Search: “reasoning”

arXiv:2609.09153v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions throug

local-aiarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

S^3-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants

DGX agent

arXiv:2609.09852v1 Announce Type: new Abstract: The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interac

researcharxiv-cs-cl

10 Sept 2026

SchemeArena: Factorized Stress Testing of Scheming in LLM Agents

arXiv:2609.08126v1 Announce Type: new Abstract: We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction o

The End of AI Exponentiation: Fluttering Inside and Outside AI Bubble

arXiv:2609.05894v1 Announce Type: new Abstract: The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large langu

agentsarxiv-cs-ai

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

arXiv:2601.18930v5 Announce Type: replace Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast this probl

agentsarxiv-cs-lg

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

arXiv:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species ide

Tutorials

ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction

arXiv:2609.06723v1 Announce Type: new Abstract: Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods stru

tutorialsarxiv-cs-cv

An overview of 3D Vision-Language Models

arXiv:2609.05583v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and

safetyarxiv-cs-cv

Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping

arXiv:2609.06820v1 Announce Type: cross Abstract: Active mapping requires a robot to select camera viewpoints that efficiently reconstruct an unknown 3D scene. To reason about unobserved regions, rece

Local Ai

DualPathOcc: Dual-Resolution BEV Encoder for 3D Occupancy Prediction

arXiv:2609.06370v1 Announce Type: new Abstract: Predicting 3D occupancy from multi-view images requires preserving geometric detail during 2D-to-3D lifting while reasoning over sparse, volumetric scen

local-aiarxiv-cs-cv

Emergent Goal-Directed Attention in Large Vision-Language Models

arXiv:2609.05517v1 Announce Type: new Abstract: Human observers prioritize visual information according to task goals. Most computational models of naturalistic viewing are gaze-trained for free viewi

Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks

arXiv:2609.08404v1 Announce Type: new Abstract: Large Language Models demonstrate remarkable proficiency in static reasoning, yet training them as autonomous agents through Reinforcement Learning (RL)

safetyarxiv-cs-lg

FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection

arXiv:2609.05876v1 Announce Type: new Abstract: AI-generated image detection is increasingly open-world: new image generators produce highly realistic images that make visual artifacts harder to ident

safetyarxiv-cs-cv

FineHOI: Part-Aware Dense Representations for Zero-Shot Human-Object Interaction Detection

arXiv:2609.05959v1 Announce Type: new Abstract: Human-Object Interaction (HOI) detection aims to localize humans and objects in images and classify their interactions. Zero-shot HOI focuses on recogni

GIFT: Reconciling Post-Training Objectives via Variational Finite-Temperature Gibbs Initialization

arXiv:2601.09233v3 Announce Type: replace Abstract: The prevailing post-training paradigm for Large Reasoning Models (LRMs)---Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL)-suffe

researcharxiv-cs-lg

Human-Centric Image Captioning with Subject-Centered Spatial Understanding

arXiv:2609.08300v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural halluc

LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation

arXiv:2602.12351v2 Announce Type: replace-cross Abstract: This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VL

safetyarxiv-cs-cv

MARBO: Relational Belief Grounding for LLM Agents in Social Deduction Games

arXiv:2609.06563v1 Announce Type: cross Abstract: Social deduction games (SDGs) require agents to reason under partial observability by maintaining relational beliefs about hidden roles and team align

agentsarxiv-cs-lg

MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data

arXiv:2601.06847v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We posit tha

Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter

arXiv:2609.05515v1 Announce Type: new Abstract: Multi-robot informative path planning (IPP) for persistent target monitoring requires robots to reason about spatial uncertainty, temporal evolution, an

safetyarxiv-cs-ro

One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control

arXiv:2609.06469v1 Announce Type: new Abstract: Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often deg

safetyarxiv-cs-lg

Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection

arXiv:2609.07455v1 Announce Type: new Abstract: How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? M

PhysVidBench: Language-Grounded Evaluation of Physical Commonsense in Text-to-Video Models

arXiv:2507.15824v2 Announce Type: replace Abstract: Text-to-video (T2V) models now produce striking visuals, yet they routinely violate everyday physics; objects float, tools are misused, and causal s

SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs

arXiv:2609.07823v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains undere

SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition

arXiv:2609.08221v1 Announce Type: new Abstract: Micro-actions are subtle, low-intensity non-verbal behaviors that provide cues to fine-grained human states, including emotions and intentions. Recogniz

Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting

arXiv:2601.14862v2 Announce Type: replace Abstract: We introduce Strategic Doctrine Language Models (sdLM), a learning-system framework for multi-document strategic reasoning with doctrinal consistenc

researcharxiv-cs-lg

Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification

arXiv:2609.07420v1 Announce Type: new Abstract: Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from video frames remain

Local Ai

A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning

arXiv:2609.05133v1 Announce Type: cross Abstract: This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reasoning and local control operate at differe

local-aiarxiv-cs-ai

Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning

arXiv:2609.04770v1 Announce Type: new Abstract: Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cogniti

agentsarxiv-cs-ro

Applications

Corporate Language Model (CLM): Transforming Tacit and Fragmented Enterprise Knowledge into a Sovereign, Auditable, and Executable Corporate Intelligence Layer

arXiv:2609.04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and

applicationsarxiv-cs-ai

Human-Human & Human-Robot Interaction Transformer (H2INT) for Robot Navigation in Dense and Uncertain Crowds

arXiv:2609.05300v1 Announce Type: new Abstract: Safe robot navigation in dense crowds requires reasoning about pedestrian motion and how it may change in response to a robot. However, many learning-ba

safetyarxiv-cs-ro

MM-IFEval-Pro: A Multilingual and Attack-Resistant Benchmark for Instruction-Following in Vision-Language Models

arXiv:2609.04859v1 Announce Type: new Abstract: As vision-language models (VLMs) rapidly advance in image understanding, cross-modal reasoning, and complex instruction execution, instruction-following

Molecular Deja Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

arXiv:2609.05381v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a proper

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

arXiv:2609.05324v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchma

Tracing Audio Grounding and Answer Selection in Audio LLMs

arXiv:2609.04637v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) have advanced in audio understanding, yet they can still predict the answer by reasoning from textual cues or

researcharxiv-cs-ai

When Seeing Overrides Knowing: Visual Dominance and Deferral-Based Method for Personalized Safety in VLMs

arXiv:2609.04281v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed in high-stakes settings, where a response that is reasonable in general may still be unsafe fo

A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities

arXiv:2609.03687v1 Announce Type: new Abstract: Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singula

researcharxiv-cs-cl

A Computationally Feasible Framework for Causal Probabilistic Explanation

arXiv:2609.04177v1 Announce Type: new Abstract: Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis.

safetyarxiv-cs-ai

BASP: Communication-Efficient Batch-Aware Sequence Parallelism for LLM Training

model-releasesarxiv-cs-lg

arXiv:2609.03151v1 Announce Type: cross Abstract: Long-context reasoning for large language models (LLMs) is becoming increasingly important, but training over long sequences remains challenging due t

Beyond Shallow Alignment: How Post-Training Methods Determine Refusal Circuits And Steering Robustness

model-releasesarxiv-cs-cl

arXiv:2609.03887v1 Announce Type: new Abstract: How do the methods used to train language models to refuse harmful requests shape how that refusal actually works inside the model? We compare three pos

Continuous Actions from Discrete Minds: Latent-Aligned Planning for End-to-End Autonomous Driving

arXiv:2609.04070v1 Announce Type: new Abstract: Bridging the gap between the discrete reasoning of Vision-Language Models and the continuous, physics-constrained nature of autonomous driving remains a

InSituMeasure: Probing Situated Measurement Grounding in Industrial Scenes with Multimodal Large Language Models

arXiv:2609.04014v1 Announce Type: new Abstract: For trained operators, gauge reading requires little specialized knowledge, low cognitive effort, and high repeatability. Yet Multimodal Large Language

safetyarxiv-cs-ai

Judging LLM-as-a-Judge: Concerning Rubric Artifacts in LLM-based Automated Text Generation Evaluation

arXiv:2609.02942v1 Announce Type: cross Abstract: LLM-as-a-Judge pipelines are increasingly used to evaluate AI-generated text, based on the assumption that judgments arise from reasoning over candida

researcharxiv-cs-ai

Large Language Models in Resolving Contextual Knowledge Conflicts

arXiv:2609.03148v1 Announce Type: new Abstract: Most prior works focused on conflicts between an LLM's internal parametric knowledge and externally provided context. In contrast, we investigate how LL

safetyarxiv-cs-cl

MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval

arXiv:2609.03201v1 Announce Type: new Abstract: Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved co

researcharxiv-cs-cl

NeoRed: A Knowledge-Logic-Alignment Multimodal Large Language Model for Neonatal Respiratory Disease Diagnosis

arXiv:2609.03527v1 Announce Type: new Abstract: Neonatal respiratory diseases are a major cause of neonatal morbidity and mortality, posing substantial challenges in clinical practice. Despite recent

safetyarxiv-cs-ai

R^{2}Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG

arXiv:2609.02894v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla

researcharxiv-cs-cl

Sequential Beats Joint: On the Interplay between On-Policy Distillation and RLVR

arXiv:2609.04108v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have emerged as two dominant methods for post-training reasonin