Search: “reasoning”

In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

arXiv:2510.00777v2 Announce Type: replace Abstract: LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feed

researcharxiv-cs-lg

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Model Releases

Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection

DGX agent

arXiv:2605.30274v1 Announce Type: cross Abstract: Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that

model-releasesarxiv-cs-ai

29 May 2026

Masked Diffusion Vision-Language Models for Temporal Action Localization

arXiv:2605.29858v1 Announce Type: new Abstract: Temporal action localization (TAL) requires recognizing the target event and localizing its start and end times precisely in untrimmed videos. Recent vi

researcharxiv-cs-cv

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

arXiv:2512.17220v2 Announce Type: replace Abstract: Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior know

NaRA: Noise-Aware LoRA for Parameter-Efficient Fine-Tuning of Diffusion LLMs

arXiv:2605.29716v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative paradigm. Given the prohibitive computational cost of

Procedural Pretraining: Warming Up Language Models with Abstract Data

arXiv:2601.21725v2 Announce Type: replace Abstract: Pretraining language models directly on web-scale corpora is the de facto paradigm. We study an alternative where the model is initially exposed to

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

arXiv:2605.30280v1 Announce Type: cross Abstract: Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented cap

SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring

arXiv:2605.29543v1 Announce Type: cross Abstract: Pilot readback of Air Traffic Control (ATC) voice instructions is a primary safeguard against miscommunication in air transportation. However, readbac

researcharxiv-cs-ai

Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison

arXiv:2605.30087v1 Announce Type: new Abstract: Emerging personal AI agents are moving toward persistent, multi-source memory. This creates an evaluation problem: systems must decide how to use confli

Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?

arXiv:2605.29678v1 Announce Type: new Abstract: Large language models are highly sensitive to prompts, but this sensitivity is usually studied through task-relevant instructions, demonstrations, or re

Tutorials

SRUG: Shadow-Guided Relightable Urban Scene with Generation Model

arXiv:2605.24700v2 Announce Type: replace Abstract: Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend

tutorialsarxiv-cs-cv

TANDEM: Temporal-Aware Neural Detection for Multimodal Hate Speech

arXiv:2601.11178v2 Announce Type: replace Abstract: Social media platforms are increasingly dominated by long-form multimodal content, where harmful narratives are constructed through a complex interp

VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data

arXiv:2605.29483v1 Announce Type: new Abstract: Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-spe

World Models in Words: Auditing Physical State-Transition Commitments in Vision-Language Models

arXiv:2605.29585v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to answer questions about physical scenes, yet most evaluations reduce performance to a final answer

AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

arXiv:2602.18481v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation to

Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback

arXiv:2605.28010v1 Announce Type: new Abstract: Self-evolving large language models (LLMs) learn by generating their own training tasks and solutions, reducing reliance on human-curated supervision. H

Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models

arXiv:2512.00349v2 Announce Type: replace Abstract: Are frontier AI systems becoming more capable? Certainly. Yet such progress is not an unalloyed blessing but rather a Trojan horse: behind their per

DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints

arXiv:2605.27957v1 Announce Type: new Abstract: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage

Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning

arXiv:2605.27935v1 Announce Type: new Abstract: Recent mechanistic studies suggest that large language models (LLMs) may utilize their depth inefficiently in standard single-turn tasks. Whether this s

ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning

arXiv:2602.02150v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels co

EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

arXiv:2605.27820v1 Announce Type: new Abstract: As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop

EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection

arXiv:2505.17654v4 Announce Type: replace-cross Abstract: E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision Language Models (VLMs) to detect illicit or misleading produ

Local Ai

FD-RAG: Federated Dual-System Retrieval-Augmented Generation

arXiv:2605.27432v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG system

local-aiarxiv-cs-ai

From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets

arXiv:2605.28359v1 Announce Type: new Abstract: Evaluating whether large language model (LLM) agents can profit in capital markets is increasingly framed as end-to-end trading: place an agent in a his

Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems

arXiv:2605.27628v1 Announce Type: new Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remai

Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles

arXiv:2512.20780v3 Announce Type: replace Abstract: Recent work has explored the use of large language models (LLMs) to generate tutoring responses in mathematics, yet it remains unclear how closely t

agentsarxiv-cs-cl

MaskClaw: Edge-Side Personalized Privacy Arbitration for GUI Agents with Behavior-Driven Skill Evolution

arXiv:2605.28646v1 Announce Type: cross Abstract: GUI agents rely on screenshots to infer intent and operate across applications, but these screenshots often contain private messages, medical records,

Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning

arXiv:2602.01745v2 Announce Type: replace-cross Abstract: Token-level reweighting is a simple yet effective mechanism for controlling supervised fine-tuning, but common indicators are largely one-dime

SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models

arXiv:2605.28338v1 Announce Type: new Abstract: Large language models(LLMs) increasingly match expert performance on licensing examinations, yet routine clinical use remains limited because governance

The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic

arXiv:2605.28700v1 Announce Type: new Abstract: The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-

this is so funny, training opus 4.7 on business skills makes it misaligned and dishonest 😭

model-releasesemad-mostaque--x

this is so funny, training opus 4.7 on business skills makes it misaligned and dishonest 😭 Learnings from testing Claude Opus 4.8: > Much worse than Opus 4.7 and GPT 5.5 on Vending Bench > More aligne

Applications

Toward Semantic-Agnostic and Shape-Aware Vision-Language Segmentation Models

arXiv:2605.28348v1 Announce Type: new Abstract: Vision-language segmentation models have recently achieved strong performance by leveraging high-level semantic object categories expressed in natural l

applicationsarxiv-cs-cv

Tutorials

Transformers Provably Learn to Internalize Chain-of-Thought

arXiv:2605.28600v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning fro

tutorialsarxiv-cs-lg

When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models

arXiv:2605.28181v1 Announce Type: new Abstract: Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inferenc

AD-H: Language-guided Autonomous Driving with Hierarchical Agents

arXiv:2406.03474v2 Announce Type: replace Abstract: Language-guided autonomous driving requires bridging a large abstraction gap between high-level natural-language instructions and low-level vehicle

agentsarxiv-cs-cv

ADRD-Bench: A Preliminary LLM Benchmark for Alzheimer's Disease and Related Dementias

arXiv:2602.11460v2 Announce Type: replace Abstract: Large language models (LLMs) have shown great potential for healthcare applications. However, existing evaluation benchmarks provide minimal coverag

APEX-Searcher: Refining Credit Assignment with Subgoaling for Agentic Retrieval-Augmented Generation

arXiv:2603.13853v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) connects large language models (LLMs) to external knowledge, but single-round retrieval is often insuffic

agentsarxiv-cs-ai

FAB-Bench: A Framework for Adaptive RAG Benchmarking in Semiconductor Manufacturing

arXiv:2605.26476v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become critical for knowledge-intensive applications, yet evaluating its performance in vertical domains remain

If we had done everything I suggested in my 2020 arXiv article “The Next Decade in AI”, we might actually have reached AGI by now. In the la…

model-releasesgary-marcus--x

If we had done everything I suggested in my 2020 arXiv article “The Next Decade in AI”, we might actually have reached AGI by now. In the last three years, after a detour driven by the false promise o

It's Not the Capability: Harness Sensitivity Is Non-Monotone Across LLM Agent Tiers

arXiv:2605.26731v1 Announce Type: new Abstract: A prevalent assumption in LLM agent deployment holds that more structured harnesses universally improve reliability, and that higher-capability models n

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

arXiv:2603.01131v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in clinical diagnosis but remain limited by unreliable report generation, weak evidence ground

agentsarxiv-cs-ai

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling

arXiv:2605.26322v1 Announce Type: new Abstract: Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-

PersianMedQA: Evaluating Large Language Models on a Persian-English Bilingual Medical Question Answering Benchmark

arXiv:2506.00250v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable performance on a wide range of Natural Language Processing (NLP) benchmarks, often surpassing

PitchBench: Measuring Pitch Hearing in Audio-Language Models

arXiv:2605.26176v1 Announce Type: cross Abstract: Audio-language models (ALMs) are increasingly used in real-world applications that require understanding music, from music tutoring and transcription

Local Ai

Ratio-Variance Regularized Policy Optimization

arXiv:2605.26784v1 Announce Type: cross Abstract: Standard on-policy reinforcement learning relies on heuristic clipping to enforce trust regions, but this mechanism imposes a severe cost by indiscrim

local-aiarxiv-cs-ai

SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs

arXiv:2512.04868v2 Announce Type: replace-cross Abstract: Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual depend

Seeing vs. Believing: Evaluating the Language Bias of Open-Source MLLMs in Counter-Intuitive Scenes

arXiv:2601.07737v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in mainstream visual understanding tasks, but their ability