Search: “reasoning”

QQJ: Quantifying Qualitative Judgment for Scalable and Human-Aligned Evaluation of Generative AI

arXiv:2605.17382v1 Announce Type: new Abstract: The rapid progress of generative artificial intelligence has exposed fundamental limitations in existing evaluation methodologies, particularly for open

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Research

Quantum Sidecar Architectures for Hybrid AI Training and Inference: Stateful Protected Registers, Stateless Reset-and-Reprepare Circuits and Quantum Weight-State Outlook

DGX agent

arXiv:2605.18031v1 Announce Type: cross Abstract: We propose a quantum sidecar architecture family for future hybrid AI training and inference. The central idea is not to store an entire Transformer i

researcharxiv-cs-ai

19 May 2026

Qumus: Realization of An Embodied AI Quantum Material Experimentalist

arXiv:2605.18407v1 Announce Type: cross Abstract: While modern Large Language Models (LLMs) and agentic artificial intelligence (AI) have demonstrated transformative capabilities in digital domains, t

REBAR: Reference Ethical Benchmark for Autonomy Readiness

model-releasesarxiv-cs-ro

arXiv:2605.18423v1 Announce Type: new Abstract: As autonomous systems grow more advanced, objective metrics to evaluate their ethical and legal compliance are critical for informing end users of their

Responsible Agentic AI Requires Explicit Provenance

arXiv:2605.17169v1 Announce Type: new Abstract: Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reas

Revisiting the Adam-SGD Gap in LLM Pre-Training: The Role of Large Effective Learning Rates

model-releasesarxiv-cs-lg

arXiv:2605.17787v1 Announce Type: new Abstract: It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptive optimizers such as Adam in pre-training Large La

Right Predictions, Misleading Explanations: On the Vulnerability of Vision-Language Model Explanations

arXiv:2605.16651v1 Announce Type: new Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model de

safetyarxiv-cs-cv

RTI-Bench: A Structured Dataset for Indian Right-to-Information Decision Analysis

model-releasesarxiv-cs-cl

arXiv:2605.16843v1 Announce Type: new Abstract: India's Right to Information Act, 2005 gives every citizen the right to demand information from public authorities, yet in practice most people cannot m

SKG-Eval: Stateful Evaluation of Multi-Turn Dialogue via Incremental Semantic Knowledge Graphs

arXiv:2605.16650v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue systems remains challenging because response quality depends not only on the current prompt, but also on previously est

SocialMemBench: Are AI Memory Systems Ready for Social Group Settings?

arXiv:2605.17789v1 Announce Type: cross Abstract: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings. Th

Strategic Over-Parameterization for Generalizable Low-Rank Adaptation

arXiv:2605.16470v1 Announce Type: cross Abstract: Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands

STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics

arXiv:2605.18548v1 Announce Type: cross Abstract: Large language models (LLMs) deployed in real-world agentic applications must be capable of replanning and adapting when mid-task disruptions invalida

Supervising the search process produces reliable and generalizable information-seeking agents

arXiv:2502.13957v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming web search by shifting from document ranking to synthesizing answers, and are increasingly deplo

Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

arXiv:2605.16671v1 Announce Type: new Abstract: Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalab

Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms

model-releasesarxiv-cs-cv

arXiv:2605.17336v1 Announce Type: cross Abstract: Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and

Taming 'Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution

arXiv:2605.17348v1 Announce Type: new Abstract: Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall ef

safetyarxiv-cs-cl

Test-Time Hinting for Black-Box Vision-Language Models

arXiv:2605.16410v1 Announce Type: new Abstract: Test-time scaling (TTS) methods have proven highly effective for LLMs, yet their application to vision-language models (VLMs) remains relatively underex

researcharxiv-cs-cv

Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation

model-releasesarxiv-cs-lg

arXiv:2605.18430v1 Announce Type: new Abstract: Text-to-CAD generation aims to create parametric CAD models from natural language, enabling rapid prototyping and intuitive design workflows. However, e

The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence

arXiv:2605.16895v1 Announce Type: cross Abstract: End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgent

The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought

model-releasesarxiv-cs-cl

arXiv:2605.18079v1 Announce Type: cross Abstract: Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconn

The Hidden Cost of Contextual Sycophancy: an AI Literacy Intervention in Human-AI Collaboration

arXiv:2605.18372v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in educational settings as interactive tools for collaboration. However, their tendency toward syco

The Illusion of Specialization: Unveiling the Domain-Invariant 'Standing Committee' in Mixture-of-Experts Models

arXiv:2601.03425v2 Announce Type: replace-cross Abstract: Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumptio

The Lattice Representation Hypothesis of Large Language Models

arXiv:2603.01227v2 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical opera

researcharxiv-cs-ai

Thinking with Patterns: Breaking the Perceptual Bottleneck in Visual Planning via Pattern Induction

arXiv:2605.16848v1 Announce Type: cross Abstract: Planning from raw visual input remains a significant challenge for current Vision-Language Models (VLMs), when the complexity of input is beyond their

TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition

arXiv:2605.16790v1 Announce Type: cross Abstract: Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to s

TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents

arXiv:2605.16909v1 Announce Type: new Abstract: Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate

Tongyi DeepResearch Technical Report

arXiv:2510.24701v3 Announce Type: replace-cross Abstract: We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking res

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments

arXiv:2605.16269v1 Announce Type: cross Abstract: Modern military operations expose soldiers to sustained psychological stress, leading to acute reactions, post-traumatic stress symptoms, and other me

Training-Free Occluded Text Rendering via Glyph Priors and Attention-Guided Semantic Blending

arXiv:2605.16810v1 Announce Type: new Abstract: We present a training-free framework for occluded text rendering with a pretrained FLUX.1-dev backbone. The task requires a model to render recognizable

local-aiarxiv-cs-cv

Hardware

TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks

arXiv:2605.17170v1 Announce Type: new Abstract: Agentic workloads have emerged as a major workload for LLM inference. They differ significantly from chat-only workloads, requiring long-context process

hardwarearxiv-cs-lg

TusoAI: Agentic Optimization for Scientific Methods

arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too

Applications

Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery

arXiv:2605.17976v1 Announce Type: new Abstract: Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for

applicationsarxiv-cs-ai

Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification

arXiv:2605.17691v1 Announce Type: cross Abstract: Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant

View-Aware Semantic Alignment for Aerial-Ground Person Re-Identification

arXiv:2605.18192v1 Announce Type: new Abstract: Aerial-Ground Person Re-Identification (AGPReID) remains highly challenging due to drastic viewpoint variations between drones and fixed cameras. Existi

safetyarxiv-cs-cv

Applications

VISAFF: Speaker-Centered Visual Affective Feature Learning for Emotion Recognition in Conversation

arXiv:2605.18547v1 Announce Type: new Abstract: Emotion Recognition in Conversation (ERC) is essential for effective human-machine interaction, aiming to identify speakers' emotional states in multi-t

applicationsarxiv-cs-ai

VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?

model-releasesarxiv-cs-cv

arXiv:2602.04802v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing bench

Visual Agentic Memory: Enabling Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval

arXiv:2605.16481v1 Announce Type: cross Abstract: Long video understanding requires more than large context windows. It also needs a memory mechanism that decides what visual evidence to retain, keeps

World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems

arXiv:2605.16547v1 Announce Type: new Abstract: Semantic communication has emerged as a promising paradigm for enabling goal-oriented networking. However, most existing semantic communication solution

safetyarxiv-cs-lg

Hardware

A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation

arXiv:2605.15475v1 Announce Type: new Abstract: We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-F

hardwarearxiv-cs-cv

A3D: Agentic AI flow for autonomous Accelerator Design

arXiv:2605.15237v1 Announce Type: cross Abstract: Accelerating applications through the design of hardware accelerators can significantly enhance system performance and energy efficiency. Despite adva

ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment

arXiv:2505.19241v2 Announce Type: replace-cross Abstract: The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various do

AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

arXiv:2605.15206v1 Announce Type: cross Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based ques

Antidistillation Fingerprinting

arXiv:2602.03812v2 Announce Type: replace-cross Abstract: Model distillation enables efficient emulation of frontier large language models (LLMs), creating a need for robust mechanisms to detect when

researcharxiv-cs-ai

Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling

arXiv:2507.01679v3 Announce Type: replace-cross Abstract: Existing LLMs-post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Each par

Capability Conditioned Scaffolding for Professional Human LLM Collaboration

arXiv:2605.15404v1 Announce Type: new Abstract: Large language model personalization typically adapts outputs to user preferences and style but does not account for differences in user evaluation capa

researcharxiv-cs-cl

CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity

arXiv:2605.16000v1 Announce Type: cross Abstract: Editors and reviewers are expected to ensure that manuscripts cite relevant, accurate, current, and ethically appropriate literature, yet manuscript-l

Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most

arXiv:2605.16207v1 Announce Type: new Abstract: Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring

Deep Pre-Alignment for VLMs

model-releasesarxiv-cs-cv

arXiv:2605.15300v1 Announce Type: new Abstract: Most Vision Language Models (VLMs) directly map outputs from ViT encoders to the LLM via a lightweight projector. While effective, recent analysis sugge