Knowledge catalogue
arXiv:2605.01935v1 Announce Type: cross Abstract: Vision Mamba (ViM) models offer a compelling efficiency advantage over Transformers by leveraging the linear complexity of State Space Models (SSMs),
This post likely discusses Harrison Chase's preferred language model or AI system for handling coding tasks, potentially comparing different models' capabilities for programming work. As the creator o
arXiv:2605.00020v1 Announce Type: new Abstract: The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer desig
arXiv:2605.00369v1 Announce Type: new Abstract: We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent adva
arXiv:2605.00291v1 Announce Type: new Abstract: The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the
This post discusses Ollama's functionality for resuming work with Anthropic's Claude models, indicating that users can safely restore previous sessions or states using a command-line interface (`ollam
arXiv:2602.04212v2 Announce Type: replace Abstract: Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier
arXiv:2505.13007v2 Announce Type: replace Abstract: The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying q
arXiv:2605.00086v1 Announce Type: new Abstract: High-quality corpora are essential for advancing Natural Language Processing (NLP) in Portuguese. Building on previous encoder-only models such as BERTi
arXiv:2511.05582v2 Announce Type: replace Abstract: Real-Time Auction (RTA) Interception aims to filter out invalid or irrelevant traffic to enhance the integrity and reliability of downstream data. H
arXiv:2605.00018v1 Announce Type: new Abstract: Data-driven MoCap-to-radar models generate plausible micro-Doppler spectrograms, but do they actually learn the underlying physics? We introduce a physi
small milestone: uninstalled the chatgpt app. codex is strict superset now! found something cool - among frontier models, @xai @grok 4.30 is the most intelligence per dollar you can get, beating even
arXiv:2604.27415v1 Announce Type: new Abstract: With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-d
arXiv:2602.00095v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. H
arXiv:2604.27510v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates und
arXiv:2604.04306v2 Announce Type: replace-cross Abstract: The increasing frequency and severity of climate related disasters have intensified the need for real time monitoring, early warning, and info
arXiv:2604.28192v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have increasingly incorporated reasoning mechanisms for complex robotic manipulation. However, existing approaches
RL is a bit of a double edged sword: in known territory performance increases, but in unknown territory the model tends to hallucinate that it is performing a completely different task it was trained
The latest crop of models remains below 1% on ARC-AGI-3 -- for now. Where will the scores be by the end of the year? GPT-5.5 & Opus 4.7 on ARC-AGI-3 - GPT-5.5: 0.43% - Opus 4.7: 0.18% We found 3 failu
The new Grok comes in below the latest Chinese open weights models, Grok 4 was at the frontier when released. (& Artificial Analysis: please stop using GDPval-AA which is not a useful test of anything
arXiv:2506.21444v4 Announce Type: replace Abstract: Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. Howeve
arXiv:2604.26577v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this
arXiv:2508.04325v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However,
Claude Code is tuned for Claude. Codex is tuned for OpenAI models. Until now, 𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 had fixed harness defaults, which meant it couldn't take advantage of the provider-specific optimizations that
arXiv:2604.02415v2 Announce Type: replace-cross Abstract: Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dime
arXiv:2604.26752v1 Announce Type: new Abstract: We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environmen
arXiv:2604.26018v1 Announce Type: cross Abstract: We introduce QERNEL, a foundational neural wavefunction that variationally solves families of parameterized many-electron Hamiltonians and captures th
arXiv:2510.20956v2 Announce Type: replace-cross Abstract: We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaki
arXiv:2604.26649v1 Announce Type: cross Abstract: Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with
As models, contexts, and workloads grow, hidden assumptions in inference infrastructure can surface as output anomalies. Reliability requires more than throughput, latency, and availability. It also r
arXiv:2604.25499v1 Announce Type: new Abstract: Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity
arXiv:2601.22154v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However
arXiv:2604.25313v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) models frequently produce answers grounded in parametric memory rather than the retrieved context, undermining the
arXiv:2510.18030v2 Announce Type: replace Abstract: Structured pruning is a practical approach to deploying large language models (LLMs) efficiently, as it yields compact, hardware-friendly architectu
arXiv:2604.25847v1 Announce Type: cross Abstract: Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problem
arXiv:2604.24833v1 Announce Type: cross Abstract: Despite transformative advances in generative motion synthesis, real-time interactive motion control remains dominated by traditional techniques. In t
arXiv:2604.05030v2 Announce Type: replace Abstract: Experiments probing natural language processing by both humans and LLMs suggest that the meaning of a semantic expression is indeterminate prior to
arXiv:2604.25895v1 Announce Type: cross Abstract: Preference-based alignment methods, most prominently Reinforcement Learning with Human Feedback (RLHF), use the judgments of human annotators to shape
arXiv:2604.24037v1 Announce Type: new Abstract: Emergent intelligence have played a major role in the modern AI development. While existing studies primarily rely on empirical observations to characte
arXiv:2604.24468v1 Announce Type: cross Abstract: Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource
arXiv:2604.22901v1 Announce Type: new Abstract: Diffusion models achieve remarkable success in time series generation. However, slow inference limits their practical deployment. We propose E^2-CRF (Er
arXiv:2604.23719v1 Announce Type: cross Abstract: Mechanistic interpretability research on emotion in large language models -- linear probing, activation patching, sparse autoencoder (SAE) feature ana
arXiv:2604.24076v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accur
arXiv:2604.22937v1 Announce Type: new Abstract: Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current v
arXiv:2509.21979v4 Announce Type: replace-cross Abstract: Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite thi
arXiv:2604.22884v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown promising potential in diverse understanding tasks, e.g., image and video analysis, math and physi
arXiv:2604.24447v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are promising for generalist robot control, but on-robot deployment is bottlenecked by real-time inference under t
arXiv:2604.24693v1 Announce Type: new Abstract: Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited
arXiv:2604.04733v2 Announce Type: replace-cross Abstract: Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concep
arXiv:2604.22822v1 Announce Type: cross Abstract: Object level hallucination remains a central reliability challenge for vision language models (VLMs), particularly in binary object existence verifica
arXiv:2602.17419v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can enrich industrial anomaly detection with semantic descriptions and anomaly reasoning, but they still la
arXiv:2506.23210v5 Announce Type: replace-cross Abstract: Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, data and syste
arXiv:2604.23267v1 Announce Type: new Abstract: Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) and in-context learning (ICL) - raising key questions about wh
arXiv:2604.23717v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) demonstrate remarkable capabilities in processing extended multi-modal sequences, yet incur high inference
arXiv:2603.17573v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) Models have become the mainstream solution for robot control, but suffer from slow inference speeds. Speculative
arXiv:2603.19482v2 Announce Type: replace Abstract: Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from vis
arXiv:2604.23865v1 Announce Type: cross Abstract: Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks an
arXiv:2509.06337v2 Announce Type: replace Abstract: Questionnaire-based surveys are foundational to social science research and public policymaking, yet traditional survey methods remain costly, time-
arXiv:2604.24636v1 Announce Type: cross Abstract: On-device Small Language Models (SLMs) promise fully offline, private AI experiences for mobile users (no cloud dependency, no data leaving the device
arXiv:2602.06542v3 Announce Type: replace Abstract: Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures requir