Search: “models”

How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings

arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary

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Research

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing

DGX agent

arXiv:2606.00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary tran

researcharxiv-cs-ai

2 Jun 2026

Industry

If your daughter needs tutoring in algebra, you can probably find someone cheaper than Albert Einstein. Giving every task to GPT5.5 or Opus …

If your daughter needs tutoring in algebra, you can probably find someone cheaper than Albert Einstein. Giving every task to GPT5.5 or Opus 4.8 is overkill. Often times you can get the task done just

industryclem-delangue--x

Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference

arXiv:2606.01711v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computation

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

arXiv:2606.00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- alg

Interpretable Self-Supervised Learning via Representer Landmarks and Nystrom Approximation

arXiv:2509.24467v3 Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necess

safetyarxiv-cs-lg

Interpreto: An Explainability Library for Transformers

arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two compleme

Is Zero-Shot Super-Resolution Possible in Operator Learning?

arXiv:2606.00296v1 Announce Type: cross Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate pre

researcharxiv-cs-lg

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it…

applicationsethan-mollick--x

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue

Iteris: Agentic Research Loops for Computational Mathematics

arXiv:2606.02484v1 Announce Type: new Abstract: Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition pr

agentsarxiv-cs-ai

Hardware

Join the livestream to hear from our team members @karan4d and @yoniebans live from @nvidia! https://www.youtube.com/watch?v=pgQDbRMa2Eg

Nous Research is hosting a livestream featuring team members Karan and Yoni speaking from NVIDIA, likely discussing AI research, model development, or collaboration between Nous Research and NVIDIA. T

hardwarenous-research--x

KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning

arXiv:2606.00532v1 Announce Type: new Abstract: Context engineering can improve large language models without updating their weights, but mathematical reasoning exposes a key limitation: feedback accu

Tutorials

Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning

arXiv:2511.07910v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabl

tutorialsarxiv-cs-cl

LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification

arXiv:2412.12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm sh

agentsarxiv-cs-lg

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

arXiv:2509.24627v2 Announce Type: replace Abstract: Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conserva

researcharxiv-cs-lg

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning

arXiv:2606.02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing appr

LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs

arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of

safetyarxiv-cs-ro

Tutorials

LLMs Need Encoders for Semantic IDs Too

arXiv:2606.00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw

tutorialsarxiv-cs-ai

Hardware

Lodestar: An Online-Learning LLM Inference Router

arXiv:2606.00946v1 Announce Type: cross Abstract: Efficiently serving large language model (LLM) inference tasks is crucial both for user-perceived latency such as time-to-first-token (TTFT) and for G

hardwarearxiv-cs-ai

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606.02004v1 Announce Type: new Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data. A recurring obstacle is

Measurement Geometry and Design for Trustworthy Generative Inverse Problems

arXiv:2606.02309v1 Announce Type: cross Abstract: Generative models are increasingly used as priors for inverse problems, but their ability to produce realistic images creates a basic trust problem: a

safetyarxiv-cs-cv

MESA: Improving MoE Safety Alignment via Decentralized Expertise

arXiv:2606.00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dy

Minimax-Optimal Policy Regret in Partially Observable Markov Games

arXiv:2606.02363v1 Announce Type: new Abstract: We study sequential decision-making in partially observable environments against strategic, adaptive opponents, modeled as partially observable Markov g

safetyarxiv-cs-lg

MINTS: Minimalist Thompson Sampling

arXiv:2606.01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all para

Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks

arXiv:2606.02042v1 Announce Type: new Abstract: Continual industrial anomaly detection with diffusion models suffers from historical normality prior drift and catastrophic forgetting. Existing continu

Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

arXiv:2606.00820v1 Announce Type: new Abstract: Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that co

agentsarxiv-cs-cl

Tutorials

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

arXiv:2606.02510v1 Announce Type: new Abstract: Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capac

tutorialsarxiv-cs-cv

Not What, But How: A Communicative Audit of LLM Response Framing

arXiv:2606.02493v1 Announce Type: new Abstract: Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses

Local Ai

Ollama can't list this C# game script, because it might cause destruction.

A Reddit post discussing an issue where Ollama (an AI model tool) refuses to process or list a C# game script due to safety concerns about potential destructive code. The post likely explores the limi

local-air-ollama

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

arXiv:2606.00135v1 Announce Type: cross Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This pa

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction

Local Ai

One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition

arXiv:2606.00936v1 Announce Type: new Abstract: Visual Place Recognition (VPR) is fundamental to long-term robot localization and SLAM, yet current systems overwhelmingly rely on RGB input, implicitly

local-aiarxiv-cs-cv

OP-LoRA: The Blessing of Dimensionality

arXiv:2412.10362v2 Announce Type: replace-cross Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-cond

OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence

arXiv:2603.14771v3 Announce Type: replace Abstract: Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting th

agentsarxiv-cs-ai

PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation

arXiv:2606.00515v1 Announce Type: cross Abstract: Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language

applicationsarxiv-cs-ai

PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder

arXiv:2606.01537v1 Announce Type: new Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data. We present

Paradoxical noise preference in RNNs

arXiv:2601.04539v2 Announce Type: replace-cross Abstract: In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biologi

Parametric Social Identity Injection and Diversification in Public Opinion Simulation

arXiv:2603.16142v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costl

applicationsarxiv-cs-cl

Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents

arXiv:2606.00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To addre

safetyarxiv-cs-lg

Physics-Aware Linearized ADMM and Its Unrolling

arXiv:2606.01652v1 Announce Type: cross Abstract: Recently, partial differential equations (PDEs) have been used to directly model the measurement process in signal processing, although their evaluati

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation

arXiv:2602.01662v4 Announce Type: replace Abstract: Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulati

agentsarxiv-cs-ro

PMC-InterCPT: Rethinking Biomedical Interleaved Data for Multimodal Continued Pretraining

arXiv:2606.01049v1 Announce Type: new Abstract: Large-scale biomedical image-text datasets extracted from scientific literature provide valuable resources for medical multimodal model training. These

Hardware

Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction

arXiv:2602.08585v2 Announce Type: replace-cross Abstract: Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typic

hardwarearxiv-cs-ai

Principle-Evolvable Scientific Discovery via Uncertainty Minimization

arXiv:2602.06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencie

ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning

arXiv:2606.02576v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually a

applicationsarxiv-cs-cv

PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making

arXiv:2606.01313v1 Announce Type: cross Abstract: Open-vocabulary navigation requires embodied agents to manage significant perception uncertainty stemming from semantic ambiguity and model errors. Ho

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

arXiv:2604.15231v2 Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (

agentsarxiv-cs-ai

Rank-Constrained Deep Matrix Completion for Group Recommendation

arXiv:2606.01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual p

applicationsarxiv-cs-ai