Search: “meta”

Human-AI Teaming Through the Lens of Calibration

arXiv:2606.10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of whic

10 Jun 2026

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One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

DGX agent

arXiv:2606.10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture lo

model-releasesarxiv-cs-lg

10 Jun 2026

With SoftBank apparently struggling to get a margin loan against it’s OpenAI shares, it’s maybe time to repost this, from two years ago. The…

model-releasesgary-marcus--x

With SoftBank apparently struggling to get a margin loan against it’s OpenAI shares, it’s maybe time to repost this, from two years ago. The vast majority of my earlier worries remain: 9 reasons that

10 Jun 2026

Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games

arXiv:2606.07790v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints

agentsarxiv-cs-lg

Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles

arXiv:2606.07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where

Hardware

Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

arXiv:2602.10016v3 Announce Type: replace-cross Abstract: Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing

hardwarearxiv-cs-ai

ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration

arXiv:2606.09381v1 Announce Type: new Abstract: Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the

safetyarxiv-cs-ro

Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks

arXiv:2506.06891v3 Announce Type: replace Abstract: We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al., 20

COF26: A new on-top functional for multiconfiguration pair-density functional theory

arXiv:2605.06215v2 Announce Type: replace-cross Abstract: Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient and accurate framework for computing electronic energies in

Applications

Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems

arXiv:2603.24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversi

applicationsarxiv-cs-ai

Endogenous Resistance to Activation Steering in Language Models

arXiv:2602.06941v2 Announce Type: replace-cross Abstract: Large language models can recover mid-generation from task-misaligned activation steering, producing explicit verbal restarts (e.g., ``wait, t

Siri AI at WWDC 2026

model-releasessimon-willison

Given how badly burned anyone who took Apple's 2024 WWDC Apple Intelligence announcements at face value was, I'm holding to a strict 'I'll believe it when I see it' policy for everything they announce

Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite L_p Moments

arXiv:2606.06855v1 Announce Type: cross Abstract: While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically

Bad take, you absolutely need to look at the data, manually

Bad take, you absolutely need to look at the data, manually Just learned: Software engineers used to do manual data labeling at Scale AI while Alex Wang was CEO. After he left, new leadership joined,

tutorialsjerry-liu--x

7 Jun 2026

not sure i see the issue here

not sure i see the issue here Just learned: Software engineers used to do manual data labeling at Scale AI while Alex Wang was CEO. After he left, new leadership joined, and were HORRIFIED to learn th

tutorialsjerry-liu--x

7 Jun 2026

GITCO: Gated Inference-Time Context Optimization in TSFMs

arXiv:2606.05332v1 Announce Type: new Abstract: Patch-based Time Series Foundation Models (TSFMs) suffer from context poisoning: structurally anomalous patches capture disproportionate attention and s

6 Jun 2026

GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data

arXiv:2606.05441v1 Announce Type: cross Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraini

tutorialsarxiv-cs-ai

6 Jun 2026

Spent more time learning deepagents from @LangChain with primary focus on integrating MCP servers with auth that doesn't conform completely …

model-releasesharrison-chase--x

Spent more time learning deepagents from @LangChain with primary focus on integrating MCP servers with auth that doesn't conform completely to the OAuth standard. ( In prep of a work coming my way ) B

6 Jun 2026

A Novel Method with Encoder-Decoder for Cross-Sensor Adaptation in Surface Shape Sensing with Sparse Strain Sensors

model-releasesarxiv-cs-ro

arXiv:2606.05903v1 Announce Type: new Abstract: Performance variations in sensor arrays, caused by intrinsic differences or installation conditions, can lead to inconsistent results during shape sensi

Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

arXiv:2606.06428v1 Announce Type: new Abstract: Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encodi

researcharxiv-cs-cl

SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization

arXiv:2606.06079v1 Announce Type: new Abstract: Agent skills, which consist of reusable strategies that guide agent reasoning and action, have shown strong potential for improving model capability at

agentsarxiv-cs-cl

Hardware

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

arXiv:2606.05979v1 Announce Type: new Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models. WLA takes textual instructions, images, and robot states as

hardwarearxiv-cs-ro

AIP: A Graph Representation for Learning and Governing Agent Skills

arXiv:2606.04781v1 Announce Type: new Abstract: Agent Skills today consist largely of free-form prose requiring the agent to read, interpret, and re-derive how to act in every session. This imposes tw

Applications

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

arXiv:2509.25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet,

applicationsarxiv-cs-ai

HighTide: An Agent-Curated Open-Source VLSI Benchmark Suite

arXiv:2606.04126v1 Announce Type: cross Abstract: We introduce HighTide, an evolving AI-assisted benchmark suite. Specifically, the contributions are: (i) a diverse open-source suite spanning multiple

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

model-releasesarxiv-cs-lg

arXiv:2602.12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.

Local Ai

locked n loaded🔒 Made in @comfyUI, @AdobeAE + @Photoshop, @suno Vid models: @Alibaba_Wan 2.2, @ltx_model 2.3 @thesystms FLW Img models: @op…

locked n loaded🔒 Made in @comfyUI, @AdobeAE + @Photoshop, @suno Vid models: @Alibaba_Wan 2.2, @ltx_model 2.3 @thesystms FLW Img models: @openai GPT 2.0, @googleai Nano Banana 2, @krea 2, @grok GFX: @t

local-aicomfyui--x

Off-Distribution Voices: Fanfiction Subgenres as Universal Vernacular Jailbreaks for Aligned LLMs

arXiv:2606.04483v1 Announce Type: new Abstract: Existing jailbreaks against aligned LLMs are discrete artifacts whose surface forms are easy to fingerprint and patch. We argue that the real failure mo

safetyarxiv-cs-cl

Self-Evolving Deep Research via Joint Generation and Evaluation

arXiv:2606.04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capab

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501.17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin

safetyarxiv-cs-ai

Distribution-Calibrated Inference Time Compute for Thinking LLM-as-a-Judge

arXiv:2512.03019v2 Announce Type: replace-cross Abstract: Thinking Large Language Models (LLMs) used as judges for pairwise preferences remain noisy at the single-sample level, and common aggregation

Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network

arXiv:2606.03574v1 Announce Type: cross Abstract: This work proposes a novel solution to predict pulsar timing residuals with limited data, addressing the critical challenge of data scarcity across sp

agentsarxiv-cs-lg

OMP: One-step Meanflow Policy with Directional Alignment

arXiv:2512.19347v3 Announce Type: replace Abstract: Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models s

safetyarxiv-cs-ro

Online Learning with Gradient-Variation Interval Regret

arXiv:2606.03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over ever

Re-Evaluating Continual Learning with Few-Shot Adaptation

arXiv:2606.03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standa

tutorialsarxiv-cs-ai

RogueMerge: Robust and Unified Attacks against LLM Model Merging

model-releasesarxiv-cs-lg

arXiv:2606.03344v1 Announce Type: cross Abstract: Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a cri

A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets

arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropi

researcharxiv-cs-cl

Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation

model-releasesarxiv-cs-cl

arXiv:2606.01629v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used for long-form generation, reliably evaluating long-form outputs has become a critical challenge. L

Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning

arXiv:2509.06948v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two widely used post-training paradigms for improving the

researcharxiv-cs-cl

Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs

arXiv:2603.24511v2 Announce Type: replace-cross Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on whi

Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications

arXiv:2606.00838v1 Announce Type: new Abstract: Inductive generalization is a framework for reinforcement learning (RL) generalization in which inductively related task instances admit inductively rel

safetyarxiv-cs-ai

Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors

arXiv:2602.03972v3 Announce Type: replace-cross Abstract: The best-arm identification (BAI) problem is one of the most fundamental problems in interactive machine learning, which has two flavors: the

Applications

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

Iterated Population Based Training with Task-Agnostic Restarts

arXiv:2511.09190v2 Announce Type: replace Abstract: Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based T

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

Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing

arXiv:2606.00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized s

safetyarxiv-cs-ai

OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources

model-releasesarxiv-cs-cl

arXiv:2405.01930v2 Announce Type: replace Abstract: This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summ

Quantifying the Salience of Geo-Cultural Values for Pluralistic Safety Alignment

arXiv:2606.00369v1 Announce Type: cross Abstract: Safe global deployment of AI models requires alignment with human values that vary across cultures. Yet rater pools in safety evaluation datasets rema

safetyarxiv-cs-lg