Search: “benchmark”

StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval

arXiv:2601.20597v2 Announce Type: replace Abstract: Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic

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Surface Sensitivity in Lean 4 Autoformalization

DGX agent

arXiv:2604.23135v1 Announce Type: new Abstract: Natural-language variation poses a key challenge in Lean autoformalization: semantically equivalent paraphrases of the same theorem statements can induc

researcharxiv-cs-lg

28 Apr 2026

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

arXiv:2601.16746v3 Announce Type: replace-cross Abstract: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts,

agentsarxiv-cs-cl

Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers

arXiv:2603.26380v2 Announce Type: replace Abstract: The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales

SycoPhantasy: Quantifying Sycophancy and Hallucination in Small Open Weight VLMs for Vision-Language Scoring of Fantasy Characters

arXiv:2604.24346v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

arXiv:2509.25346v2 Announce Type: replace Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic dis

Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling

arXiv:2604.23586v1 Announce Type: cross Abstract: Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existi

researcharxiv-cs-cl

Tutorials

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning

arXiv:2604.23623v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit st

tutorialsarxiv-cs-ai

TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agents

arXiv:2604.24005v1 Announce Type: cross Abstract: On-policy distillation (OPD) has shown strong potential for transferring reasoning ability from frontier or domain-specific models to smaller students

safetyarxiv-cs-ai

TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data

arXiv:2604.23368v1 Announce Type: new Abstract: Event-Based Models (EBMs) infer biomarker progression from cross-sectional data but typically only as ordinal sequences and rely on rigid model assumpti

researcharxiv-cs-lg

TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction

arXiv:2604.22880v1 Announce Type: new Abstract: Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientif

TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering

arXiv:2604.24459v1 Announce Type: new Abstract: Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- esp

The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers

arXiv:2604.24155v1 Announce Type: cross Abstract: The quest to align machine behavior with human values raises fundamental questions about the moral frameworks that should govern AI decision-making. M

The Consensus Trap: Dissecting Subjectivity and the 'Ground Truth' Illusion in Data Annotation

arXiv:2602.11318v3 Announce Type: replace Abstract: In machine learning, 'ground truth' refers to the assumed correct labels used to train and evaluate models. However, the foundational 'ground truth'

safetyarxiv-cs-ai

The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability

arXiv:2604.24083v1 Announce Type: new Abstract: This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium

The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation

arXiv:2604.23750v1 Announce Type: cross Abstract: Hypernetwork-based methods such as Doc-to-LoRA internalize a document into an LLM's weights in a single forward pass, but they fail systematically on

The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications

arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode

The Security Cost of Intelligence: AI Capability, Cyber Risk, and Deployment Paradox

arXiv:2604.23058v1 Announce Type: cross Abstract: Firms are deploying more capable AI systems, but organizational controls often have not kept pace. These systems can generate greater productivity gai

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage

arXiv:2508.09603v2 Announce Type: replace Abstract: Membership inference attacks serves as useful tool for fair use of language models, such as detecting potential copyright infringement and auditing

Think Anywhere in Code Generation

arXiv:2603.29957v3 Announce Type: replace-cross Abstract: Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answe

agentsarxiv-cs-lg

Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

arXiv:2511.08577v2 Announce Type: replace-cross Abstract: Improving reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications.

Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate

arXiv:2604.23605v1 Announce Type: new Abstract: The application of large language models (LLMs) in clinical decision support faces significant challenges of 'tunnel vision' and diagnostic hallucinatio

agentsarxiv-cs-ai

This is an incredibly cool experiment It is also fascinating that the model knows information up to 1931, but, at least in some science topi…

applicationsethan-mollick--x

This is an incredibly cool experiment It is also fascinating that the model knows information up to 1931, but, at least in some science topics, seems very stuck in the early 1900s. For example, it def

TOL: Textual Localization with OpenStreetMap

arXiv:2604.01644v2 Announce Type: replace Abstract: Natural language provides an intuitive way to express spatial intent in geospatial applications. While existing localization methods often rely on d

TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations

arXiv:2604.24119v1 Announce Type: new Abstract: Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a

Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning

arXiv:2604.23231v1 Announce Type: cross Abstract: Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shar

Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis

arXiv:2509.26158v2 Announce Type: replace-cross Abstract: The performance of deep neural networks is strongly influenced by the quality of their training data. However, mitigating dataset bias by manu

Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey

arXiv:2505.15957v4 Announce Type: replace-cross Abstract: With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models

safetyarxiv-cs-ai

TRINITY: An Evolved LLM Coordinator

arXiv:2512.04695v3 Announce Type: replace Abstract: Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. Trinity addresses this

researcharxiv-cs-lg

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation

arXiv:2604.24763v1 Announce Type: new Abstract: Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creatin

researcharxiv-cs-cv

Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models

arXiv:2604.23460v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable,

Unconstrained Multi-view Human Pose Estimation with Algebraic Priors

arXiv:2604.24312v1 Announce Type: cross Abstract: Recovering 3D human pose from multi-view imagery typically relies on precise camera calibration, which is often unavailable in real-world scenarios, t

applicationsarxiv-cs-ai

UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems

arXiv:2604.23362v1 Announce Type: cross Abstract: Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has demonstrated t

safetyarxiv-cs-lg

Local Ai

Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization

arXiv:2604.22846v1 Announce Type: new Abstract: The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical t

local-aiarxiv-cs-cv

Local Ai

Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set

arXiv:2502.03669v3 Announce Type: replace-cross Abstract: AI methods, such as generative models and reinforcement learning, have recently been applied to combinatorial optimization (CO) problems, espe

local-aiarxiv-cs-ai

V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models

arXiv:2509.14837v2 Announce Type: replace Abstract: Recent advances in causal interpretability have extended from language models to vision-language models (VLMs), seeking to reveal their internal mec

VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models

arXiv:2510.08618v2 Announce Type: replace-cross Abstract: Omni-modal large language models (OLLMs) offer a promising end-to-end solution for slide-enhanced speech recognition due to their inherent mul

Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms

arXiv:2604.23775v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety challenges, s

safetyarxiv-cs-ro

VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation

arXiv:2604.22942v1 Announce Type: cross Abstract: Diffusion models produce high-quality synthetic data but suffer from slow inference. We propose 3D Variable-Step Denoising Diffusion Probabilistic Mod

researcharxiv-cs-ai

When Annotators Agree but Labels Disagree: The Projection Problem in Stance Detection

arXiv:2603.24231v2 Announce Type: replace Abstract: Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis

safetyarxiv-cs-cl

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models

arXiv:2510.00626v3 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored.

applicationsarxiv-cs-cl

WISE-FM:Operation-Aware, Engineering-Informed Foundation Model for Multi-Task Well Design

model-releasesarxiv-cs-lg

arXiv:2604.23767v1 Announce Type: new Abstract: Deploying machine learning models across diverse well portfolios requires generalisation to wells with design parameters outside the training distributi

Local Ai

3DAlign-DAER: Dynamic Attention Policy and Efficient Retrieval Strategy for Fine-grained 3D-Text Alignment at Scale

arXiv:2511.13211v2 Announce Type: replace Abstract: Despite recent advancements in 3D-text cross-modal alignment, existing state-of-the-art methods still struggle to align fine-grained textual semanti

local-aiarxiv-cs-cv

A Probabilistic Framework for Hierarchical Goal Recognition

arXiv:2604.22256v1 Announce Type: cross Abstract: Goal recognition aims to infer an agent's goal from observations of its behaviour. In realistic settings, recognition can benefit from exploiting hier

agentsarxiv-cs-ai

AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection

arXiv:2602.11931v2 Announce Type: replace-cross Abstract: Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability by repeatedly invoking large la

agentsarxiv-cs-ai

Adapting MLLMs for Nuanced Video Retrieval

arXiv:2512.13511v2 Announce Type: replace Abstract: Our objective is to build an embedding model that captures the nuanced relationship between a search query and candidate videos. We cover three aspe

researcharxiv-cs-cv

Algorithmic Compliance and Regulatory Loss in Digital Assets

arXiv:2603.04328v2 Announce Type: replace Abstract: We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward

applicationsarxiv-cs-lg

Aligning Dense Retrievers with LLM Utility via DistillationAligning Dense Retrievers with LLM Utility via Distillation

arXiv:2604.22722v1 Announce Type: cross Abstract: Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations