Search: “evaluation”

Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games

arXiv:2604.16785v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In co

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Agents

Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments

DGX agent

arXiv:2512.23786v2 Announce Type: replace Abstract: Accurate Monocular Depth Estimation (MDE) is critical for autonomous robotic surgery. However, existing self-supervised methods often exhibit a seve

agentsarxiv-cs-cv

21 Apr 2026

Can Large Language Models Understand Context?

model-releasesapple-ml-research

Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the eval

CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China

arXiv:2510.08986v2 Announce Type: replace Abstract: We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives ann

CaTS-Bench: Can Language Models Describe Time Series?

model-releasesarxiv-cs-cv

arXiv:2509.20823v5 Announce Type: replace-cross Abstract: Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation,

CCAR: Intrinsic Robustness as an Emergent Geometric Property

arXiv:2604.16861v1 Announce Type: cross Abstract: Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding repre

safetyarxiv-cs-cv

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation

arXiv:2604.17178v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world appli

Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment

arXiv:2604.17074v1 Announce Type: new Abstract: The rapid advancement of generative models has led to a growing volume of AI-generated videos, making the automatic quality assessment of such videos in

ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship

arXiv:2604.18356v1 Announce Type: new Abstract: Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While r

Compositional Steering of Large Language Models with Steering Tokens

arXiv:2601.05062v2 Announce Type: replace Abstract: Deploying LLMs in real-world applications requires controllable output that satisfies multiple desiderata at the same time. While existing work exte

CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction

arXiv:2604.16742v1 Announce Type: cross Abstract: Scientists have long sought to accurately predict outcomes of real-world events before they happen. Can AI systems do so more reliably? We study this

DART: Learning-Enhanced Model Predictive Control for Dual-Arm Non-Prehensile Manipulation

arXiv:2604.17833v1 Announce Type: new Abstract: What appears effortless to a human waiter remains a major challenge for robots. Manipulating objects nonprehensilely on a tray is inherently difficult,

researcharxiv-cs-ro

Debate as Reward: A Multi-Agent Reward System for Scientific Ideation via RL Post-Training

arXiv:2604.16723v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated potential in automating scientific ideation, yet current approaches relying on iterative prompting or c

safetyarxiv-cs-lg

Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness

arXiv:2603.04881v2 Announce Type: replace Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade perf

safetyarxiv-cs-lg

DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion

arXiv:2604.18257v1 Announce Type: cross Abstract: Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQ

Does Welsh media need a review? Detecting bias in Nation.Cymru's political reporting

arXiv:2604.17628v1 Announce Type: new Abstract: Wales' political landscape has been marked by growing accusations of bias in Welsh media. This paper takes the first computational step toward testing t

Dual-End Consistency Model

arXiv:2602.10764v2 Announce Type: replace Abstract: The slow iterative sampling nature remains a major bottleneck for the practical deployment of diffusion and flow-based generative models. While cons

EasyVideoR1: Easier RL for Video Understanding

model-releasesarxiv-cs-cv

arXiv:2604.16893v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large languag

Edit Fidelity Field: Semantics-Aware Region Isolation for Training-Free Scene Text Editing

arXiv:2604.17500v1 Announce Type: new Abstract: Scene text editing (STE) has achieved remarkable progress in accurately rendering target text through diffusion-based methods. However, we identify a cr

applicationsarxiv-cs-cv

Tutorials

EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

arXiv:2508.17008v2 Announce Type: replace Abstract: Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall exper

tutorialsarxiv-cs-cl

Enhancing Anomaly-Based Intrusion Detection Systems with Process Mining

arXiv:2604.18066v1 Announce Type: cross Abstract: Anomaly-based Intrusion Detection Systems (IDSs) ensure protection against malicious attacks on networked systems. While deep learning-based IDSs achi

Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

arXiv:2604.16757v1 Announce Type: new Abstract: The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and

Tutorials

FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation

arXiv:2604.17625v1 Announce Type: new Abstract: This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-tra

tutorialsarxiv-cs-cv

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

model-releasesarxiv-cs-lg

arXiv:2604.18379v1 Announce Type: new Abstract: Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-base

From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning

arXiv:2601.04278v2 Announce Type: replace Abstract: Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfull

From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?

arXiv:2604.17968v1 Announce Type: cross Abstract: Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a f

From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs

arXiv:2601.03682v2 Announce Type: replace Abstract: Recent studies reveal that large language models (LLMs) exhibit limited logical reasoning abilities in mathematical problem-solving, instead often r

From Inheritance to Saturation: Disentangling the Evolution of Visual Redundancy for Architecture-Aware MLLM Inference Acceleration

model-releasesarxiv-cs-cv

arXiv:2604.16462v1 Announce Type: new Abstract: High-resolution Multimodal Large Language Models (MLLMs) face prohibitive computational costs during inference due to the explosion of visual tokens. Ex

FUSE: Ensembling Verifiers with Zero Labeled Data

arXiv:2604.18547v1 Announce Type: cross Abstract: Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In p

Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease

arXiv:2601.00921v2 Announce Type: replace Abstract: Quantum methods are increasingly proposed for healthcare, but translational biomarker studies demand transparent benchmarking and robust small-datas

applicationsarxiv-cs-lg

Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT

arXiv:2511.16698v2 Announce Type: replace Abstract: SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SN

Local Ai

How Creative Are Large Language Models in Generating Molecules?

arXiv:2604.18031v1 Announce Type: new Abstract: Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes i

local-aiarxiv-cs-cl

How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function

arXiv:2604.17581v1 Announce Type: new Abstract: How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress inc

I find that open weights models over-perform on benchmarks compared to actual real-world usage, and Kimi feels like no exception. For exampl…

model-releasesethan-mollick--x

I find that open weights models over-perform on benchmarks compared to actual real-world usage, and Kimi feels like no exception. For example, a small amount of use will show that Kimi is not as good

Identifying Ethical Biases in Action Recognition Models

arXiv:2604.17971v1 Announce Type: new Abstract: Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has n

safetyarxiv-cs-cv

Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification

arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator

In Search of Lost DNA Sequence Pretraining

arXiv:2604.16570v1 Announce Type: new Abstract: DNA sequence encoding is fundamental to gene function prediction, protein synthesis, and diverse downstream biological tasks. Despite the substantial pr

Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling

arXiv:2604.17001v1 Announce Type: new Abstract: The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of extit{tensor completion with arbitrary sampling} (TCAS),

Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias

arXiv:2601.16934v2 Announce Type: replace Abstract: To be discoverable in an embedding-based search process, each part of a document should be reflected in its embedding representation. To quantify an

Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG

arXiv:2604.16422v1 Announce Type: new Abstract: The injection of domain-specific knowledge is crucial for adapting language models (LMs) to specialized fields such as biomedicine. While most current a

IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language

arXiv:2604.16654v1 Announce Type: new Abstract: Reclaimed slur usage is a common and meaningful practice online for many marginalized communities. It serves as a source of solidarity, identity, and sh

JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew

arXiv:2604.18041v1 Announce Type: new Abstract: Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a s

Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions

arXiv:2601.05414v2 Announce Type: replace Abstract: As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intel

Latent Preference Modeling for Cross-Session Personalized Tool Calling

arXiv:2604.17886v1 Announce Type: new Abstract: Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental cha

Learning Stable Predictors from Weak Supervision under Distribution Shift

model-releasesarxiv-cs-lg

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

arXiv:2604.16379v1 Announce Type: cross Abstract: Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual intera

Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting

arXiv:2512.00198v2 Announce Type: replace Abstract: Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammog

safetyarxiv-cs-cv

Matched-Learning-Rate Analysis of Attention Drift and Transfer Retention in Fine-Tuned CLIP

arXiv:2604.16410v1 Announce Type: new Abstract: CLIP adaptation can improve in-domain accuracy while degrading out-of-domain transfer, but comparisons between Full Fine-Tuning (Full FT) and LoRA are o