely justify executable behavior in one place: a transition may inherit its source state from a remote control block, name i
arXiv:2610.20582v1 Announce Type: new Abstract: Long contexts enlarge key--value (KV) caches, increasing memory use and attention costs during generation. Compressing these caches must preserve inform
arXiv:2610.18014v1 Announce Type: cross Abstract: Non-prefix KV cache reuse is a promising approach for improving inference efficiency in multi-agent systems as agents share the same context blocks. H
arXiv:2610.20241v1 Announce Type: new Abstract: Offline navigation demonstrations are multimodal, because one observation often allows several valid actions, such as passing an obstacle on either side
arXiv:2609.32717v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional s
arXiv:2610.19623v1 Announce Type: new Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layered
arXiv:2610.16601v1 Announce Type: cross Abstract: Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing m
arXiv:2610.19712v1 Announce Type: new Abstract: Multi-session agents can switch between LLMs to balance performance and inference cost. Such switches require transferring a history for continuity, yet
arXiv:2610.20009v1 Announce Type: new Abstract: Scaling self-supervised contrastive reinforcement learning (CRL) to 1000-layer Euclidean neural networks has enabled state-of-the-art goal-reaching perf
arXiv:2606.16569v2 Announce Type: replace Abstract: Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the re
arXiv:2610.18143v1 Announce Type: new Abstract: Prefix reuse improves LLM inference by sharing the KV cache of common prompt prefixes. This helps only if the scheduler can execute a matched branch in
arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and