Search: “evaluation”

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

arXiv:2608.27142v1 Announce Type: new Abstract: Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formu

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Model Releases

GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions

DGX agent

arXiv:2608.26157v1 Announce Type: new Abstract: Natural-language analytics over enterprise data warehouses is increasingly important, but production use is limited by hallucinated metrics, invalid joi

model-releasesarxiv-cs-ai

28 Aug 2026

Tutorials

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

arXiv:2604.02324v2 Announce Type: replace-cross Abstract: Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in

tutorialsarxiv-cs-ai

HALO: A Heterogeneity-Aware Language-Aligned IMU Foundation Model for Open-Set Human Activity Recognition

arXiv:2608.27233v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using inertial measurement units (IMUs) enables a wide range of applications, yet the field still lacks a unified model

applicationsarxiv-cs-lg

HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence

arXiv:2608.26517v1 Announce Type: new Abstract: Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intell

Hull First, Wake Second: Wake-Reliance Suppression for Robust Maritime Vessel Detection

arXiv:2608.26665v1 Announce Type: new Abstract: Maritime vessel detectors often face scenes where hulls are small, low-contrast, or blurred, while wakes are longer and easier to detect. This creates a

HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement

arXiv:2505.15740v2 Announce Type: replace-cross Abstract: Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their

ITL: Interpretable Document Alignment with Structured Reference Frameworks

arXiv:2608.27031v1 Announce Type: new Abstract: Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and r

safetyarxiv-cs-cl

KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations

arXiv:2608.27365v1 Announce Type: cross Abstract: Physics-integrated 3D Gaussian representations now allow reconstructed deformable objects to be simulated and rendered under explicit material models.

LAAF: A Layered Accountability Architecture Framework for LLM Applications

arXiv:2608.27102v1 Announce Type: new Abstract: Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authorita

Learning a Continuous Sepsis Severity Score Without Hour-by-Hour Supervision: A Two-Site Retrospective Study

arXiv:2608.27421v1 Announce Type: new Abstract: Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a c

Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

arXiv:2608.27077v1 Announce Type: cross Abstract: Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a cent

LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics

arXiv:2608.27395v1 Announce Type: cross Abstract: Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing se

LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?

arXiv:2506.14496v3 Announce Type: replace Abstract: Swarm intelligence describes how simple, decentralized agents can collectively produce complex behaviors. Recently, the concept of swarming has been

agentsarxiv-cs-ai

Lost in Compression: A Controlled Cross-Lingual Audit of Extractive Prompt Compressors

arXiv:2608.26175v1 Announce Type: cross Abstract: Extractive prompt compression promises to cut LLM inference costs by removing low-information tokens, and learned compressors such as LLMLingua-2 repo

applicationsarxiv-cs-ai

Local Ai

Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding

arXiv:2608.26925v1 Announce Type: new Abstract: In many low-resource settings, even just eliciting speech for data collection is difficult. One promising approach has been to ask speakers to describe

local-aiarxiv-cs-cl

Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact

arXiv:2608.27271v1 Announce Type: cross Abstract: The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understan

agentsarxiv-cs-ro

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

arXiv:2608.27429v1 Announce Type: new Abstract: Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through extit{de novo} ge

Local Ai

MeshPriorDiT: Hierarchical Modeling for Action-Conditioned Cloth Dynamics

arXiv:2608.26766v1 Announce Type: new Abstract: Action-conditioned cloth dynamics prediction requires both locally plausible deformation and long-range coordination. Existing approaches largely follow

local-aiarxiv-cs-ro

Methodological and Conceptual Framework for 5D Multi-Table Analysis: A Unified Approach for Complex Data Reuse

arXiv:2608.26149v1 Announce Type: new Abstract: Multi-table learning remains a major challenge in machine learning for healthcare and other complex information systems. Relational data combine several

Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion

arXiv:2608.26879v1 Announce Type: new Abstract: Fusing multiple modalities is expected to improve model performance. However, on the MultiHuSE dataset, early, late, and symmetric attention fusion ofte

MoganColBERT-TR: A Late-Interaction Multi-Vector Retrieval Model for Turkish

model-releasesarxiv-cs-cl

arXiv:2608.26344v1 Announce Type: new Abstract: We previously reported a ModernBERT encoder trained from scratch for Turkish (MoganBERT-TR) and a single-vector embedding model built on top of it (Moga

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

arXiv:2608.26161v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable capabilities in reasoning and decision-making, high-fidelity probabilistic sampling remai

Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

arXiv:2607.15682v2 Announce Type: replace Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability infor

Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs

arXiv:2608.26729v1 Announce Type: new Abstract: In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledg

applicationsarxiv-cs-lg

Nomad: Autonomous Exploration and Discovery

arXiv:2603.29353v3 Announce Type: replace Abstract: We introduce Nomad, a system for autonomous data exploration and insight discovery. Given a corpus of documents, databases, or other data sources, u

agentsarxiv-cs-ai

Omni-Interactive Universal Embedder

arXiv:2608.27044v1 Announce Type: new Abstract: Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their

Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

arXiv:2608.27137v1 Announce Type: cross Abstract: The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred d

PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

arXiv:2608.27206v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation

Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs

arXiv:2608.27178v1 Announce Type: new Abstract: Accurate rectal cancer segmentation from magnetic resonance imaging (MRI) is essential for adaptive radiotherapy and tumor response assessment, but depl

Per-View Gaussian Predictions Enable Training-Free Distractor Filtering in Feed-Forward 3DGS

arXiv:2608.26951v1 Announce Type: cross Abstract: Feed-forward 3D Gaussian Splatting reconstructs an explicit Gaussian representation from multiple input images in one network execution, making 3D rec

Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equations

arXiv:2608.26549v1 Announce Type: cross Abstract: While Physics-Informed Neural Networks (PINNs) have emerged as a transformative paradigm for solving complex differential equations, their reliance on

PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents

arXiv:2608.26530v1 Announce Type: new Abstract: Long-horizon agent runs generate experience that can improve both the current run and future work. Most self-improvement methods process this experience

agentsarxiv-cs-ai

Planning a Shared Modular Fixture Layout Across Robotic Disassembly Stages

arXiv:2608.27151v1 Announce Type: new Abstract: Stable support remains challenging in robotic disassembly of irregularly shaped products. As components are progressively removed, the available support

researcharxiv-cs-ro

Local Ai

Position Is All You Need: A Free Lunch Token Compression Strategy for MLLM-based Referring Expression Segmentation

arXiv:2608.26142v1 Announce Type: cross Abstract: Referring Expression Segmentation (RES) aims to generate pixel-wise segmentation masks from complex and implicit textual queries. While recent advance

local-aiarxiv-cs-ai

Predicting Consequences and Reinforcing Navigation Policies with Latent World Models

arXiv:2608.26190v1 Announce Type: new Abstract: World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primaril

PredVLA: A Sub-Million-Parameter Predictive-Coding Policy for Robot Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.26673v1 Announce Type: new Abstract: Large pretrained vision-language-action models dominate modern robot-manipulation benchmarks, but it remains unclear how much model scale is necessary f

Private and interpretable clinical prediction with quantum-inspired tensor train models

arXiv:2602.06110v2 Announce Type: replace Abstract: Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover i

ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving

arXiv:2608.26334v1 Announce Type: new Abstract: Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fu

Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions

arXiv:2608.27392v1 Announce Type: cross Abstract: Camera-derived remote photoplethysmography (rPPG) is commonly validated through endpoint accuracy, but endpoint performance does not establish whether

Pruning Binarized Neural Networks: A Dedicated Framework and Globally Weighted Algorithms

arXiv:2608.26233v1 Announce Type: new Abstract: Extreme compression of deep neural networks, up to full binarization, dramatically reduces memory footprint and arithmetic complexity, facilitating depl

Puro-2B: Poor Lab's Qwen2-1.5B Trained on RTX 5090 within $5090

model-releasesarxiv-cs-cl

arXiv:2608.27370v1 Announce Type: new Abstract: Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communit

Hardware

Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models

arXiv:2508.06753v3 Announce Type: replace Abstract: The advent of ultra-low-bit LLM models, approaching the perplexity and task accuracy of their full precision counterparts, is ushering in a new era

hardwarearxiv-cs-ai

Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset

arXiv:2601.05063v2 Announce Type: replace-cross Abstract: Magnetic resonance imaging (MRI) is a cornerstone of clinical neuroimaging, yet conventional MRIs provide qualitative information heavily depe

applicationsarxiv-cs-cv

RCMN: Understanding Misleadingness in Influential Public Discourse

arXiv:2608.27358v1 Announce Type: cross Abstract: Influential public discourse shapes public beliefs and can also mislead, not only through what is stated, but also through how information is framed,

Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic

arXiv:2608.26860v1 Announce Type: cross Abstract: Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control i

ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction

arXiv:2608.27154v1 Announce Type: new Abstract: Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this

Reward-Informed Sparse Autoencoders and the Solution-Completeness Confound

model-releasesarxiv-cs-cl

arXiv:2608.26136v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose language-model activations into sparse, interpretable features, and an appealing way to aim them at reasoning is to