Search: “models”

arXiv:2606.11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cos

tutorialsarxiv-cs-lg

Research

Do LLMsMakeNeural Distinguishers Wise?

DGX agent

arXiv:2606.10692v1 Announce Type: cross Abstract: Neural distinguishers are a cryptanalysis method for symmetric-key cryptography that trains machine learning models on pairs of plaintexts and ciphert

researcharxiv-cs-lg

10 Jun 2026

Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017

arXiv:2606.11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transfo

researcharxiv-cs-lg

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606.10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challen

researcharxiv-cs-lg

Effective Training Principles of Physical Reservoirs

arXiv:2606.10130v1 Announce Type: cross Abstract: Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directl

Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling

arXiv:2606.10439v1 Announce Type: cross Abstract: The rapid progress of large language models (LLMs) has opened up a new frontier for automatic speech recognition (ASR), making their effective integra

safetyarxiv-cs-cl

Agents

EstRTL: Functional Estimation Guided RTL Code Generation

arXiv:2606.09867v1 Announce Type: cross Abstract: Optimizing register transfer level (RTL) code is of vital importance in hardware design. Large language models (LLMs) provide new methods for the auto

agentsarxiv-cs-ai

everything your agents do, in a bucket you own. private by default. session traces, task claims, artifacts — claude code, hermes, whatever y…

model-releasesclem-delangue--x

everything your agents do, in a bucket you own. private by default. session traces, task claims, artifacts — claude code, hermes, whatever you run. and make your agents friends via messaging https://h

Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fix

arXiv:2606.09884v1 Announce Type: cross Abstract: We study two reproducible failure modes of deep multi-agent reinforcement learning in continuous-time pricing markets: (i) tacit cartel formation betw

Applications

FoA-SR: Faithful or Aesthetic? Profile-Aware Preference Optimization for Real-World Image Super-Resolution

arXiv:2606.10275v1 Announce Type: new Abstract: Real-world image super-resolution (SR) is often designed with a single restoration objective, despite the current capacity of generative models to produ

applicationsarxiv-cs-cv

From data to decisions: how LSEG is scaling trusted AI

LSEG (London Stock Exchange Group) is implementing trusted AI systems to enhance decision-making processes across its financial services operations. The article likely discusses how LSEG is leveraging

model-releasesopenai

Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions

arXiv:2606.10942v1 Announce Type: cross Abstract: As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant b

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting

arXiv:2606.10611v1 Announce Type: cross Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guid

Tutorials

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection

arXiv:2510.08906v2 Announce Type: replace-cross Abstract: Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. W

tutorialsarxiv-cs-lg

Grok Build is pretty good at optimizing my code in one shot. Prompt: I want you to optimize it entirely on GPU to speed it up. Measure two m…

model-releaseselon-musk--x

Grok Build is pretty good at optimizing my code in one shot. Prompt: I want you to optimize it entirely on GPU to speed it up. Measure two metrics: the result must compare with the golden image (CPU)

HarmoView: Harmonizing Multi-View Constraints for Identity-Consistent Video Generation

arXiv:2606.10839v1 Announce Type: new Abstract: Current identity-consistent video generation methods struggle to preserve appearance fidelity under large viewpoint changes. While introducing multi-vie

Here are Fable's pelicans for the different thinking effort levels, plus how much each one cost to generate via the Claude API

model-releasessimon-willison--x

Simon Willison demonstrated Fable's pelican illustrations generated at different Claude API thinking effort levels, showcasing how increased computational investment affects output quality and cost. T

Industry

Here is a thought that might sounds odd/wrong the first time you hear it: recorded media is a phase. A long one, but a phase. For most of hu…

industrycristobal-valenzuela--x

Here is a thought that might sounds odd/wrong the first time you hear it: recorded media is a phase. A long one, but a phase. For most of human history, media was live. Stories were told around fires

Agents

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

arXiv:2606.10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degr

agentsarxiv-cs-ai

How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs

arXiv:2606.10646v1 Announce Type: cross Abstract: Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat

researcharxiv-cs-cl

I argued, publicly, AGAINST each of these foolish predictions– and got endless shit for it. Moral: on X, you probably shouldn’t trust the “w…

model-releasesgary-marcus--x

I argued, publicly, AGAINST each of these foolish predictions– and got endless shit for it. Moral: on X, you probably shouldn’t trust the “wisdom” of the crowds. Remember all the AI predictions people

I too have felt the latest Claude's to be too smart its bored. Glad others are noticing the same

model-releasesjerry-liu--x

I too have felt the latest Claude's to be too smart its bored. Glad others are noticing the same Claude Fable 5 thinks document parsing is beneath it It is absolutely crushing on all reasoning-intensi

Local Ai

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606.09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment. Existing methods rely predomi

local-aiarxiv-cs-ai

IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference

arXiv:2606.09916v1 Announce Type: cross Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning. Both KV memory and KV read b

Interactions Between Crosscoder Features: A Compact Proofs Perspective

arXiv:2606.09940v1 Announce Type: cross Abstract: Dictionary learning methods like Sparse Autoencoders (SAEs) and crosscoders attempt to explain a model by decomposing its activations into independent

Interpretable Temporal Facial-Region Motion Analysis for In-the-Wild Parkinson's Disease Video Classification

arXiv:2606.10088v1 Announce Type: new Abstract: Reduced facial expressivity is a common motor manifestation of Parkinson's disease (PD), often described as hypomimia or facial bradykinesia. This paper

IPSM-Bench: A New Intermediate Phase Segmentation Benchmark in Microstructure Images of Zinc-Based Absorbable Biomaterials

arXiv:2606.11001v1 Announce Type: new Abstract: Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characte

It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO

arXiv:2606.10931v1 Announce Type: new Abstract: Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale post-

safetyarxiv-cs-cl

LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization

arXiv:2606.10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs). Current QAT methods are mainly based on scalar quan

Letter: Texas Governor Greg Abbott released regulatory recommendations for state lawmakers to ensure that data centers shoulder the costs of their growth (The Texas Tribune)

The Texas Tribune: Letter: Texas Governor Greg Abbott released regulatory recommendations for state lawmakers to ensure that data centers shoulder the costs of their growth — The recommendations for l

model-releasestechmeme

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners

arXiv:2511.10234v3 Announce Type: replace-cross Abstract: While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operat

mlr3mbo: Bayesian Optimization in R

arXiv:2603.29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point p

Moonshine: An Autonomous Mathematical Research Agent Centered on Conjecture Generation

arXiv:2606.10806v1 Announce Type: new Abstract: Moonshine is an autonomous agent whose central objective is to generate mathematical conjectures. Its core capability is to extract structure from class

nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection

arXiv:2606.09934v1 Announce Type: new Abstract: Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in op

Local Ai

Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

arXiv:2606.10909v1 Announce Type: cross Abstract: Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottlene

local-aiarxiv-cs-lg

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks

arXiv:2605.05857v2 Announce Type: replace Abstract: Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficu

safetyarxiv-cs-lg

Optimality of FSQ Tokens for Continuous Diffusion for Categorical Data with Application to Text-to-Speech

arXiv:2606.09962v1 Announce Type: cross Abstract: Continuous diffusion for categorical data is a framework belonging to the diffusion family and aiming at generating discrete data. The scientific inte

PathRelax: Parallel-Path Relaxed Speculative Jacobi Decoding for Accelerating Auto-Regressive Text-to-Image Generation

arXiv:2606.10492v1 Announce Type: new Abstract: The growing need for high-resolution image generation in autoregressive text-to-image models has resulted in extended token sequences, significantly inc

researcharxiv-cs-cv

PRC-linked influence operations are targeting AI debates in the US

OpenAI reported that People's Republic of China (PRC)-linked actors are conducting coordinated influence operations aimed at shaping artificial intelligence policy debates in the United States. These

model-releasesopenai

Agents

Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation

arXiv:2606.10875v1 Announce Type: new Abstract: Large language models (LLMs) rely on tool use to act as autonomous agents, yet often fail in multi-step execution due to insufficient tool-related knowl

agentsarxiv-cs-cl

Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement

arXiv:2603.08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and

Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks

arXiv:2606.10324v1 Announce Type: new Abstract: The analogy between deep neural network forward passes and renormalization group (RG) flows has been repeatedly noted in the literature, but existing tr

RAT: Reference-Augmented Training for ASV Anti-Spoofing

arXiv:2606.10908v1 Announce Type: cross Abstract: We introduce a spoofing countermeasure architecture conditioned on speaker-reference recordings, but observe that it converges to a solution that effe

Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning

arXiv:2606.10346v1 Announce Type: new Abstract: Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discoverin

safetyarxiv-cs-ai

Recoverable but Not Stationary:Local Linear Structures in Weights and Activations

arXiv:2606.10929v1 Announce Type: cross Abstract: Task vectors, LoRA, activation steering, and random search around pretrained weights all suggest that learned behaviour can be controlled by linear di

Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective

arXiv:2602.01179v2 Announce Type: replace Abstract: Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via inter

researcharxiv-cs-lg

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages

model-releasesarxiv-cs-cl

arXiv:2510.07061v2 Announce Type: replace Abstract: While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated a

Hardware

Run DiffusionGemma on NVIDIA for Developer-Ready, High-Throughput Text Generation

DiffusionGemma is an experimental open model built for exceptionally fast text generation that NVIDIA has optimized to run on GeForce RTX GPUs, RTX PRO, and DGX Spark systems. Rather than generating t

hardwarenvidia-developer