Knowledge catalogue
arXiv:2603.05917v3 Announce Type: replace-cross Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market e
arXiv:2605.16138v1 Announce Type: cross Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on
There are a lot of coding and reasoning benchmarks for AI agents, but not a lot for document understanding - which is a prerequisite for all downstream knowledge work. We released ParseBench ~a month
arXiv:2412.06853v4 Announce Type: replace-cross Abstract: This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regr
arXiv:2605.15894v1 Announce Type: cross Abstract: Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human healt
arXiv:2605.15305v1 Announce Type: cross Abstract: A unified simulator that can model diverse physical phenomena without solver-specific redesign is a long-standing goal across simulation science. We p
arXiv:2605.16127v1 Announce Type: new Abstract: While multi-modal 3D semantic occupancy prediction typically enhances robustness by fusing camera and LiDAR inputs, its effectiveness is fundamentally c
We've gotten really really good at RL. Composer 2.5 is fighting well-above its weight class. Very excited for the next release as we scale model sizes and FLOPs with @SpaceXAI! Introducing Composer 2.
arXiv:2602.04003v3 Announce Type: replace Abstract: Most adversarial threats in artificial intelligence (AI) target the computational behavior of models rather than the humans who rely on them. Yet mo
arXiv:2605.16046v1 Announce Type: cross Abstract: Semantic code search has been widely adopted in both academia and industry. These approaches embed natural-language queries and code snippets into a s
Andon Labs conducted an experiment in which four major AI models (ChatGPT, Gemini, Claude, and Grok) were tasked with operating 24/7 radio stations continuously for five months. Each AI was required t
arXiv:2605.15010v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading representation for real-time novel view synthesis and been widely adopted in various downstream ap
40 Grok Build agents tearing through C code in parallel. All supervised by DAD. DAD is a lightweight autonomous tmux supervisor for long-running Grok tasks. Built entirely in Grok Build. /dad 'your ob
arXiv:2605.14427v1 Announce Type: new Abstract: In hybrid automatic speech recognition (ASR) systems, the vocabulary size is unambiguous, typically determined by the number of phones, bi-phones, or tr
A milestone for Pearl Research Labs: our first major enterprise partnership is live with Together AI. @togethercompute’s inference platform is an ideal demonstration of @prlnet's value proposition — O
arXiv:2602.24273v3 Announce Type: replace Abstract: We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design impleme
arXiv:2605.14581v1 Announce Type: cross Abstract: Visual RAG has offered an alternative to traditional RAG. It treats documents as images and uses vision encoders to obtain vision patch tokens. Howeve
arXiv:2605.14679v1 Announce Type: cross Abstract: Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by
arXiv:2605.14341v1 Announce Type: new Abstract: Existing diffusion models have made significant progress in generating realistic images. However, their direct adaptation to remote sensing imagery ofte
arXiv:2605.14271v1 Announce Type: new Abstract: LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. Howev
arXiv:2605.14438v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standa
arXiv:2605.14886v1 Announce Type: new Abstract: Electrocardiogram (ECG) monitoring in Internet of Medical Things (IoMT) networks is constrained by strict data-sharing regulations and privacy concerns.
arXiv:2602.20571v2 Announce Type: replace Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effe
arXiv:2511.15408v2 Announce Type: replace-cross Abstract: Chinese demonstrates high semantic compactness and rich metaphorical expressiveness, enabling limited text to convey dense meanings while incr
arXiv:2602.03814v2 Announce Type: replace Abstract: Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases, motivating ada
arXiv:2605.14274v1 Announce Type: new Abstract: Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical
arXiv:2605.14759v1 Announce Type: new Abstract: De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative model
arXiv:2602.17949v2 Announce Type: replace-cross Abstract: Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifier
arXiv:2605.14382v1 Announce Type: new Abstract: Interactive real-time autoregressive video generation is essential for applications such as content creation and world modeling, where visual content mu
arXiv:2605.14880v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable success in high-fidelity Novel View Synthesis (NVS), yet the optimization proce
arXiv:2605.14953v1 Announce Type: new Abstract: We address the problem of conformal selection, where an agent must select a minimal subset of options to ensure that at least one ``success'' is identif
arXiv:2602.02427v2 Announce Type: replace Abstract: Large language Models (LLMs) have achieved significant breakthroughs across diverse domains; however, they can still produce unreliable or misleadin
arXiv:2605.14249v1 Announce Type: new Abstract: We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducin
arXiv:2605.14525v1 Announce Type: new Abstract: In multi-view 3D human pose estimation, models typically rely on images captured simultaneously from different camera views to predict a pose at a speci
arXiv:2605.14912v1 Announce Type: new Abstract: Pluralistic alignment is typically operationalised as preference aggregation: producing responses that span (Overton), steer toward (Steerable), or prop
The Gemini Live Agent Challenge is officially in the books! We challenged developers worldwide to break out of the traditional 'text box' paradigm by building next-generation AI agents. From our initi
arXiv:2605.14703v1 Announce Type: new Abstract: The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persist
arXiv:2605.14475v1 Announce Type: new Abstract: Interpreting ultra-high-resolution (UHR) remote sensing images requires models to search for sparse and tiny visual evidence across large-scale scenes.
arXiv:2605.14309v1 Announce Type: cross Abstract: Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove tar
arXiv:2605.14352v1 Announce Type: new Abstract: Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging f
arXiv:2605.14840v1 Announce Type: new Abstract: We study inventory control with decision-dependent censoring, focusing on the censored or repeated newsvendor (R-NV), where each order quantity determin
arXiv:2605.13871v1 Announce Type: cross Abstract: This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of trad
Inference is becoming the largest compute market and energy consumer in AI. Pearl turns inference CapEx of hyperscalers into a profit center: every LLM token produced by GPUs can simultaneously genera
Introducing Gemma-4-31B-it-Pearl on Together AI, Pearl Research Labs’ instruction-tuned checkpoint of Gemma 4 31B powered by @prlnet Proof of Useful Work protocol. AI natives can now use this Pearl mo
arXiv:2605.14365v1 Announce Type: cross Abstract: Recent tabular learning benchmarks increasingly show a tight performance cluster rather than a clear hierarchy among leading methods, spanning gradien
arXiv:2605.14874v1 Announce Type: new Abstract: Virtual Try-On (VTON) aims to synthesize photorealistic images of garments precisely aligned with a person's body and pose. Current diffusion-based meth
arXiv:2605.15172v1 Announce Type: cross Abstract: Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safe
arXiv:2605.14568v1 Announce Type: cross Abstract: Context. Behaviour-Driven Development (BDD) software test suites accumulate duplicated step subsequences. Three published refactoring patterns are ava
arXiv:2510.23477v2 Announce Type: replace Abstract: Effective math tutoring requires not only solving problems but also diagnosing students' difficulties and guiding them step by step. While multimoda
arXiv:2605.14941v1 Announce Type: cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio which makes extraction of meaningful
arXiv:2512.07461v3 Announce Type: replace Abstract: We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reas
arXiv:2605.15177v1 Announce Type: new Abstract: Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. S
arXiv:2511.16964v2 Announce Type: replace-cross Abstract: Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writi
Lighthouse Attention is a novel attention mechanism that improves efficiency in transformer models by selectively focusing computation on the most relevant tokens, similar to how a lighthouse beam ill
arXiv:2605.14978v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by having a lightweight draft model propose speculative windows of candidate tokens for parallel verifica
arXiv:2603.04885v2 Announce Type: replace Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. H
arXiv:2605.14045v1 Announce Type: new Abstract: Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training of
arXiv:2603.12554v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion
arXiv:2605.14558v1 Announce Type: cross Abstract: Agentic reinforcement learning trains large language models using multi-turn trajectories that interleave long reasoning traces with short environment
arXiv:2605.14894v1 Announce Type: new Abstract: Recent breakthroughs in video diffusion models have significantly accelerated the development of video editing techniques. However, existing methods oft