Knowledge catalogue
arXiv:2605.12493v1 Announce Type: new Abstract: Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workfl
arXiv:2605.11209v1 Announce Type: new Abstract: While existing benchmarks demonstrate the near-perfect performance of large language models (LLMs) on various tasks, this apparent saturation often obsc
arXiv:2605.12361v1 Announce Type: new Abstract: Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain dis
arXiv:2605.12477v1 Announce Type: cross Abstract: LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. Whil
arXiv:2605.12185v1 Announce Type: new Abstract: Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect para
arXiv:2412.13050v2 Announce Type: replace-cross Abstract: In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language M
arXiv:2605.11836v1 Announce Type: cross Abstract: Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabiliti
arXiv:2501.02955v2 Announce Type: replace Abstract: In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grain
arXiv:2605.12134v1 Announce Type: new Abstract: Recent text-to-image models produce high-quality images, yet text ambiguity hinders precise control when specific styles or objects are required. There
arXiv:2605.11530v1 Announce Type: new Abstract: Single-model ensembles (SMEs) have attracted attention as a way to approximate some of the benefits of deep ensembles within a single network. However,
arXiv:2605.12176v1 Announce Type: new Abstract: This paper presents the Constrained Multi-Task Representation Learning (CMTRL) framework for linear bandits. We consider T linear bandit tasks in a d di
arXiv:2605.11396v1 Announce Type: new Abstract: The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings throug
AI Security Institute: Mythos Preview is the first AI model to complete both of AISI's cyber ranges, which measure models' cyberattack capabilities; GPT-5.5 solved only one of them — In February 2026,
arXiv:2605.11665v1 Announce Type: new Abstract: Robot learning research is fragmented across policy families, benchmark suites, and real robots; each implementation is entangled with the others in a c
arXiv:2605.11762v1 Announce Type: new Abstract: Learning robust navigation policies remains a core challenge in robotics. Offline imitation learning suffers from distribution shift and compounding err
arXiv:2509.06697v2 Announce Type: replace-cross Abstract: Accurate forecasting of exchange rates remains a persistent challenge, particularly for emerging economies such as Brazil, Russia, India, and
arXiv:2605.12233v1 Announce Type: new Abstract: Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret
arXiv:2605.12207v1 Announce Type: cross Abstract: We study the extit{parameter placement problem}: given a fixed budget of k trainable entries within the B matrix of a LoRA adapter (A frozen), does th
NVIDIA Nemotron models received native support integration in Deep Agents version 0.6, enabling improved language model capabilities within the LangChain framework. This update allows developers to le
At the Android Show yesterday, we introduced Googlebooks: a new category of premium laptops built with Gemini’s helpfulness at the core. Designed for Gemini Intelligence, Googlebooks will give persona
arXiv:2605.12345v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) techniques offer task-specific fine-tuning at a fraction of the cost of full fine-tuning, but require separate fi
arXiv:2605.11459v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLA
arXiv:2605.12199v1 Announce Type: new Abstract: Emergent misalignment (EM), where fine-tuning on a narrow task (like insecure code) causes broad misalignment across unrelated domains, was first demons
arXiv:2605.12313v1 Announce Type: new Abstract: Multi-hop question answering (QA) remains a significant challenge in the biomedical domain, requiring systems to integrate information across multiple s
arXiv:2605.10953v1 Announce Type: cross Abstract: The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dens
arXiv:2510.05497v5 Announce Type: replace-cross Abstract: Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open-weight models, achieving remarkable m
arXiv:2605.11427v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must downlo
arXiv:2602.08058v2 Announce Type: replace Abstract: In the presence of occlusions and measurement noise, geometrically accurate scene reconstructions -- which fit the sensor data -- can still be physi
arXiv:2602.15473v2 Announce Type: replace Abstract: Gradient-based optimizers are highly sensitive to design choices in their adaptive learning rate mechanisms. To address this limitation, we introduc
arXiv:2605.11497v1 Announce Type: new Abstract: Zero-shot skeleton-based action recognition (ZSSAR) is typically treated as a skeleton-text alignment problem: encode joint-coordinate sequences, align
arXiv:2605.11652v1 Announce Type: cross Abstract: We study posterior contraction rates for sparse Bayesian Kolmogorov-Arnold networks (KANs) over anisotropic Besov spaces, providing a statistical foun
arXiv:2605.11303v1 Announce Type: new Abstract: We investigate the use of Large Language Models (LLMs) for zero-shot prediction of Ryff Psychological Well-Being (PWB) scores from spontaneous speech. U
arXiv:2605.12160v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are typically evaluated as if the user had finished typing or speaking before the robot begins acting. In real dep
arXiv:2605.12243v1 Announce Type: new Abstract: Conversational scams, such as romance and investment scams, are emerging as a major form of online fraud. Unlike one-shot scam lures such as fake lotter
arXiv:2605.11363v1 Announce Type: cross Abstract: Presentation generation is moving beyond static slide creation toward end-to-end presentation video generation with research grounding, multimodal med
arXiv:2605.12260v1 Announce Type: new Abstract: Long-horizon language agents accumulate conversation history far faster than any fixed context window can hold, making memory management critical to bot
arXiv:2605.11534v1 Announce Type: new Abstract: When an LLM-based embodied agent fails at a household task, the culprit could be misidentified objects, forgotten sub-goals, or poor action sequencing -
arXiv:2605.11845v1 Announce Type: new Abstract: Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation probabilities a
arXiv:2512.24558v2 Announce Type: replace-cross Abstract: Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their sc
arXiv:2605.12194v1 Announce Type: cross Abstract: Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access t
arXiv:2605.11907v1 Announce Type: new Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as
arXiv:2602.02406v2 Announce Type: replace-cross Abstract: Data-driven algorithm design automates hyperparameter tuning, but its statistical foundations remain limited because model performance can dep
arXiv:2504.14129v4 Announce Type: replace Abstract: The challenge of tracing the source attribution of forged faces has gained significant attention due to the rapid advancement of generative models.
arXiv:2605.10959v1 Announce Type: new Abstract: There is currently no unified metric for evaluating the efficiency of quantized neural networks. We propose QuIDE, built around the Intelligence Index I
Llama-eval is a proposed evaluation tool for llama.cpp designed to standardize and improve comparability of community-run evaluations. The tool aims to address inconsistencies in how different users b
arXiv:2605.11887v1 Announce Type: new Abstract: Large language models have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque, li
🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time. Hermes Agent, here we go!! ⚡️ @NousResearch Qwen 3.6 Plus by @Alibaba_Qwen is now FREE for a limited time on Nous Portal! Nous Portal i
arXiv:2312.06950v3 Announce Type: replace-cross Abstract: Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal
Really curious when Gemini is going to join the Cowork & Codex race to build a local app that isn’t just for developers. Antigravity hasn’t posted updates to X in a month, and remains very software fo
arXiv:2409.08290v4 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their eve
arXiv:2504.12326v3 Announce Type: replace Abstract: Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e
arXiv:2605.11232v1 Announce Type: cross Abstract: Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements dif
arXiv:2605.11771v1 Announce Type: new Abstract: Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to dis
arXiv:2605.11659v1 Announce Type: new Abstract: Cross-Domain Few-Shot Learning (CDFSL) aims to adapt large-scale pretrained models to specialized target domains with limited samples, yet the few-shot
arXiv:2605.12006v1 Announce Type: new Abstract: The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in
arXiv:2605.11800v1 Announce Type: cross Abstract: Large language models (LLMs) with mixture-of-experts (MoE) architectures achieve remarkable scalability by sparsely activating a subset of experts per
arXiv:2605.12476v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (SMoE) models enable scaling language models efficiently, but training them remains challenging, as routing can collapse ont
arXiv:2605.12386v1 Announce Type: new Abstract: Robotic manipulation is typically evaluated by task success, but successful completion does not guarantee safe execution. Many safety failures are tempo
arXiv:2605.12022v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on standard knowledge evaluation benchmarks, yet recent work shows that their knowledge capabili
arXiv:2605.12049v1 Announce Type: new Abstract: Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological