model releases

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation

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

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@nvidia Nemotron native support in Deep Agents 0.6 #interrupt #langchain

DGX agent

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

model-releasesharrison-chase--x

13 May 2026

Our continued commitment to Chromebooks, and looking ahead

model-releasesgoogle-cloud-ai

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

Output Composability of QLoRA PEFT Modules for Plug-and-Play Attribute-Controlled Text Generation

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

Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models

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

Overtrained, Not Misaligned

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

Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering

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

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

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

Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference

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

PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming

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

Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling

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

POP: Prior-Fitted First-Order Optimization Policies

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

PoseBridge: Bridging the Skeletonization Gap for Zero-Shot Skeleton-Based Action Recognition

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

Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces

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

Predicting Psychological Well-Being from Spontaneous Speech using LLMs

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

Premover: Fast Vision-Language-Action Control by Acting Before Instructions Are Complete

model-releasesarxiv-cs-ro

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

PreScam: A Benchmark for Predicting Scam Progression from Early Conversations

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

PresentAgent-2: Towards Generalist Multimodal Presentation Agents

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

PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents

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

PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments

model-releasesarxiv-cs-ro

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 -

Probabilistic Calibration Is a Trainable Capability in Language Models

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

Probabilistic Computers for Neural Quantum States

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

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

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

Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models

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

Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

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

PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution

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.

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization

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

Quite excited about llama-eval, a proposed eval tool for llama.cpp. Could be a nice step toward more comparable community evals 🎉 https://g…

model-releasesgeorgi-gerganov--x

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

Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models

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

🚀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

model-releasesqwen--x

READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling

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 …

model-releasesethan-mollick--x

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

Reconsidering the energy efficiency of spiking neural networks

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

Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis

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

Rethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack

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

Revisiting Shadow Detection from a Vision-Language Perspective

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

Reviving In-domain Fine-tuning Methods for Source-Free Cross-domain Few-shot Learning

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

Robust Promptable Video Object Segmentation

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

ROMER: Expert Replacement and Router Calibration for Robust MoE LLMs on Analog Compute-in-Memory Systems

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

Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts

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

SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation

model-releasesarxiv-cs-ro

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

SAGE: Scalable Automated Robustness Augmentation for LLM Knowledge Evaluation

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

Scaling Laws and Tradeoffs in Recurrent Networks of Expressive Neurons

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

SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification

arXiv:2605.11022v1 Announce Type: cross Abstract: Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction

Search Your Block Floating Point Scales!

arXiv:2605.12464v1 Announce Type: new Abstract: Quantization has emerged as a standard technique for accelerating inference for generative models by enabling faster low-precision computations and redu

See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model

arXiv:2605.11817v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown remarkable promise in robotics manipulation, yet their high computational cost hinders real-time deploy

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

arXiv:2605.12208v1 Announce Type: cross Abstract: Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of inte

SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation

arXiv:2605.12389v1 Announce Type: new Abstract: Segmenting small and sparse structures in large-scale images is fundamentally constrained by voxel-level, lattice-bound computation and extreme class im