Search: “reasoning”

Large Language Model based Interactive Decision-Making for Autonomous Driving

arXiv:2604.23513v1 Announce Type: new Abstract: In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly cons

safetyarxiv-cs-ro

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Safety

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support

DGX agent

arXiv:2604.23902v1 Announce Type: new Abstract: Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to ad

safetyarxiv-cs-ai

28 Apr 2026

LOCAL AI MODELS ARE CATCHING UP TO FRONTIER MODELS WAY FASTER THAN ANYONE EXPECTED this guy ran qwen 3.6 27B locally on a base macbook pro M…

model-releasesclem-delangue--x

LOCAL AI MODELS ARE CATCHING UP TO FRONTIER MODELS WAY FASTER THAN ANYONE EXPECTED this guy ran qwen 3.6 27B locally on a base macbook pro M4 with 24GB of memory quantized and stripped of safety guard

MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining

arXiv:2604.24374v1 Announce Type: new Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Repr

safetyarxiv-cs-cl

One Identity, Many Roles: Multimodal Entity Coreference for Enhanced Video Situation Recognition

arXiv:2604.23173v1 Announce Type: new Abstract: Video Situation Recognition (VidSitu) addresses the challenging problem of 'who did what to whom, with what, how, and where' in a video. It tests thorou

researcharxiv-cs-cv

Applications

One Size Fits None: Heuristic Collapse in LLM Investment Advice

arXiv:2604.23837v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors in high-stakes domains -- answering medical questions, interpreting legal documents, recomme

applicationsarxiv-cs-cl

Perfecting Aircraft Maneuvers with Reinforcement Learning

arXiv:2604.24338v1 Announce Type: new Abstract: This paper evaluates an advanced jet trainer's utilization of artificial intelligence (AI)-based aircraft aerobatic maneuvers with the intention of deve

researcharxiv-cs-lg

Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs

arXiv:2604.04177v2 Announce Type: replace Abstract: As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which

safetyarxiv-cs-cl

Process orchestration has become the critical mandate for enterprise AI

Enterprise AI orchestration has become the defining challenge of the agentic era — not because the models aren’t ready, but because most enterprises aren’t. The whole software stack is being reimagine

agentssiliconangle

Local Ai

RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering

arXiv:2604.22755v1 Announce Type: cross Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remai

local-aiarxiv-cs-ai

Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

arXiv:2604.23842v1 Announce Type: cross Abstract: Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in c

Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs

arXiv:2509.25414v2 Announce Type: replace-cross Abstract: Large language models are often adapted using parameter-efficient techniques such as Low-Rank Adaptation (LoRA), formulated as y = W_0x + BAx,

Hardware

RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference

arXiv:2505.02922v3 Announce Type: replace Abstract: Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and band

hardwarearxiv-cs-lg

Safe Navigation in Unknown and Cluttered Environments via Direction-Aware Convex Free-Region Generation

arXiv:2604.23648v1 Announce Type: new Abstract: Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered

safetyarxiv-cs-ro

Scaling Coding Agents via Atomic Skills

arXiv:2604.05013v2 Announce Type: replace-cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to task-specific overfitting

researcharxiv-cs-ai

Applications

See No Evil: Semantic Context-Aware Privacy Risk Detection for AR

arXiv:2604.22805v1 Announce Type: cross Abstract: Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic

applicationsarxiv-cs-ai

Should we care about AI happiness? In our new research, we find evidence of functional AI wellbeing across several independent measures. We …

tutorialsdan-hendrycks--x

Should we care about AI happiness? In our new research, we find evidence of functional AI wellbeing across several independent measures. We find which AI models are happiest, how to make them happier,

SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing

arXiv:2604.23392v1 Announce Type: new Abstract: Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widel

SPEAR-1: Scaling Beyond Robot Demonstrations via 3D Understanding

arXiv:2511.17411v2 Announce Type: replace-cross Abstract: Robotic Foundation Models (RFMs) hold great promise as generalist, end-to-end systems for robot control. Yet their ability to generalize acros

researcharxiv-cs-lg

Structure Guided Retrieval-Augmented Generation for Factual Queries

arXiv:2604.22843v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be fact

The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models

arXiv:2604.24698v1 Announce Type: new Abstract: Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive

agentsarxiv-cs-cl

The Pragmatic Persona: Discovering LLM Persona through Bridging Inference

arXiv:2604.24079v1 Announce Type: cross Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on su

Towards Automated Ontology Generation from Unstructured Text: A Multi-Agent LLM Approach

arXiv:2604.23090v1 Announce Type: new Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language

agentsarxiv-cs-ai

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

Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

arXiv:2603.25158v4 Announce Type: replace Abstract: Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a seve

Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines

arXiv:2604.23001v1 Announce Type: cross Abstract: Despite remarkable progress in Vision--Language--Action (VLA) models, a central bottleneck remains underexamined: the data infrastructure that underli

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

When AI reviews science: Can we trust the referee?

arXiv:2604.23593v1 Announce Type: new Abstract: The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the s

XGRAG: A Graph-Native Framework for Explaining KG-based Retrieval-Augmented Generation

arXiv:2604.24623v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a str

xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models

arXiv:2509.13021v2 Announce Type: replace-cross Abstract: This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-dr

Adaptive Head Budgeting for Efficient Multi-Head Attention

arXiv:2604.22583v1 Announce Type: new Abstract: Transformers have become the dominant architecture across a wide range of domains, largely due to the effectiveness of multi-head attention in capturing

researcharxiv-cs-lg

Agentic AI gives CPG brands a real-time edge on marketing spend and product testing

Consumer packaged goods companies face mounting pressure to grow profitably while operating on razor-thin margins — and agentic AI is emerging as the tool that can close the gap between fragmented dat

agentssiliconangle

Agentic Inequality

arXiv:2510.16853v3 Announce Type: replace-cross Abstract: Autonomous AI agents capable of complex planning and action mark a shift beyond today's generative tools. As these systems enter political and

agentsarxiv-cs-ai

Local Ai

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a centra

local-aiarxiv-cs-ai

Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning

arXiv:2604.22191v1 Announce Type: cross Abstract: In agentic workflows, LLMs frequently process retrieved contexts that are legally protected from further training. However, auditors currently lack a

agentsarxiv-cs-cl

CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation

arXiv:2604.22274v1 Announce Type: new Abstract: Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible and fine-grained relation phrases beyond a fixed predicate voc

researcharxiv-cs-cv

Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines

arXiv:2604.22661v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling

researcharxiv-cs-cl

Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions

arXiv:2512.20831v2 Announce Type: replace Abstract: Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisio

Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners

arXiv:2604.22542v1 Announce Type: cross Abstract: Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. T

Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer

arXiv:2601.10386v2 Announce Type: replace-cross Abstract: Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Mu

Healthcare AI is converging tech and marketing — and rewriting the CMO role in the process

AI-native healthcare has arrived, with the convergence of cloud infrastructure and intelligent data rewriting how insurance companies engage members, run campaigns and measure business outcomes. The r

agentssiliconangle

How attention simplifies mental representations for planning

arXiv:2506.09520v2 Announce Type: replace-cross Abstract: Human planning is efficient--it frugally deploys limited cognitive resources to accomplish difficult tasks--and flexible--adapting to novel pr

researcharxiv-cs-ai

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

arXiv:2511.18903v2 Announce Type: replace-cross Abstract: Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even

How Many Visual Levers Drive Urban Perception? Interventional Counterfactuals via Multiple Localised Edits

arXiv:2604.22103v1 Announce Type: cross Abstract: Street-view perception models predict subjective attributes such as safety at scale, but remain correlational: they do not identify which localized vi

safetyarxiv-cs-cv

Local Ai

How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications

arXiv:2604.22679v1 Announce Type: cross Abstract: The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses includin

local-aiarxiv-cs-ai

LLM+Graph@VLDB'2025 Workshop Summary

arXiv:2604.02861v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing s

researcharxiv-cs-ai

Hardware

Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches

arXiv:2509.22166v4 Announce Type: replace-cross Abstract: The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N

hardwarearxiv-cs-ai

Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

arXiv:2604.22239v1 Announce Type: cross Abstract: This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark fo