Search: “reasoning”

Counteraction-Aware Multi-Teacher On-Policy Distillation for General Capability Recovery with Domain Preservation

arXiv:2605.27115v1 Announce Type: new Abstract: Domain specialization can improve LLM behavior in vertical domains, but often weakens the general capabilities inherited from the original model. Recent

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Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking

DGX agent

arXiv:2605.26385v1 Announce Type: cross Abstract: Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker

safetyarxiv-cs-ai

27 May 2026

Applications

Decent chance that, not even taking into account GenAI, this will be viewed as a decade of incredible progress against the problems that wer…

applicationsethan-mollick--x

Decent chance that, not even taking into account GenAI, this will be viewed as a decade of incredible progress against the problems that were, ironically, caused by modernity: metabolic syndrome, auto

DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation

arXiv:2602.18907v2 Announce Type: replace-cross Abstract: We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresse

researcharxiv-cs-cv

Applications

DinoComplete: 3D Shape Completion with Distilled Semantic Priors and State Space Models

arXiv:2605.26949v1 Announce Type: new Abstract: 3D shape completion from partial scans remains challenging for unseen categories and noisy real-world observations, where geometry alone is often insuff

applicationsarxiv-cs-cv

ECSEL: Explainable Classification via Signomial Equation Learning

arXiv:2601.21789v2 Announce Type: replace-cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the o

Entropy Sentinel: Continuous LLM Accuracy Monitoring from Decoding Entropy Traces in STEM

arXiv:2601.09001v4 Announce Type: replace Abstract: Deploying LLMs raises two coupled challenges: (1) monitoring--estimating where a model underperforms as traffic and domains drift--and (2) improveme

Experiments in Agentic AI for Science

arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote

local-aiarxiv-cs-ai

Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL

arXiv:2605.26371v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than non-hierarchical counte

Generating Robust Portfolios of Optimization Models using Large Language Models

arXiv:2605.27013v1 Announce Type: new Abstract: Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimiz

Grammar of the Wave: Towards Explainable Multivariate Time Series Event Detection via Neuro-Symbolic VLM Agents

arXiv:2603.11479v2 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-sta

Image Feature Fusion-based Federated Client Unlearning (FCU)

arXiv:2605.26715v1 Announce Type: new Abstract: Major data protection regulations all mention the 'right to be forgotten,' and that's what pushed federated unlearning (FU) techniques forward. But one

researcharxiv-cs-lg

IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streams

model-releasesarxiv-cs-cv

arXiv:2605.27074v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) achieve strong performance on reactive question answering, but real-world streaming assistants require p

JobBench: Aligning Agent Work With Human Will

arXiv:2605.26329v1 Announce Type: new Abstract: Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story. We introduce JobBench, which evaluat

Tutorials

Joint 2D-3D Segmentation and Association in Street-level Imaging

arXiv:2605.26725v1 Announce Type: new Abstract: Accurate interpretation of street-level imagery is essential for large-scale urban mapping and the creation of Spatial Digital Twin (SDT) environments.

tutorialsarxiv-cs-cv

Learning to Predict Future-Aligned Research Proposals with Language Models

arXiv:2603.27146v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals re

Lessons from Penetration Tests on Large-Scale Agent Systems

arXiv:2605.27042v1 Announce Type: cross Abstract: As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of

LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring

arXiv:2605.27088v1 Announce Type: new Abstract: Aligning LLMs for math tutoring typically requires RL-based training with multi-GPU infrastructure. We investigate whether training-free prompt optimiza

MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv:2605.26842v1 Announce Type: cross Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to pro

local-aiarxiv-cs-cl

MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation

arXiv:2602.09878v2 Announce Type: replace Abstract: World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely im

safetyarxiv-cs-cv

Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

arXiv:2605.26942v1 Announce Type: new Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce una

OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants

arXiv:2605.26485v1 Announce Type: cross Abstract: We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-vi

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic

arXiv:2511.20586v4 Announce Type: replace Abstract: Trustworthiness has become a key requirement for the deployment of artificial intelligence systems in safety-critical applications. Conventional eva

Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions

arXiv:2605.26256v1 Announce Type: new Abstract: Multimodal large language model (MLLM)-based embodied agents have shown strong potential for solving complex tasks in physical environments. However, pe

PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design

arXiv:2605.26543v1 Announce Type: new Abstract: Polymer discovery is central to fields ranging from energy storage to biomedicine, but it is hindered by an astronomically large chemical design space a

Qiskit QuantumKatas: Adapting Microsoft's Quantum Computing exercises for LLM evaluation

arXiv:2605.27210v1 Announce Type: cross Abstract: We adapt Microsoft's QuantumKatas -- a well-established quantum computing curriculum -- from Q# to Qiskit, the most widely-adopted quantum computing f

Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation

arXiv:2003.05746v4 Announce Type: replace-cross Abstract: In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of f

Representation-Aware Unlearning via Activation Signatures: From Suppression to Entity-Signature Erasure

arXiv:2601.10566v5 Announce Type: replace Abstract: Entity-level unlearning is usually evaluated by what a model says: whether it stops naming the target, refuses a query, or shifts a Truth Ratio dist

REVERSE: Reinforcing Evidence Verification and Search for Agentic Image geo-localization

arXiv:2605.26861v1 Announce Type: new Abstract: Image geo-localization aims to determine where a photograph was taken, a task that often requires more than recognizing visible landmarks. Human experts

local-aiarxiv-cs-cv

Risk Averse Alert Prioritization for IDS Using Subnormal Gaussian Fuzzy Models

arXiv:2605.27299v1 Announce Type: cross Abstract: Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too

Applications

Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

arXiv:2506.03627v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. Howe

applicationsarxiv-cs-ai

Hardware

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models

arXiv:2605.26632v1 Announce Type: new Abstract: Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cos

hardwarearxiv-cs-lg

Slide Deck Q&A Quality Assurance App: A Multi-Stage Pipeline for Pedagogical Question Generation

arXiv:2605.26428v1 Announce Type: new Abstract: Generating high-quality, pedagogically useful questions from lecture slide decks is difficult because important instructional content is distributed acr

researcharxiv-cs-cl

Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training

arXiv:2605.26606v1 Announce Type: cross Abstract: Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout gener

SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking

arXiv:2511.04711v2 Announce Type: replace-cross Abstract: Large-scale vision-language models, especially CLIP, have demonstrated remarkable performance across diverse downstream tasks. Soft prompts, a

The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance

arXiv:2601.07085v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding mi

The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works

model-releasesarxiv-cs-lg

arXiv:2605.26246v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student. In language modeling, the student is trained either on

The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models

arXiv:2605.26128v1 Announce Type: new Abstract: Production LLM systems increasingly require machine-readable outputs: JSON objects, typed traces, regex-constrained fields, and tool-call schemas. This

local-aiarxiv-cs-lg

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

arXiv:2605.26494v1 Announce Type: new Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum

The MiniMax M2 series was one of the most widely used open-weight LLM series earlier this year. Now, we got a technical report with some int…

model-releasessebastian-raschka--x

The MiniMax M2 series was one of the most widely used open-weight LLM series earlier this year. Now, we got a technical report with some interesting tidbits. I summarized some of them below: 1. Full a

The Necessity of a Unified Framework for LLM-Based Agent Evaluation

arXiv:2602.03238v2 Announce Type: replace Abstract: With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents present

Hardware

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

arXiv:2605.26720v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by feedback-conditioned planni

hardwarearxiv-cs-ai

UCPO: Uncertainty-Aware Policy Optimization

arXiv:2601.22648v2 Announce Type: replace Abstract: The key to building trustworthy large language models (LLMs) lies in endowing them with inherent uncertainty expression capabilities, thereby mitiga

VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions

arXiv:2605.27141v1 Announce Type: new Abstract: Large language models (LLMs) have evolved into interactive agents that collaborate with users in real-world tasks. Effective collaboration in such setti

When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning

arXiv:2605.26350v1 Announce Type: cross Abstract: In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples. However, we

Tutorials

A Context Augmented Multi-Play Multi-Armed Bandit Algorithm for Fast Channel Allocation in Opportunistic Spectrum Access

arXiv:2605.25391v1 Announce Type: new Abstract: We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario

tutorialsarxiv-cs-lg

26 May 2026

A Contractive Feedback Semantics for Reinforcement Learning

arXiv:2605.24759v1 Announce Type: new Abstract: Discounted reinforcement learning is usually presented through Bellman equations on closed Markov decision processes. This paper develops a compositiona

safetyarxiv-cs-lg

26 May 2026

A post about Pope Leo XIV's encyclical on AI. Why the Pope is right, but perhaps not right enough. Artificial intelligence is reshaping the …