Knowledge catalogue
arXiv:2604.18548v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-info
arXiv:2604.16895v1 Announce Type: new Abstract: We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder locali
Physics paper in, manga out We are happy to share early results from Logos, our novel first-principles augmented intelligence system, that has enabled insightful results across domains. We start with
arXiv:2505.20211v3 Announce Type: replace Abstract: Fine-tuning large foundation models is essential for building expert models tailored to specialized tasks and domains, but fully updating billions o
arXiv:2509.18169v3 Announce Type: replace-cross Abstract: Tasks on complex systems require high-precision numerical computation to support decisions, but current large language models (LLMs) cannot in
arXiv:2604.17856v1 Announce Type: new Abstract: Plankton monitoring is essential for assessing aquatic ecosystems but is limited by the labor-intensive nature of manual microscopic analysis. Automatin
arXiv:2604.17212v1 Announce Type: new Abstract: This paper presents a framework for safe navigation of a unicycle point robot to a goal position in an environment populated with obstacles from almost
arXiv:2602.06663v2 Announce Type: replace Abstract: Unified multimodal models (UMMs) have shown impressive capabilities in generating natural images and supporting multimodal reasoning. However, their
arXiv:2411.04832v3 Announce Type: replace-cross Abstract: Plasticity refers to a network's ability to adapt to changing data distributions, which is crucial for the successful training of deep reinfor
arXiv:2604.04825v2 Announce Type: replace Abstract: Large language models achieve strong performance on many language tasks, yet it remains unclear whether they integrate world knowledge with syntacti
Please don’t trust your chatbot for medical advice. 🙏 Remember how I used to say that large language models are “frequently wrong, never in doubt”, and how I warned three years ago on 60 Minutes that
arXiv:2604.17132v1 Announce Type: new Abstract: Safety-aligned large language models (LLMs) often generate refusal responses to harmless queries due to the over-refusal problem. However, existing meth
Gary Marcus shared an observation or commentary about poetry encountered on a platform or service referenced as 'B.sky' (likely Bluesky, the decentralized social network). The post appears to document
arXiv:2604.16540v1 Announce Type: new Abstract: Poisoning input views of 3D reconstruction systems has been recently studied. However, we identify that existing studies simply backpropagate adversaria
arXiv:2604.16583v1 Announce Type: new Abstract: Edge deployment of large language models (LLMs) increasingly relies on libraries of lightweight LoRA adapters, yet GPU/DRAM can keep only a small reside
arXiv:2505.15342v2 Announce Type: replace-cross Abstract: We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model wi
arXiv:2604.17543v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challeng
arXiv:2604.17837v1 Announce Type: cross Abstract: An LLM's residual stream is both state and instruction: it encodes the current context and determines the next transformation. We introduce a paramete
arXiv:2502.02871v2 Announce Type: replace Abstract: Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, i
arXiv:2604.16480v1 Announce Type: new Abstract: This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning. Th
arXiv:2604.16519v1 Announce Type: new Abstract: In the field of online reinforcement learning (RL), traditional Gaussian policies and flow-based methods are often constrained by their unimodal express
arXiv:2511.23170v5 Announce Type: replace Abstract: Contrastive vision-language pre-training frameworks such as CLIP have demonstrated impressive zero-shot performance across a range of vision-languag
arXiv:2604.17163v1 Announce Type: new Abstract: We propose PPEDCRF, a calibrated selective perturbation framework that protects background-based location privacy in released video frames against galle
arXiv:2604.17338v1 Announce Type: cross Abstract: Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but o
arXiv:2604.16505v1 Announce Type: new Abstract: The selection of the optimal embryo for transfer is a critical yet challenging step in in vitro fertilization (IVF), primarily due to its reliance on th
arXiv:2604.18085v1 Announce Type: new Abstract: Matrix-level low-rank compression is a promising way to reduce the cost of large language models, but running compression and evaluating the resulting m
arXiv:2604.18316v1 Announce Type: cross Abstract: The most common cause of dementia is Alzheimer disease, a progressive neurodegenerative disorder affecting older adults that gradually impairs memory,
arXiv:2508.20751v2 Announce Type: replace Abstract: Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generati
arXiv:2506.13674v3 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tun
arXiv:2511.07329v3 Announce Type: replace-cross Abstract: It introduces FractalNet, a fractal-inspired computational architectures for advanced large language model analysis that mainly challenges mod
arXiv:2604.16334v1 Announce Type: new Abstract: The use of Deep Neural Network based systems in the real world is growing. They have achieved state-of-the-art performance on many image, speech and tex
arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k
arXiv:2604.17670v1 Announce Type: new Abstract: We introduce Prior-Fitted Functional Flows, a generative foundation model for pharmacokinetics that enables zero-shot population synthesis and individua
arXiv:2604.16909v1 Announce Type: new Abstract: As large language models (LLMs) evolve from conversational assistants into agents capable of handling complex tasks, they are increasingly deployed in h
arXiv:2604.18354v1 Announce Type: new Abstract: Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog
arXiv:2601.15220v2 Announce Type: replace Abstract: We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle
arXiv:2604.16523v1 Announce Type: new Abstract: This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed met
arXiv:2510.16054v2 Announce Type: replace-cross Abstract: When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send th
arXiv:2510.18109v4 Announce Type: replace-cross Abstract: Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model bu
arXiv:2604.17476v1 Announce Type: cross Abstract: Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive comp
arXiv:2505.14412v2 Announce Type: replace-cross Abstract: Effective prompt engineering remains a central challenge in fully harnessing the capabilities of LLMs. While well-designed prompts can dramati
arXiv:2604.17290v1 Announce Type: new Abstract: LLMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to rea
This post by Harrison Chase (creator of LangChain) likely discusses the concept of statements or propositions that are 'probably true' and advocates for keeping such discussions or determinations open
arXiv:2604.01348v2 Announce Type: replace Abstract: Test-time scaling has emerged as an effective way to improve language models on challenging reasoning tasks. However, most existing methods treat ea
arXiv:2604.17957v1 Announce Type: new Abstract: Process Reward Models (PRMs) have emerged as a powerful tool for providing step-level feedback when evaluating the reasoning of Large Language Models (L
arXiv:2509.26278v4 Announce Type: replace-cross Abstract: Most existing approaches formulate action quality assessment and skill proficiency estimation as discriminative prediction tasks, typically pr
arXiv:2604.17715v1 Announce Type: cross Abstract: Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still l
arXiv:2604.18459v1 Announce Type: new Abstract: Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability th
arXiv:2508.10531v3 Announce Type: replace Abstract: Modifications to test-time sampling have emerged as an important extension to diffusion algorithms, with the goal of biasing the generative process
arXiv:2604.17126v1 Announce Type: new Abstract: Vision-language models enable open-vocabulary object grounding through natural language queries, under the implicit assumption that semantically equival
arXiv:2604.17920v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level
As enterprises race to adopt AI without sacrificing control or flexibility, open-source infrastructure is the stable foundation that enables organizations to modernize while maintaining full digital s
As hybrid and multicloud strategies evolve, a new reality is coming into focus — one where open architectures are increasingly central to operational resilience, sovereignty and choice. The industry’s
arXiv:2604.18444v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) have shown promise for chest radiograph classification, but their performance is often limited by confounding
arXiv:2406.08334v2 Announce Type: replace-cross Abstract: Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constra
arXiv:2604.16889v1 Announce Type: new Abstract: Existing feature-interpretation pipelines typically operate on uniformly sampled units, but only a small fraction of cross-layer transcoder (CLT) featur
arXiv:2510.08047v2 Announce Type: replace-cross Abstract: Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although p
arXiv:2603.04531v2 Announce Type: replace Abstract: Tactile dexterous manipulation is essential to automating complex household tasks, yet learning effective control policies remains a challenge. Whil
Replit announced a feature enabling users to publish applications with a single click, streamlining the deployment process from development to production. This capability reduces friction in the app p
arXiv:2508.02750v2 Announce Type: replace Abstract: This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly