Knowledge catalogue
arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the
arXiv:2604.19769v1 Announce Type: cross Abstract: Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context leng
arXiv:2604.19233v1 Announce Type: new Abstract: Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with s
arXiv:2604.19509v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding human-object interactions, but their application to robotic sys
arXiv:2512.03467v2 Announce Type: replace Abstract: Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a
Excited to share our work on production-ready W4A8 inference, now integrated in vLLM! By combining 4-bit weights (low memory) with 8-bit activations (high compute), we hit the sweet spot for both deco
This article examines the fundamental trade-offs in large language model inference operations, specifically the competing priorities of maximizing throughput, minimizing latency, and reducing costs. I
arXiv:2507.14808v3 Announce Type: replace-cross Abstract: Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing
arXiv:2602.07794v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual r
arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i
arXiv:2603.04592v3 Announce Type: replace Abstract: Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in d
arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods
arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra
arXiv:2604.17175v1 Announce Type: new Abstract: We introduce RosettaSearch, an inference-time multi-objective optimization approach for protein sequence optimization. We use large language models (LLM
arXiv:2411.05472v2 Announce Type: replace Abstract: The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-en
arXiv:2507.02935v2 Announce Type: replace Abstract: Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction
arXiv:2511.14178v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies st
arXiv:2604.15009v1 Announce Type: cross Abstract: Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for ge
arXiv:2604.13251v1 Announce Type: new Abstract: Analog optical computers promise large efficiency gains for machine learning inference, yet no demonstration has moved beyond small-scale image benchmar
arXiv:2604.13253v1 Announce Type: new Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distributio
We're building AI Gateway into a unified inference layer for AI, letting developers call models from 14+ providers. New features include Workers AI binding integration and an expanded catalog with mul
arXiv:2506.09163v2 Announce Type: replace Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic process
We’re back 🔥. Thrilled to be named once again to the @Forbes AI 50. The AI Native Cloud, built for the full AI lifecycle: fast inference, open models, fine-tuning at scale. https://www.forbes.com/list
arXiv:2604.11948v1 Announce Type: new Abstract: Large Foundation Model (LFM) inference is both memory- and compute-intensive, traditionally relying on GPUs. However, the limited availability and high
Banger paper from NVIDIA. Agentic reasoning needs models that are not just capable, but efficient at long-context inference. The agent model layer is moving toward open, long-context, high-throughput
Compute constraints are a double bind: On the inference side you need to either (a) raise prices, (b) ration use, and/or (c) serve worse models. This hurts current growth On the training side, you can
arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transport
arXiv:2604.12806v1 Announce Type: new Abstract: Inferring latent interaction structures from observed dynamics is a fundamental inverse problem in many-body interacting systems. Most neural approaches
arXiv:2604.11943v1 Announce Type: cross Abstract: An OS kernel that runs LLM inference internally can read logit distributions before any text is generated -- and act on them as a governance primitive
The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, not by adding parameters. Bigger-model quality at smaller-mode
arXiv:2512.06443v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed on edge devices. To meet strict resource constraints, real-world deployment has pushed
arXiv:2511.03015v2 Announce Type: replace Abstract: Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their dis
GMI Cloud and @Zai_org are bringing fast inference + frontier models around the globe. First stop: Singapore. Big congrats to all the builders at the GMI x @Zai_org Agent Hackathon in 🇸🇬 100+ on-site,
This Reddit thread discusses a user's experience attempting LoRA fine-tuning of the Gemma 4 26B-A4B model on a 16GB VRAM GPU, where training loss decreases normally but the resulting model degenerates
arXiv:2512.23532v2 Announce Type: replace Abstract: Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency
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
arXiv:2604.10908v1 Announce Type: new Abstract: Every existing knowledge system separates storage from computation. We show this separation is unnecessary and eliminate it. In a standard triple is_a(A
We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than payroll) — cutting it by 2-5x would be transformative. Last year, OSS models
arXiv:2509.16606v5 Announce Type: replace-cross Abstract: In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained comm
arXiv:2604.08584v1 Announce Type: cross Abstract: Long-context LLMs increasingly rely on extended, reusable prefill prompts for agents and domain Q&A, pushing attention and KV-cache to become the domi
arXiv:2604.08585v1 Announce Type: cross Abstract: Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computati
And if you are interested in the answer, Claude and ChatGPT 5.4 Pro think running OpenClaw with local inference on your Mac uses more total power. Gemini doesn't think so (but appears to have not thou
arXiv:2604.07394v1 Announce Type: cross Abstract: The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. W
arXiv:2604.05650v2 Announce Type: replace Abstract: Video Large Language Models (Video-LLMs) excel in video understanding but suffer from high inference latency during autoregressive generation. Specu
Fardis Makraduli ([@f_makraduli](https://x.com/f_makraduli)) shared a post about attending the AI Engineer ([@aiDotEngineer](https://x.com/aiDotEngineer)) event, noting shared themes and networking...
The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to @lqiao, 90% of the world's data is still private and locked inside ap
arXiv:2606.12674v2 Announce Type: replace Abstract: Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to
arXiv:2610.21833v1 Announce Type: new Abstract: Grid foundation models (GridFMs) are graph transformers pretrained on a masked power-flow objective, and evaluate power flow orders of magnitude faster
Title: I'm building a small local AI server to validate a project I've developed. The application currently runs Qwen 9B and the server will only be used for an initial pilot with a few users. I have
arXiv:2610.22120v1 Announce Type: new Abstract: Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness
arXiv:2610.19656v1 Announce Type: new Abstract: Action-chunking vision-language-action (VLA) models predict sequences of future robot actions. Under fixed-horizon execution, the robot executes a fixed
arXiv:2604.03925v2 Announce Type: replace Abstract: Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manne
arXiv:2610.19817v1 Announce Type: new Abstract: Human values shape how we expect language models to behave. Dominant alignment approaches treat values as global objectives, although the appropriate va
arXiv:2610.19878v1 Announce Type: new Abstract: Population dynamics are often observed only as unpaired snapshots, which leave the evolution between measurements underdetermined. Second-order regulari
arXiv:2610.14688v1 Announce Type: new Abstract: LLM-augmented data workflows often involve multiple heterogeneous models operating over substantial shared context, such as schemas, tool specifications
arXiv:2610.15287v1 Announce Type: new Abstract: Category-level 6D pose and shape estimation requires recovering an object's pose and geometry from partial observations without an exact instance model.
arXiv:2610.18656v1 Announce Type: new Abstract: Language model systems repeatedly make test-time decisions by comparing similarity: they retrieve exemplars, rank candidates, and revise responses. Yet
arXiv:2610.17332v1 Announce Type: new Abstract: Vision large language models (VLLMs) are deployed in privacy-sensitive domains, raising training-data exposure concerns. Existing gray-box membership in
arXiv:2610.14277v1 Announce Type: cross Abstract: Tensor parallelism ends every layer with an all-reduce that sits between two dependent matrix multiplications, so its cost cannot be hidden and grows
arXiv:2610.15274v1 Announce Type: new Abstract: Training humanoid robots to learn human behaviors requires expensive data collection, and human-robot interaction data can be even more difficult and co