Search: “benchmark”

HKJudge: A Legal Discourse-Annotated Corpus for Interpreting What Courts Find, How They Reason, and What They Rule

arXiv:2606.06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing l

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Research

HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG

DGX agent

arXiv:2606.07218v1 Announce Type: cross Abstract: Multi-hop RAG poses a data-engineering problem beyond passage matching: under fixed retrieval budgets, a system must organize retrieved text into evid

researcharxiv-cs-cl

8 Jun 2026

Korean Culture into LLM Alignment: Toward Cultural Coherence

arXiv:2606.06797v1 Announce Type: new Abstract: Cultural-aspect work on large language models is dominated by a negative target: which outputs to suppress. We argue that a constructive counterpart is

safetyarxiv-cs-cl

LARA: Latent Action Representation Alignment for Vision-Language-Action Models

arXiv:2606.07100v1 Announce Type: new Abstract: Visual-language action (VLA) models enable robots to predict actions directly from observations and language instructions, but their performance depends

safetyarxiv-cs-cv

Local Ai

Learning Explicit Behavioral Models with Adaptive Questions and World-Model Probes

arXiv:2606.07127v1 Announce Type: new Abstract: Interactive agents trained only against task return can achieve high scores while failing to represent the mechanisms that make their actions succeed. T

local-aiarxiv-cs-lg

Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings

model-releasesarxiv-cs-cl

arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP

Tutorials

Lighting-Aware Representation Learning under Controllable Lighting Variation

arXiv:2606.06899v1 Announce Type: new Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and wi

tutorialsarxiv-cs-cv

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606.06864v1 Announce Type: new Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level pre

applicationsarxiv-cs-cv

MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models

arXiv:2510.11014v2 Announce Type: replace-cross Abstract: Autonomous robots often view rooms only partially, through a doorway, where the walls and scene structure hide the geometry and task-relevant

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

arXiv:2510.05363v2 Announce Type: replace Abstract: Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive. While prior work has demonstrated

researcharxiv-cs-ai

Mind the Gap: Disentangling Performance Bottlenecks in Video Instance Segmentation

arXiv:2606.07394v1 Announce Type: new Abstract: In Video Instance Segmentation (VIS), classification, segmentation, and tracking objectives are jointly evaluated, but their individual contributions to

researcharxiv-cs-cv

Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks

arXiv:2606.06861v1 Announce Type: cross Abstract: Understanding nonlinear feature interactions is crucial in science and engineering, yet standard multilayer perceptrons (MLPs) often capture such inte

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

arXiv:2606.06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding. While current VLM

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

model-releasesarxiv-cs-lg

arXiv:2505.10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.g., DPO, IPO) has greatly improved alignment, yet these approaches assume a sing

Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints

arXiv:2606.06877v1 Announce Type: cross Abstract: Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constr

safetyarxiv-cs-ai

OffQ: Taming Structured Outliers in LLM Quantization by Offsetting

arXiv:2606.07116v1 Announce Type: cross Abstract: Low-bit quantization has been widely adopted to accelerate the inference of large language models (LLMs) by significantly reducing computational cost

researcharxiv-cs-ai

On the Effect of Neural Field Reparameterization for 4DVAR

model-releasesarxiv-cs-lg

arXiv:2509.21751v2 Announce Type: replace Abstract: Four-dimensional variational data assimilation (4DVAR) is a cornerstone of numerical weather prediction, yet it remains computationally intensive an

Agents

OpenSkill: Open-World Self-Evolution for LLM Agents

arXiv:2606.06741v1 Announce Type: new Abstract: Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful tra

agentsarxiv-cs-ai

OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models

arXiv:2606.07180v1 Announce Type: new Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine lea

Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images

arXiv:2606.06847v1 Announce Type: cross Abstract: Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability. In SAR

Probabilistic Gaussian Homotopy: A Probability-Space Continuation Framework for Nonconvex Optimization

arXiv:2603.13546v2 Announce Type: replace Abstract: We introduce Probabilistic Gaussian Homotopy (PGH), a probability-space continuation framework for nonconvex optimization. Unlike classical Gaussian

researcharxiv-cs-lg

Product units in gated recurrent units improve nuclear-mass prediction

model-releasesarxiv-cs-lg

arXiv:2606.06866v1 Announce Type: new Abstract: The prediction of masses of atomic nuclei using machine learning can complement theoretical models and advance the exploration of poorly known domains o

Tutorials

Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

arXiv:2606.06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challeng

tutorialsarxiv-cs-lg

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI

arXiv:2510.17004v2 Announce Type: replace-cross Abstract: Purpose: To develop and evaluate a multi-agent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline

Rethinking Genomic Modeling Through Optical Character Recognition

arXiv:2602.02014v2 Announce Type: replace-cross Abstract: Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However,

researcharxiv-cs-ai

RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking

arXiv:2606.07181v1 Announce Type: cross Abstract: Single-step retrosynthesis needs both accurate first-ranked suggestions and candidate lists that are rich enough for downstream selection. We study th

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

arXiv:2604.01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across su

applicationsarxiv-cs-lg

Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs

arXiv:2606.07175v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensitiv

SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices

model-releasesarxiv-cs-cl

arXiv:2606.07098v1 Announce Type: new Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices S to aid truncated Singular Value Decomposition (SVD) based Large Language Model

Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping

arXiv:2606.07389v1 Announce Type: new Abstract: Biosignals-free shared-autonomy control of upper-limb prosthetic hands aims to enable natural and low-effort manipulation without relying on EMG or othe

safetyarxiv-cs-ro

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606.06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers. We reveal the wide existence

researcharxiv-cs-lg

SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating

arXiv:2606.07074v1 Announce Type: cross Abstract: Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cos

safetyarxiv-cs-ai

Agents

Small Language Model Agents Enable Efficient and High-Quality Knowledge Mining

arXiv:2510.01427v3 Announce Type: replace Abstract: At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user i

agentsarxiv-cs-ai

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

arXiv:2606.07500v1 Announce Type: cross Abstract: Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to ca

Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition

arXiv:2606.07355v1 Announce Type: new Abstract: Micro-gesture online recognition aims to temporally localize and classify subtle gestures in untrimmed videos. Owing to their extremely short duration,

SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models

arXiv:2606.06907v1 Announce Type: cross Abstract: Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data. However, the scarcity of high-quali

applicationsarxiv-cs-ai

Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning

arXiv:2509.05316v2 Announce Type: replace-cross Abstract: A conventional LLM Unlearning setting consists of two subsets -'forget' and 'retain', with the objectives of removing the undesired knowledge

applicationsarxiv-cs-ai

Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors

arXiv:2606.06891v1 Announce Type: new Abstract: Despite advances in 3D scene understanding, existing 3D Large Multimodal Models operate in offline settings, requiring complete scene observations or pr

Test-Time Trajectory Optimization for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.07170v1 Announce Type: new Abstract: End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate.

The Fine-Tuning Trap: Evaluating Negative Transfer and the Role of PEFT in Sub-1B Mathematical Reasoning

arXiv:2606.06920v1 Announce Type: cross Abstract: Deploying Small Language Models (SLMs) on edge devices requires efficient fine-tuning strategies that adapt models to new tasks without degrading thei

Local Ai

The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs

arXiv:2606.07422v1 Announce Type: cross Abstract: Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural kn

local-aiarxiv-cs-ai

Agents

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective

arXiv:2606.07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical cont

agentsarxiv-cs-ai

Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models

arXiv:2606.07157v1 Announce Type: new Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficien

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

arXiv:2606.06915v1 Announce Type: cross Abstract: Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute

Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

arXiv:2606.07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of st

applicationsarxiv-cs-lg

Hardware

TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection

arXiv:2606.06742v1 Announce Type: new Abstract: TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, wi

hardwarearxiv-cs-lg

TraRA: Trajectory-level Recognition Aggregation for Video Text Spotting in Urban Surveillance

arXiv:2606.07161v1 Announce Type: new Abstract: Video Text Spotting (VTS) is essential for urban surveillance and intelligent transportation systems, enabling automated reading of street signs, vehicl

researcharxiv-cs-cv

Tutorials

Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors

arXiv:2606.07291v1 Announce Type: new Abstract: Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspo

tutorialsarxiv-cs-lg