Search: “framework”

Talent or Luck? Evaluating Attribution Bias in Large Language Models

arXiv:2505.22910v2 Announce Type: replace Abstract: When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event o

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TDD Governance for Multi-Agent Code Generation via Prompt Engineering

DGX agent

arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipl

agentsarxiv-cs-ai

30 Apr 2026

The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety

arXiv:2603.02259v2 Announce Type: replace-cross Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain esse

safetyarxiv-cs-lg

The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection

arXiv:2512.20340v3 Announce Type: replace Abstract: Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing metho

researcharxiv-cs-cv

The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding

arXiv:2602.03467v2 Announce Type: replace Abstract: Explanations are central to human cognition, yet AI systems often produce outputs that are difficult to understand. While symbolic AI offers a trans

Applications

TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation

arXiv:2604.26247v1 Announce Type: cross Abstract: Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user inte

applicationsarxiv-cs-ai

Local Ai

TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models

arXiv:2604.26553v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language,

local-aiarxiv-cs-ai

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling

arXiv:2510.14703v2 Announce Type: replace Abstract: Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an in

Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning

arXiv:2604.26370v1 Announce Type: new Abstract: Vision-language models have shown strong performance, but they often generalize poorly to specialized domains. While semi-supervised vision-language lea

safetyarxiv-cs-cv

Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models

arXiv:2604.26951v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters f

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

arXiv:2604.26836v1 Announce Type: new Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploratio

safetyarxiv-cs-lg

Uncertainty-Aware Reward Discounting for Mitigating Reward Hacking

arXiv:2604.26360v1 Announce Type: cross Abstract: Reinforcement learning (RL) systems typically optimize scalar reward functions that assume precise and reliable evaluation of outcomes. However, real-

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Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

arXiv:2604.26694v1 Announce Type: cross Abstract: We propose X-WAM, a Unified 4D World Model that unifies real-time robotic action execution and high-fidelity 4D world synthesis (video + 3D reconstruc

Local Ai

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

arXiv:2604.26837v1 Announce Type: new Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing onl

local-aiarxiv-cs-lg

Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks

arXiv:2509.09870v2 Announce Type: replace-cross Abstract: Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such des

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ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection

arXiv:2604.26218v1 Announce Type: new Abstract: Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward vis

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Hardware

Video Compression Meets Video Generation: Latent Inter-Frame Pruning with Attention Recovery

arXiv:2603.05811v2 Announce Type: replace Abstract: Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we addres

hardwarearxiv-cs-cv

VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models

arXiv:2505.22897v2 Announce Type: replace Abstract: While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less atten

VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness

model-releasesarxiv-cs-lg

arXiv:2603.07080v3 Announce Type: replace-cross Abstract: Vision-and-Language Navigation (VLN) increasingly relies on large vision-language models, but their inference cost conflicts with real-time de

Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance

arXiv:2604.26839v1 Announce Type: new Abstract: Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and soc

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When Hidden States Drift: Can KV Caches Rescue Long-Range Speculative Decoding?

arXiv:2604.26412v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the specul

researcharxiv-cs-cl

When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models

model-releasesarxiv-cs-ai

arXiv:2604.26649v1 Announce Type: cross Abstract: Large reasoning models such as DeepSeek-R1 and OpenAI o1 generate extended chains of thought spanning thousands of tokens, yet their integration with

// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model s…

// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model starts reasoning. Large reasoning models like o1 and R1 don't

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Applications

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

arXiv:2604.26174v1 Announce Type: new Abstract: Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environmen

applicationsarxiv-cs-cv

World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning

arXiv:2604.26934v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance on static visual understanding, yet they still struggle with dynamic spatial reasoning that

researcharxiv-cs-cv

A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks

model-releasesarxiv-cs-lg

arXiv:2604.24810v1 Announce Type: new Abstract: Edge computing environments impose strict constraints on energy consumption and latency, making the deployment of deep neural networks a significant cha

Tutorials

A New Kind of Network? Review and Reference Implementation of Neural Cellular Automata

arXiv:2604.24990v1 Announce Type: new Abstract: Stephen Wolfram proclaimed in his 2003 seminal work 'A New Kind Of Science' that simple recursive programs in the form of Cellular Automata (CA) are a p

tutorialsarxiv-cs-cv

A Survey on LLM-based Conversational User Simulation

arXiv:2604.24977v1 Announce Type: new Abstract: User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary

researcharxiv-cs-cl

Adaptable phase retrieval for coherent transition radiation spectroscopy based on differentiable physics information

model-releasesarxiv-cs-lg

arXiv:2604.25489v1 Announce Type: cross Abstract: Coherent transition radiation (CTR) spectroscopy is a critical diagnostic for characterizing the longitudinal structure of relativistic electron bunch

ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models

arXiv:2604.24940v1 Announce Type: new Abstract: Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representa

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

arXiv:2604.25850v1 Announce Type: new Abstract: Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environment

Agents

// Agentic Harness Engineering // Pay attention to this one, AI devs. (bookmark it) Most coding-agent harnesses are still tuned by hand or b…

// Agentic Harness Engineering // Pay attention to this one, AI devs. (bookmark it) Most coding-agent harnesses are still tuned by hand or brittle trial-and-error self-evolution. This new work introdu

agentsdair-ai--x

Analyzing LLM Reasoning to Uncover Mental Health Stigma

arXiv:2604.25053v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly being explored for mental health applications, recent studies reveal that they can exhibit stigma to

asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics

model-releasesarxiv-cs-ro

arXiv:2604.24916v1 Announce Type: new Abstract: We introduce asRoBallet, to the best of our knowledge, the first successful deployment of reinforcement learning (RL) on a humanoid ballbot hardware. Hi

BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks

arXiv:2604.24955v1 Announce Type: new Abstract: As benchmarks grow in complexity, many apparent agent failures are not failures of the agent at all - they are failures of the benchmark itself: broken

Benchmarking Logistic Regression, SVM, and LightGBM Against BiLSTM with Attention for Sentiment Analysis on Indonesian Product Reviews

arXiv:2604.25452v1 Announce Type: new Abstract: Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing a

researcharxiv-cs-cl

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

arXiv:2512.12087v3 Announce Type: replace Abstract: The growing demand for long-context inference capabilities in Large Language Models (LLMs) has intensified the computational and memory bottlenecks

Tutorials

C3G: Learning Compact 3D Representations with 2K Gaussians

arXiv:2512.04021v2 Announce Type: replace Abstract: Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. R

tutorialsarxiv-cs-cv

Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty

arXiv:2604.25241v1 Announce Type: new Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of ad

researcharxiv-cs-lg

CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way

arXiv:2510.09382v2 Announce Type: replace Abstract: Curriculum learning (CL) structures training from simple to complex samples, facilitating progressive learning. However, existing CL approaches for

safetyarxiv-cs-lg

Citation Failure: Definition, Analysis and Efficient Mitigation

arXiv:2510.20303v3 Announce Type: replace Abstract: Citations from LLM-based RAG systems are supposed to simplify response verification. However, this goal is undermined in cases of citation failure,

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.25273v1 Announce Type: new Abstract: Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus

Tutorials

Contrastive Image-Metadata Pre-Training for Materials Transmission Electron Microscopy

arXiv:2604.24909v1 Announce Type: new Abstract: The vast majority of transmission electron microscopy (TEM) data never gets published and ends up on a backup drive until deleted to free up space. Thes

tutorialsarxiv-cs-lg

CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.25376v1 Announce Type: new Abstract: Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data

CoreFlow: Low-Rank Matrix Generative Models

arXiv:2604.24959v1 Announce Type: new Abstract: Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is co

researcharxiv-cs-lg

CRAFT: Grounded Multi-Agent Coordination Under Partial Information

arXiv:2603.25268v2 Announce Type: replace Abstract: We introduce CRAFT, a multi-agent benchmark for evaluating pragmatic communication in large language models under strict partial information. In thi

Curl Descent: Non-Gradient Learning Dynamics with Sign-Diverse Plasticity

arXiv:2510.02765v4 Announce Type: replace Abstract: Gradient-based algorithms are a cornerstone of artificial neural network training, yet it remains unclear whether biological neural networks use sim

researcharxiv-cs-lg

Applications

Deflation-Free Optimal Scoring

arXiv:2604.25664v1 Announce Type: cross Abstract: Sparse Optimal Scoring (SOS) reformulates linear discriminant analysis to enable feature selection through elastic net regularization, making it well-

applicationsarxiv-cs-lg