Search: “context”

Material-Agnostic Zero-Shot Thermal Inference for Metal Additive Manufacturing via a Parametric PINN Framework

arXiv:2604.14562v1 Announce Type: new Abstract: Accurate thermal modeling in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship. While prio

applicationsarxiv-cs-lg

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Model-Based Reinforcement Learning Exploits Passive Body Dynamics for High-Performance Biped Robot Locomotion

DGX agent

arXiv:2604.14565v1 Announce Type: new Abstract: Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate

researcharxiv-cs-ro

17 Apr 2026

Hardware

Nautilus: An Auto-Scheduling Tensor Compiler for Efficient Tiled GPU Kernels

arXiv:2604.14825v1 Announce Type: cross Abstract: We present Nautilus, a novel tensor compiler that moves toward fully automated math-to-kernel optimization. Nautilus compiles a high-level algebraic s

hardwarearxiv-cs-lg

Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation

arXiv:2604.14953v1 Announce Type: new Abstract: Gesture recognition research, unlike NLP, continues to face acute data scarcity, with progress constrained by the need for costly human recordings or im

Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options

arXiv:2604.14634v1 Announce Type: new Abstract: Multiple choice evaluation is widely used for benchmarking large language models, yet near ceiling accuracy in low option settings can be sustained by s

safetyarxiv-cs-cl

Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving

arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder

researcharxiv-cs-ai

Text2Arch: A Dataset for Generating Scientific Architecture Diagrams from Natural Language Descriptions

arXiv:2604.14941v1 Announce Type: new Abstract: Communicating complex system designs or scientific processes through text alone is inefficient and prone to ambiguity. A system that automatically gener

The Acoustic Camouflage Phenomenon: Re-evaluating Speech Features for Financial Risk Prediction

arXiv:2604.14619v1 Announce Type: cross Abstract: In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attem

Model Releases

Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation

model-releasesarxiv-cs-cv

arXiv:2604.15301v1 Announce Type: new Abstract: Many SLT systems quietly assume that brief chunks of signing map directly to spoken-language words. That assumption breaks down because signers often cr

Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement

arXiv:2604.14749v1 Announce Type: new Abstract: Large language models still struggle with faithfulness and hallucinations despite their remarkable reasoning abilities. In Knowledge Graph Question Answ

Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception

arXiv:2510.23853v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlook

safetyarxiv-cs-cl

A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings

arXiv:2604.13367v1 Announce Type: new Abstract: Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images could

A Multimodal Clinically Informed Coarse-to-Fine Framework for Longitudinal CT Registration in Proton Therapy

arXiv:2604.13397v1 Announce Type: new Abstract: Proton therapy offers superior organ-at-risk sparing but is highly sensitive to anatomical changes, making accurate deformable image registration (DIR)

Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning

arXiv:2604.13133v1 Announce Type: new Abstract: Thermodynamic cycles are pivotal in determining the efficacy of energy conversion systems. Traditional design methodologies, which rely on expert knowle

Bi-Predictability: A Real-Time Signal for Monitoring LLM Interaction Integrity

arXiv:2604.13061v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes autonomous and interactive workflows, where reliability demands continuous, multi-

agentsarxiv-cs-cl

Bridging Compositional and Distributional Semantics: A Survey on Latent Semantic Geometry via AutoEncoder

arXiv:2506.20083v4 Announce Type: replace Abstract: Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, com

Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference

arXiv:2604.13634v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by letting draft tokens bypass full verification, but conventional frameworks suffer from fre

CLIP Architecture for Abdominal CT Image-Text Alignment and Zero-Shot Learning: Investigating Batch Composition and Data Scaling

arXiv:2604.13561v1 Announce Type: new Abstract: Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the eff

Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation

arXiv:2604.13761v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost an

Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare

arXiv:2603.00192v2 Announce Type: replace Abstract: In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk est

applicationsarxiv-cs-lg

DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off

arXiv:2604.13902v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the reasoning capabilities of Large Language Models (LLMs).

safetyarxiv-cs-lg

Enhanced Text-to-Image Generation by Fine-grained Multimodal Reasoning

arXiv:2604.13491v1 Announce Type: new Abstract: With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced

Hardware

Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel

arXiv:2604.13327v1 Announce Type: cross Abstract: Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter

hardwarearxiv-cs-lg

Model Releases

ExpSeek: Self-Triggered Experience Seeking for Web Agents

model-releasesarxiv-cs-cl

arXiv:2601.08605v2 Announce Type: replace Abstract: Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insi

FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction

arXiv:2604.13453v1 Announce Type: new Abstract: Traffic forecasting requires modeling complex temporal dynamics and long-range spatial dependencies over large sensor networks. Existing methods typical

From Pixels to Nucleotides: End-to-End Token-Based Video Compression for DNA Storage

arXiv:2604.13667v1 Announce Type: new Abstract: DNA-based storage has emerged as a promising approach to the global data crisis, offering molecular-scale density and millennial-scale stability at low

Hierarchical Reinforcement Learning with Augmented Step-Level Transitions for LLM Agents

arXiv:2604.05808v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision-making tasks. However, existing LLM ag

Tutorials

Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies

arXiv:2604.14111v1 Announce Type: new Abstract: Large Language Models (LLMs) are now capable of generating highly fluent, human-like text. They enable many applications, but also raise concerns such a

tutorialsarxiv-cs-cl

Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensembling

arXiv:2604.13386v1 Announce Type: new Abstract: Linear probes can detect when language models produce outputs they 'know' are wrong, a capability relevant to both deception and reward hacking. However

Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning

arXiv:2601.02902v2 Announce Type: replace-cross Abstract: Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decis

Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin

arXiv:2604.13062v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for sym

Med-CAM: Minimal Evidence for Explaining Medical Decision Making

arXiv:2604.13695v1 Announce Type: new Abstract: Reliable and interpretable decision-making is essential in medical imaging, where diagnostic outcomes directly influence patient care. Despite advances

Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery

arXiv:2604.13235v1 Announce Type: new Abstract: Digital elevation modeling of planetary surfaces is essential for studying past and ongoing geological processes. Wide-angle imagery acquired during spa

Neural architectures for resolving references in program code

arXiv:2604.14073v1 Announce Type: new Abstract: Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewritin

applicationsarxiv-cs-lg

OmniTrace: A Unified Framework for Generation-Time Attribution in Omni-Modal LLMs

arXiv:2604.13073v1 Announce Type: new Abstract: Modern multimodal large language models (MLLMs) generate fluent responses from interleaved text, image, audio, and video inputs. However, identifying wh

Local Ai

One Token per Highly Selective Frame: Towards Extreme Compression for Long Video Understanding

arXiv:2604.14149v1 Announce Type: new Abstract: Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame ty

local-aiarxiv-cs-cv

PAT-VCM: Plug-and-Play Auxiliary Tokens for Video Coding for Machines

arXiv:2604.13294v1 Announce Type: new Abstract: Existing video coding for machines is often trained for a specific downstream task and model. As a result, the compressed representation becomes tightly

Model Releases

PersonaVLM: Long-Term Personalized Multimodal LLMs

model-releasesarxiv-cs-cl

arXiv:2604.13074v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual pr

pi-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data

arXiv:2604.14054v1 Announce Type: cross Abstract: Deep search agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due t

agentsarxiv-cs-cl

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro

agentsarxiv-cs-cl

Rethinking Uncertainty in Segmentation: From Estimation to Decision

arXiv:2604.13262v1 Announce Type: new Abstract: In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions. We study the missing step: how uncertainty m

Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift

arXiv:2604.13326v1 Announce Type: new Abstract: The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A

Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model

arXiv:2510.18165v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) are emerging as a powerful and promising alternative to the dominant autoregressive paradigm, offering inhere

Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments

arXiv:2502.00414v2 Announce Type: replace Abstract: In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stance

Training-Free Semantic Multi-Object Tracking with Vision-Language Models

arXiv:2604.14074v1 Announce Type: new Abstract: Semantic Multi-Object Tracking (SMOT) extends multi-object tracking with semantic outputs such as video summaries, instance-level captions, and interact

UNBOX: Unveiling Black-box visual models with Natural-language

arXiv:2603.08639v2 Announce Type: replace Abstract: Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet moder

VibeFlow: Versatile Video Chroma-Lux Editing through Self-Supervised Learning

arXiv:2604.13425v1 Announce Type: new Abstract: Video chroma-lux editing, which aims to modify illumination and color while preserving structural and temporal fidelity, remains a significant challenge

Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap

arXiv:2604.13654v1 Announce Type: new Abstract: Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on ena

agentsarxiv-cs-ro