Search: “benchmark”

RouteNLP: Closed-Loop LLM Routing with Conformal Cascading and Distillation Co-Optimization

arXiv:2604.23577v1 Announce Type: new Abstract: Serving diverse NLP workloads with large language models is costly: at one enterprise partner, inference costs exceeded $200K/month despite over 70% of

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Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins

DGX agent

arXiv:2512.15930v2 Announce Type: replace-cross Abstract: Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80% o

model-releasesarxiv-cs-ai

28 Apr 2026

SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling

arXiv:2604.24514v1 Announce Type: new Abstract: Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, a

safetyarxiv-cs-lg

Scheduling Your LLM Reinforcement Learning with Reasoning Trees

arXiv:2510.24832v2 Announce Type: replace Abstract: Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601.03555v2 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

agentsarxiv-cs-ai

SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution

arXiv:2604.24372v1 Announce Type: cross Abstract: LLM-guided evolutionary search has emerged as a promising paradigm for automated algorithm discovery, yet most systems track search progress primarily

agentsarxiv-cs-ai

Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions

arXiv:2604.24347v1 Announce Type: cross Abstract: In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are more readily available than

ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction

arXiv:2604.23813v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabili

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v1 Announce Type: cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models

Skill Retrieval Augmentation for Agentic AI

arXiv:2604.24594v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their

Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt

arXiv:2604.23263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. How

researcharxiv-cs-ai

Tutorials

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs

arXiv:2604.23996v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert

tutorialsarxiv-cs-cv

Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction

arXiv:2604.23551v1 Announce Type: new Abstract: Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatio

Speech Enhancement Based on Drifting Models

arXiv:2604.24199v1 Announce Type: cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Ra

Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization

arXiv:2502.12672v4 Announce Type: replace-cross Abstract: Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization abili

Tutorials

SRL-CLIP: Efficient CLIP Video Adaptation via Structured Semantic Role Labels

arXiv:2401.07669v2 Announce Type: replace Abstract: Adapting CLIP for videos has gained popularity due to its semantic and rich representation. While CLIP is a good starting point, it typically underg

tutorialsarxiv-cs-cv

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

arXiv:2505.16637v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities in machine translation (MT). However, most advanced MT-specifi

Stabilizing Efficient Reasoning with Step-Level Advantage Selection

arXiv:2604.24003v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and ver

Applications

Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing

arXiv:2511.17902v3 Announce Type: replace-cross Abstract: Distributed Fiber Optic Sensing (DFOS) is promising for long-range perimeter security, yet practical deployment faces three key obstacles: sev

applicationsarxiv-cs-ai

StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

arXiv:2604.23198v1 Announce Type: new Abstract: Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see extit{what is happening} but fail to

Strategic Bidding in 6G Spectrum Auctions with Large Language Models

arXiv:2604.24156v1 Announce Type: cross Abstract: Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compe

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

Surface Sensitivity in Lean 4 Autoformalization

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

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

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'

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

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