Search: “models”

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems

arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th

Entropy Equivalence Testing

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DGX agent

arXiv:2605.23225v1 Announce Type: cross Abstract: We introduce the problem of entropy equivalence testing for probability distributions, a relaxation of the well-studied closeness testing problem, whe

model-releasesarxiv-cs-lg

25 May 2026

Evaluating Memory Structure in LLM Agents

arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv:2605.23163v1 Announce Type: new Abstract: End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficie

safetyarxiv-cs-cl

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs

arXiv:2605.23078v1 Announce Type: cross Abstract: Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters.

researcharxiv-cs-cl

GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

arXiv:2508.14083v3 Announce Type: replace-cross Abstract: The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a s

applicationsarxiv-cs-ai

Graph Alignment Topology as an Inductive Bias for Grounding Detection

arXiv:2605.22963v1 Announce Type: cross Abstract: Large Language Models (LLMs) are optimized to produce distributionally plausible continuations rather than to explicitly verify whether generated prop

Local Ai

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

arXiv:2605.23043v1 Announce Type: new Abstract: Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an e

local-aiarxiv-cs-cl

HTMuon: Improving Muon via Heavy-Tailed Spectral Correction

arXiv:2603.10067v2 Announce Type: replace-cross Abstract: Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonal

Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems

arXiv:2605.23109v1 Announce Type: new Abstract: AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage t

Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion

arXiv:2605.23847v1 Announce Type: new Abstract: Large behaviour models have transformed the field of robotic manipulation, but prohibitive data requirements have thus far prevented a revolution simila

safetyarxiv-cs-ro

IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents

arXiv:2604.05157v2 Announce Type: replace Abstract: Computer-Use Agents (CUAs) leverage large language models to execute GUI operations on desktop environments, yet they generate actions without evalu

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering

arXiv:2605.23721v1 Announce Type: new Abstract: Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single

applicationsarxiv-cs-cl

Learning Safely Without Knowing the World:COMPASS-Hedge

arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv

Metadata Predictability Is Not Evidence Dependence: An Intervention-Based Audit for Weak-Label Benchmarks

arXiv:2605.23701v1 Announce Type: new Abstract: We study a protocol-level test for weak-label benchmarks: whether benchmark outputs change when the provided evidence is intervened on. Metadata-only sh

Moonwalk: Inverse-Forward Differentiation

arXiv:2402.14212v4 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth

Non-normal spectral signatures of instability in neural network training dynamics

arXiv:2605.23476v1 Announce Type: new Abstract: Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous

One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents

arXiv:2605.23652v1 Announce Type: new Abstract: On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.7

OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations

arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they re

Online Partitioned Local Depth for semi-supervised applications

arXiv:2512.15436v2 Announce Type: replace-cross Abstract: We introduce an extension of the partitioned local depth (PaLD) algorithm that is adapted to online applications such as semi-supervised predi

Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems

arXiv:2605.23297v1 Announce Type: new Abstract: AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, a

Optimization of randomized neural networks for transfer operator approximation

arXiv:2605.23689v1 Announce Type: new Abstract: RaNNDy is a randomized neural network architecture for the data-driven approximation of transfer operators associated with complex dynamical systems. Th

Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes

arXiv:2604.07796v2 Announce Type: replace-cross Abstract: In this paper, we study the problem of mean estimation under 1-bit communication constraints. We propose a novel adaptive mean estimator based

PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

arXiv:2605.23074v1 Announce Type: new Abstract: The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating l

PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation

model-releasesarxiv-cs-cv

arXiv:2503.06684v3 Announce Type: replace Abstract: Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing

PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations

arXiv:2605.22855v1 Announce Type: cross Abstract: Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision makin

Push Your Agent: Measuring and Enforcing Quantitative Goal Persistence in Long-Horizon LLM Agents

arXiv:2605.23574v1 Announce Type: new Abstract: Long-horizon language agents can make many plausible local tool calls yet fail to persist until a requested count is actually complete. We study this ga

R^3L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification

arXiv:2601.03715v2 Announce Type: replace-cross Abstract: Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration

RADAR: Relative Angular Divergence Across Representations

arXiv:2605.23028v1 Announce Type: cross Abstract: Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for doma

researcharxiv-cs-cl

Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks

arXiv:2605.23472v1 Announce Type: new Abstract: Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness

researcharxiv-cs-cv

RMA: an Agentic System for Research-Level Mathematical Problems

arXiv:2605.22875v1 Announce Type: new Abstract: We present extbf{Research Math Agents (RMA)}, an agentic framework for automated reasoning on research-level mathematical problems. Unlike prior studies

RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering

model-releasesarxiv-cs-cv

arXiv:2605.23068v1 Announce Type: new Abstract: Reliable visual understanding in robot-assisted and minimally invasive surgery (RMIS/MIS) demands more than accurate masks: in clinical practice, clinic

Robust Counterfactual Inference in Markov Decision Processes

arXiv:2502.13731v5 Announce Type: replace Abstract: This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a

SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research

arXiv:2605.22878v1 Announce Type: new Abstract: The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmen

Smoothed Elicitation Complexity for Approximate Gamma-calibration of Discrete Classification Tasks

arXiv:2605.23017v1 Announce Type: new Abstract: One prominent method of evaluating machine learning model trustworthiness is the notion of calibration. In the binary outcome setting, a probabilistic p

researcharxiv-cs-lg

SPACENUM: Revisiting Spatial Numerical Understanding in VLMs

arXiv:2605.23898v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly deployed in embodied environments, where they need produce numerical outputs such as action magnitudes an

Sparser Block-Sparse Attention via Token Permutation

arXiv:2510.21270v2 Announce Type: replace-cross Abstract: Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems pr

applicationsarxiv-cs-ai

SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection

arXiv:2605.23306v1 Announce Type: cross Abstract: Active traffic management (ATM) is frequently hindered by traditional macroscopic models and rigid empirical thresholds that fail to capture metastabl

safetyarxiv-cs-lg

SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

arXiv:2605.23440v1 Announce Type: cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a comm

StereoGenBench: A Synthetic Multi-Camera Benchmark for Stereo Generation under Controlled Baseline Regimes

model-releasesarxiv-cs-cv

arXiv:2605.23237v1 Announce Type: new Abstract: Stereo image and video generation, stereo geometry estimation, and condition-controlled view synthesis require paired data in which the variables that d

TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning

arXiv:2601.21692v2 Announce Type: replace Abstract: Fine-Tuning-as-a-Service (FTaaS) facilitates the customization of Multimodal Large Language Models (MLLMs) but introduces critical backdoor risks vi

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems

arXiv:2605.23024v1 Announce Type: new Abstract: Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Fre

applicationsarxiv-cs-ai

Vector Retrieval with Similarity and Diversity: How Hard Is It?

arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search

VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction

arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning,

What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA

arXiv:2605.23067v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Exist