Search: “benchmark”

When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents

arXiv:2608.22339v1 Announce Type: new Abstract: Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents. Prevailing agent self-evolution para

agentsarxiv-cs-cl

25 Aug 2026

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Research

When Persona Simulations Are Informative: Graph-Structured Signals for Pluralistic Opinion Sensing

DGX agent

arXiv:2608.22438v1 Announce Type: new Abstract: Persona-conditioned large language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response v

researcharxiv-cs-ai

25 Aug 2026

When Test-Time Adaptation Helps, Harms, or Becomes Inactive: A Condition-Level Study on CIFAR-10-C

arXiv:2608.22233v1 Announce Type: cross Abstract: Test-time adaptation (TTA) aims to improve model robustness under distribution shift by adapting a source model using unlabeled test data. Although me

25 Aug 2026

Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision

arXiv:2608.23221v1 Announce Type: cross Abstract: Historical retrieval for time-series prediction commonly treats past similarity as a proxy for usefulness. We ask a different question: which historic

researcharxiv-cs-lg

25 Aug 2026

Applications

A Factorial Ablation of a Speech-to-SFT Pipeline: Differential Effects on Data Quality and Downstream Transfer

arXiv:2608.20394v1 Announce Type: cross Abstract: Industry pipelines that turn speech into supervised fine-tuning (SFT) data via multi-stage refinement are increasingly adopted but, to our knowledge,

applicationsarxiv-cs-cl

A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans

arXiv:2608.21140v1 Announce Type: cross Abstract: Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generat

A2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration

arXiv:2608.21099v1 Announce Type: cross Abstract: Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Rece

agentsarxiv-cs-ai

AgentOCR: Reimagining Agent History via Optical Self-Compression

arXiv:2601.04786v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction, b

agentsarxiv-cs-ai

Aggregating Visual Information with Optimal Transport for VideoLM Token Compression

arXiv:2608.20473v1 Announce Type: new Abstract: Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is theref

researcharxiv-cs-cv

AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning

arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sear

Amortized Bandwidth Learning for Kernel Density Estimation under Logarithmic Score

arXiv:2608.20445v1 Announce Type: new Abstract: Kernel density estimation converts finite samples into probability densities, but its performance depends critically on bandwidth selection. Classical s

Anchoring Instruction Outside Mask: Exact Reference Caching for Efficient In-Context Diffusion Transformers

arXiv:2608.21229v1 Announce Type: new Abstract: Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. It

safetyarxiv-cs-cv

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

arXiv:2608.20492v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have become a prevailing paradigm for unified video perception. However, post-training on large multi-task data

Ansari: A Retrieval-Grounded Islamic AI Assistant -- Architecture, Deployment, and Lessons from 140,000 Conversations

arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks

// Applying Anthropic Primitives at Large Enterprises // Frontier models collapsed the cost of writing custom code. But they did not reduce …

// Applying Anthropic Primitives at Large Enterprises // Frontier models collapsed the cost of writing custom code. But they did not reduce the cost of reviewing and maintaining it. Each bespoke solut

model-releasesdair-ai--x

ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding

arXiv:2608.20405v1 Announce Type: new Abstract: Persuasive argument generation requires modeling audience beliefs, rhetorical strategies, and factual grounding. Despite recent advancements, existing m

agentsarxiv-cs-cl

ArtiMo: Agent-Driven Articulated Mesh Animation

arXiv:2608.20699v1 Announce Type: new Abstract: Animating articulated 3D meshes via text requires satisfying strict kinematic constraints, modeling causal interactions between parts, and achieving ins

Asymmetric Capacity Allocation in Self-Refinement Pipelines

arXiv:2608.21345v1 Announce Type: new Abstract: Self-refinement, typically structured as generation, critique, and revision, is a widely adopted paradigm for improving LLM generation and serves as a c

Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning

arXiv:2608.21204v1 Announce Type: cross Abstract: Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a polic

Breaking High Confidence: Practical Face Impersonation under High-Security Thresholds

arXiv:2608.20884v1 Announce Type: new Abstract: Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport i

CARD: Diagnosing Belief to Action Routing Failures in Vision Language Models

arXiv:2608.20763v1 Announce Type: cross Abstract: Linear probes and activation steering have uncovered that vision-language models (VLMs) internally represent mental states such as agents' beliefs, kn

CFM: Language-aligned Concept Foundation Model for Vision

arXiv:2601.13798v3 Announce Type: replace-cross Abstract: Language-aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque,

CLEAR: Continuous Latent Adapter Routing for Utility-Preserving LLM Safety Alignment

arXiv:2608.21278v1 Announce Type: new Abstract: Improving the safety of large language models (LLMs) often comes at the expense of utility, as globally applied safety tuning may affect model responses

Continuous-Time Quantum Walks based Graph Neural Network

arXiv:2608.20738v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used on graph-structured data, but most suffer from two key weaknesses. First, message passing behaves as a low-

Local Ai

Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection

arXiv:2504.11055v3 Announce Type: replace Abstract: Zero-shot anomaly detection and localization aims to learn from source-domain data and generalize to unseen target domains without target-domain sam

local-aiarxiv-cs-cv

CubicSplat: Differentiable Vector Graphics via Error-Bounded Forward Relaxation

arXiv:2608.20803v1 Announce Type: cross Abstract: Vector graphics are prized for their resolution independence, compact storage, and direct editability, making differentiable optimization of their par

researcharxiv-cs-cv

Curriculum-Aware Interpolate-then-Refine: Learned Physiological Time-Series Imputation under Realistic Missingness

arXiv:2608.21207v1 Announce Type: cross Abstract: Imputing physiological time series (arterial blood pressure, blood glucose, etc.) is essential for addressing the missingness that pervades clinical d

Local Ai

DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization

arXiv:2608.21176v1 Announce Type: cross Abstract: Automatic speech quality assessment aims to predict Mean Opinion Scores (MOS) consistent with human subjective perception and is essential for evaluat

local-aiarxiv-cs-ai

Tutorials

Decoupled Vision-Language System for Multimodal Understanding and Generation

arXiv:2608.20382v1 Announce Type: new Abstract: We introduce a new architecture design for multimodal large language models (MLLMs), Libra, capable of both multimodal understanding and generation. Lib

tutorialsarxiv-cs-cl

Deep Models, Shallow Alignment: Uncovering the Granularity Mismatch in Neural Decoding

arXiv:2601.21948v2 Announce Type: replace Abstract: Neural visual decoding is a central problem in brain-computer interface research, aiming to reconstruct human visual perception and to elucidate the

safetyarxiv-cs-cv

Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation

arXiv:2608.20804v1 Announce Type: cross Abstract: Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent diffusion-based approaches imp

researcharxiv-cs-ai

Difficulty-Aware Semantic-ID Optimization for Generative Recommendation

arXiv:2608.20611v1 Announce Type: new Abstract: Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe

DiffVC-ONE: Diffusion-based Generative Video Compression with One-Step Video Diffusion Transformer

arXiv:2608.20515v1 Announce Type: new Abstract: Generative video compression can recover rich visual details at low bitrates, but simultaneously achieving high temporal consistency and low inference c

researcharxiv-cs-cv

Hardware

Enabling Memory-efficient Im2win Convolution with Multi-precision Support on GPU CUDA and Tensor Cores

arXiv:2608.20725v1 Announce Type: cross Abstract: Convolution is a principal computational bottleneck in deep neural networks, and its efficiency depends on tight integration between algorithms and GP

hardwarearxiv-cs-cv

Local Ai

Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challengi

local-aiarxiv-cs-cv

ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation

arXiv:2608.21194v1 Announce Type: new Abstract: Visual prompting (VP) has emerged as a parameter-efficient method for adapting pre-trained models to downstream tasks. However, existing approaches enco

Applications

EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking

arXiv:2608.20886v1 Announce Type: new Abstract: Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to retain, an attribute to modify, and

applicationsarxiv-cs-cv

FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning

arXiv:2608.20518v1 Announce Type: new Abstract: In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and edge devices drop in and

Free-Probability Kernels for Zero-Rollout Hyperparameter Selection in Reservoir Computing

arXiv:2608.20998v1 Announce Type: new Abstract: Reservoir computing (RC) couples a fixed recurrent dynamical system with a trained lightweight readout, but this efficiency is partly lost during hyperp

researcharxiv-cs-lg

From Regulation to Implementation: A Critical Evaluation of LLM-Assisted Regulatory Compliance in Industry

arXiv:2608.21317v1 Announce Type: new Abstract: The European Union (EU) has emerged as a leading regulatory body in the development of sustainability and privacy regulations. While new regulation requ

researcharxiv-cs-ai

Frozen CLIP Priors for Robust Self-Supervised Poisson Inverse Problems

arXiv:2608.20524v1 Announce Type: cross Abstract: Self-supervised learning for imaging inverse problems is increasingly important in photon-limited settings, where acquiring clean ground truth is impr

Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting

arXiv:2608.20761v1 Announce Type: cross Abstract: In non-stationary multivariate time series, different variables and samples often exhibit heterogeneous latent dynamic states, while existing deep for

Geometric Regularization for Long-Tailed Semi-Supervised Learning via Gaussian Feature Bridges

arXiv:2608.20710v1 Announce Type: new Abstract: Real-world semi-supervised learning (SSL) often encounters significant challenges with long-tailed label distributions and noisy pseudo-labels, which hi

safetyarxiv-cs-lg

Harmonic Torsional Diffusion for Protein-Ligand Flexible Docking

arXiv:2608.20366v1 Announce Type: cross Abstract: Molecular docking requires reasoning jointly about ligand pose and protein flexibility. Most diffusion-based docking models predict torsional updates

Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring

arXiv:2602.17751v2 Announce Type: replace-cross Abstract: Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species a

agentsarxiv-cs-ai

Is Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosis

arXiv:2608.20743v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by allowing a lightweight drafter to propose future tokens while a target model verifies them

JuryProbe: An Empirical Consensus-Risk Diagnostic for Routing Reference-Free Factuality Judge Panels to Grounded Verification

arXiv:2608.20607v1 Announce Type: cross Abstract: Panels of inexpensive LLM judges increasingly make accept-or-escalate decisions. In factuality settings, accepting a claim because several reference-f

Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models

arXiv:2608.21247v1 Announce Type: new Abstract: Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-ca