Search: “models”

arXiv:2606.03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to bu

agentsarxiv-cs-ai

Model Releases

TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering

DGX agent

arXiv:2606.02624v1 Announce Type: cross Abstract: AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather

model-releasesarxiv-cs-ai

3 Jun 2026

The DeepSpeak-Agentic Dataset

arXiv:2606.03686v1 Announce Type: new Abstract: We present DeepSpeak-Agentic, a dataset of videos comprising over 37 hours of semi-structured conversations between a human and an embodied AI agent. We

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit

arXiv:2501.02173v2 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance

researcharxiv-cs-lg

Towards Characterizing Scientific Image Utility and Upgradability

arXiv:2606.03401v1 Announce Type: new Abstract: Scientific images function as critical evidence in research communication, yet their integrity faces unprecedented threats from AI-generated content tha

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias

arXiv:2510.23469v2 Announce Type: replace Abstract: Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often

Applications

Training-Free Multi-Concept LoRA Composition with Prompt-Aware Weighting

arXiv:2606.03792v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) successfully enables personalization in text-to-image generation by adapting pre-trained diffusion models to specific visual

applicationsarxiv-cs-cv

TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning

arXiv:2606.03629v1 Announce Type: new Abstract: Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently,

Hardware

What’s new in serverless Managed Service for Apache Spark

Whether you use it for data preparation, real-time interactive queries, AI model training, or something entirely different, running Apache Spark at scale is demanding — you shouldn’t have to manage th

hardwaregoogle-cloud-ai

WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents

arXiv:2606.02908v1 Announce Type: cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute val

A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process

arXiv:2606.00821v1 Announce Type: new Abstract: This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine lea

A Shared Valence Axis Across Modern LLMs and Human EEG: The Saturation Regularity

arXiv:2606.00129v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features increasingly align with human cognition. We stud

safetyarxiv-cs-ai

Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents

arXiv:2606.01886v1 Announce Type: new Abstract: Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfo

Adaptive Order Policies for Masked Diffusion

arXiv:2606.00295v1 Announce Type: new Abstract: Masked diffusion models have seen great success in capturing data distributions over discrete sequences in domains such as text and proteins. These mode

safetyarxiv-cs-lg

Adaptive Time Series Reasoning via Segment Selection

arXiv:2602.18645v2 Announce Type: replace Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full

safetyarxiv-cs-lg

Agents

Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning

arXiv:2511.14460v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agen

agentsarxiv-cs-cl

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512.16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an age

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large

{alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion

arXiv:2606.00386v1 Announce Type: new Abstract: Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreg

local-aiarxiv-cs-cv

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification

arXiv:2602.16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal da

local-aiarxiv-cs-lg

Approximating f-Divergences with Rank Statistics

arXiv:2601.22784v2 Announce Type: replace-cross Abstract: We introduce a rank-statistic approximation of f-divergences that avoids explicit density-ratio estimation by working directly with the distri

ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training

arXiv:2606.00602v1 Announce Type: new Abstract: Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak,

Applications

Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation

arXiv:2606.01900v1 Announce Type: new Abstract: Generative video models have achieved remarkable visual fidelity and temporal coherence, yet intentional camera control remains elusive. Existing framew

applicationsarxiv-cs-cv

Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision

arXiv:2606.01397v1 Announce Type: cross Abstract: A fixed-wing UAV must hold airspeed, altitude, and heading references under wind, gusts, and turbulence, channels coupled so that correcting one can d

Tools

banger writeup on what it takes to make MiniMax M3 go brrrr!

This post likely provides technical insights and optimization strategies for running or fine-tuning the MiniMax M3 model efficiently, discussing performance enhancements and practical implementation t

toolstogether-ai--x

Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler

arXiv:2606.00783v1 Announce Type: cross Abstract: Reliable quantification of malaria dynamics in sub-Saharan Africa is hindered by short, noisy, and spatially heterogeneous surveillance records. In Gh

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation

arXiv:2606.00827v1 Announce Type: cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Exis

safetyarxiv-cs-ai

Beyond Task Success: Behavioral and Representational Diagnostics for WAM and VLA

arXiv:2606.01095v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies and World-Action Models (WAM) represent two increasingly important paradigms for robotic manipulation. However,

Beyond Visual Memory: Mechanistic Diagnostics of Latent Visual Reasoning

arXiv:2606.01287v1 Announce Type: cross Abstract: Recent latent visual reasoning methods achieve substantial gains by inserting continuous latent tokens into multimodal language models. These gains ar

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization

arXiv:2606.00079v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation, but their deployment remains memory-inte

Boundary-Protection W8A8 HiFloat8 Quantization for Large-Scale Text-to-Video Diffusion Transformers

arXiv:2606.00957v1 Announce Type: new Abstract: We present a post-training quantization (PTQ) approach for Wan2.1-T2V-14B, a 14-billion-parameter text-to-video diffusion transformer, targeting the W8A

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

arXiv:2509.23782v4 Announce Type: replace Abstract: While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to the

researcharxiv-cs-cl

CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery

arXiv:2606.00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public he

Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM judges that check an agent’s work against rubric criter…

model-releasesharrison-chase--x

Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM judges that check an agent’s work against rubric criteria: they're used both in agent benchmarking and as reward si

Capability Self-Assessment: Teaching LLMs to Know Their Limits

arXiv:2606.00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fundamental for reliable intelligent systems. Yet we

local-aiarxiv-cs-ai

Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand

arXiv:2606.00811v1 Announce Type: cross Abstract: Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) an

local-aiarxiv-cs-ai

CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping

arXiv:2509.14143v2 Announce Type: replace Abstract: Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natu

safetyarxiv-cs-ro

Co-training with Ego-centric Video and Demonstration for Robot Navigation Task

arXiv:2606.01951v1 Announce Type: new Abstract: Vision-language-action (VLA) models are promising for diverse robotic tasks, but their performance heavily depends on large-scale high-quality training

researcharxiv-cs-ro

Concept Heterogeneity-aware Representation Steering

arXiv:2603.02237v2 Announce Type: replace-cross Abstract: Representation steering offers a lightweight mechanism for controlling the behavior of large language models (LLMs) by intervening on internal

Continual Learning involves engineering whole systems including Efficient Verifiers to make RL/fine-tuning and running evaluations much chea…

model-releasesharrison-chase--x

Continual Learning involves engineering whole systems including Efficient Verifiers to make RL/fine-tuning and running evaluations much cheaper at scale! some initial work we’re releasing from LangCha

CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation

model-releasesarxiv-cs-cl

arXiv:2606.01815v1 Announce Type: new Abstract: Evaluating LLM agents in realistic service scenarios requires complex task dependencies, imperfect user behavior, and an evaluation that accommodates mu

Hardware

CRAFT: Fine-Grained Cost-Aware Expert Replication For Efficient Mixture-of-Experts Serving

arXiv:2603.28768v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has recently emerged as the mainstream architecture for efficiently scaling large language models while maintaining n

hardwarearxiv-cs-lg

CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMs

arXiv:2602.00742v2 Announce Type: replace Abstract: User modeling characterizes individuals through their preferences and behavioral patterns to enable personalized simulation and generation with Larg

researcharxiv-cs-cl

Design-MLLM: A Reinforcement Alignment Framework for Verifiable and Aesthetic Interior Design

arXiv:2603.13312v2 Announce Type: replace-cross Abstract: Interior design is a requirements-to-visual-plan generation process that must simultaneously satisfy verifiable spatial feasibility and compar

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606.02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

researcharxiv-cs-lg

Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark

arXiv:2606.02246v1 Announce Type: new Abstract: To operate in the physical world, embodied agents must perceive their environment in an 'always-on' fashion, selectively accessing the most informative

Enhancing Blind Source Separation with Dissociative Principal Component Analysis

arXiv:2411.12321v2 Announce Type: replace Abstract: Principal component analysis (PCA) and its sparse variants (sPCA) are widely used as a precursor to independent component analysis (ICA) for blind s

Evaluating Reliability Asymmetries in Chinese Factual Search and AI Answers

arXiv:2602.22221v2 Announce Type: replace-cross Abstract: Search engines and AI-powered systems increasingly mediate access to factual information, yet their reliability remains difficult to evaluate