Search: “reasoning”

Latent State Design for World Models under Sufficiency Constraints

arXiv:2605.01694v1 Announce Type: new Abstract: A world model matters to an agent only through the state it constructs. That state must preserve some information, discard other information, and suppor

agentsarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation

DGX agent

arXiv:2605.01394v1 Announce Type: cross Abstract: Formal specification is essential for rigorous program verification, yet writing correct specifications remains costly and difficult to automate. Alth

model-releasesarxiv-cs-ai

6 May 2026

LLM-Powered AI Agent Systems and Their Applications in Industry

arXiv:2505.16120v2 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered

agentsarxiv-cs-ai

LLMs Should Not Yet Be Credited with Decision Explanation

arXiv:2605.01164v1 Announce Type: new Abstract: This position paper argues that LLMs should not yet be credited with decision explanation. This matters because recent work increasingly treats accurate

researcharxiv-cs-ai

Applications

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

arXiv:2605.03971v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors

applicationsarxiv-cs-cl

MCJudgeBench: A Benchmark for Constraint-Level Judge Evaluation in Multi-Constraint Instruction Following

arXiv:2605.03858v1 Announce Type: new Abstract: Multi-constraint instruction following requires verifying whether a response satisfies multiple individual requirements, yet LLM judges are often assess

Local Ai

MedGemma 1.5 Technical Report

arXiv:2604.05081v2 Announce Type: replace Abstract: We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities

local-aiarxiv-cs-ai

MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing

arXiv:2605.02199v1 Announce Type: new Abstract: Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually mea

researcharxiv-cs-ai

Model Routing as a Trust Problem: Route Receipts for Adaptive AI Systems

arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before res

Hardware

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

arXiv:2603.03756v3 Announce Type: replace-cross Abstract: While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, l

hardwarearxiv-cs-cl

Neural Decision-Propagation for Answer Set Programming

arXiv:2605.01797v1 Announce Type: new Abstract: Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI. While existing approaches extend

tutorialsarxiv-cs-ai

NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science

arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag

Not that Groove: Zero-Shot Symbolic Music Editing

arXiv:2505.08203v2 Announce Type: replace-cross Abstract: While recent advancements in AI music generation have predominantly focused on direct audio synthesis, these systems suffer from inherent rigi

Physically Guided Visual Mass Estimation from a Single RGB Image

arXiv:2601.20303v2 Announce Type: replace Abstract: Estimating object mass from visual input is challenging because mass depends jointly on geometric volume and material-dependent density, neither of

tutorialsarxiv-cs-cv

PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments

arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record

Power-Softmax: Towards Secure LLM Inference over Encrypted Data

arXiv:2410.09457v2 Announce Type: replace Abstract: Modern cryptographic methods for implementing privacy-preserving LLMs such as gls{HE} require the LLMs to have a polynomial form. Forming such a rep

safetyarxiv-cs-lg

PrismAgent: Illuminating Harm in Memes via a Zero-Shot Interpretable Multi-Agent Framework

arXiv:2605.02940v1 Announce Type: new Abstract: The rapid spread of memes makes harmful content detection increasingly crucial, as effective identification can curb the circulation of misinformation.

agentsarxiv-cs-lg

Retrieval-Augmented LLMs for Security Incident Analysis

arXiv:2603.18196v3 Announce Type: replace-cross Abstract: Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection aler

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding

arXiv:2605.03514v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a wid

researcharxiv-cs-cl

S^2tory: Story Spine Distillation for Movie Script Summarization

arXiv:2605.03244v1 Announce Type: new Abstract: Movie scripts pose a fundamental challenge for automatic summarization due to their non-linear, cross-cut narrative structure, which makes surface-level

agentsarxiv-cs-cl

Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation

arXiv:2605.01750v1 Announce Type: cross Abstract: Grounding is the collaborative process of establishing mutual belief sufficient for the current communicative purpose. While static grounding maps lan

Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

model-releasesarxiv-cs-cv

arXiv:2605.03909v1 Announce Type: cross Abstract: Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor conf

Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version

arXiv:2605.02024v1 Announce Type: new Abstract: In Dung-style abstract argumentation, various semantics capture notions of acceptability of arguments. The admissibility semantics capture the notion th

Text-Conditional JEPA for Learning Semantically Rich Visual Representations

arXiv:2605.03245v1 Announce Type: cross Abstract: Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature pre

tutorialsarxiv-cs-cv

The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate

arXiv:2605.00914v1 Announce Type: cross Abstract: Multi-agent debate, where teams of LLMs iteratively exchange rationales and vote on answers, is widely deployed under the assumption that peer review

The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence

arXiv:2605.02106v1 Announce Type: new Abstract: Contemporary artificial intelligence systems achieve strong performance through large-scale parameterization, retrieval augmentation, and training on ex

The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It

arXiv:2605.03258v1 Announce Type: cross Abstract: Large language models often fail at simple counting tasks, even when the items to count are explicitly present in the prompt. We investigate whether t

Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection

arXiv:2605.03358v1 Announce Type: new Abstract: When orthodontists trace cephalometric radiographs, they follow a structured workflow: identify the soft tissue profile, partition the skull into anatom

safetyarxiv-cs-cv

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

arXiv:2605.01970v2 Announce Type: cross Abstract: Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characte

1BT: One-Block Transformer for EEG-Based Cognitive Workload Assessment

arXiv:2605.00856v1 Announce Type: cross Abstract: Accurate and continuous estimation of cognitive workload is fundamental to creating adaptive human-machine systems. However, designing architectures t

researcharxiv-cs-lg

A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning

arXiv:2605.01232v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled visually realistic demonstration generation from a single expert trajectory and a short mul

safetyarxiv-cs-ro

A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation

arXiv:2605.01065v1 Announce Type: new Abstract: The goal of differentially private text obfuscation is to obfuscate, or 'perturb', input texts with Differential Privacy (DP) guarantees, such that the

researcharxiv-cs-cl

Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving

arXiv:2605.00880v1 Announce Type: new Abstract: Autonomous driving (AD) is evolving towards end-to-end (E2E) frameworks through two primary paradigms: monolithic models exemplified by Vision-Language-

agentsarxiv-cs-cv

Agentic AI for Trip Planning Optimization Application

arXiv:2605.00276v1 Announce Type: new Abstract: Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting

AgentReputation: A Decentralized Agentic AI Reputation Framework

arXiv:2605.00073v1 Announce Type: new Abstract: Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security audi

ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review

model-releasesarxiv-cs-lg

arXiv:2605.02651v1 Announce Type: cross Abstract: Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibil

Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance

model-releasesarxiv-cs-ro

arXiv:2605.01368v1 Announce Type: new Abstract: Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the no

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2506.09082v5 Announce Type: replace Abstract: The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as gener

Behavior-Grounded Lane Representation Learning for Multi-Task Traffic Digital Twins

arXiv:2605.01901v1 Announce Type: new Abstract: Traffic digital twins are powerful tools for advanced traffic management, and most systems are built on static geometric representations. However, these

safetyarxiv-cs-cv

Applications

Can Causal Discovery Algorithms Help in Generating Legal Arguments?

arXiv:2605.02318v1 Announce Type: cross Abstract: In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence thro

applicationsarxiv-cs-lg

CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets

arXiv:2401.04139v4 Announce Type: replace Abstract: Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but criti

tutorialsarxiv-cs-lg

Local Ai

CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction

arXiv:2605.01346v1 Announce Type: new Abstract: Standard selective prediction methods typically estimate uncertainty from the output of a single predictive branch. While effective for general uncertai

local-aiarxiv-cs-cv

Applications

CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine

arXiv:2605.01011v1 Announce Type: new Abstract: Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiri

applicationsarxiv-cs-cl

Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models

arXiv:2601.08517v2 Announce Type: replace Abstract: Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial chal

tutorialsarxiv-cs-cv

COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following

arXiv:2509.04470v2 Announce Type: replace Abstract: Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detectio

CoFrGeNet: Continued Fraction Architectures for Language Generation

arXiv:2601.21766v3 Announce Type: replace Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new fun

researcharxiv-cs-cl

ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming

arXiv:2605.02647v1 Announce Type: new Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety alignment and elicit harmful responses. A growing body of work sh

CorrSteer: Generation-Time LLM Steering via Correlated Sparse Autoencoder Features

arXiv:2508.12535v3 Announce Type: replace Abstract: Sparse Autoencoders (SAEs) can extract interpretable features from large language models (LLMs) without supervision. However, their effectiveness in