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Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems

arXiv:2606.17443v2 Announce Type: replace Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new ch

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Model Releases

Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports

DGX agent

arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (

model-releasesarxiv-cs-cl

25 Aug 2026

Inertial Manifold Neural Operator for Dissipative Time-Dependent Partial Differential Equations

model-releasesarxiv-cs-lg

arXiv:2608.23546v1 Announce Type: cross Abstract: In this paper, we introduce the Inertial Manifold Neural Operator (IMNO) for solving dissipative time-dependent partial differential equations (PDEs).

Inferring Action from Future Latent State for Robotic Manipulation

arXiv:2608.22067v1 Announce Type: cross Abstract: World-Action Models (WAMs) build robot control on video-generation backbones, which jointly predict dense future visual trajectories and robot actions

InjecMEM: Memory Injection Attack on LLM Agent Memory Systems

arXiv:2608.23471v1 Announce Type: cross Abstract: Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a question:

agentsarxiv-cs-ai

InstructMove: A Text-Indispensable Benchmark for Instruction-Following Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.22990v1 Announce Type: new Abstract: Vision-language-action (VLA) models have made general-purpose robot manipulation increasingly plausible by conditioning robot actions on natural-languag

IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning

model-releasesarxiv-cs-cv

arXiv:2608.23330v1 Announce Type: new Abstract: Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human actions

Interpretable AI with Local Distillation

model-releasesarxiv-cs-lg

arXiv:2608.23538v1 Announce Type: cross Abstract: Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reaso

Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak

arXiv:2608.22515v1 Announce Type: cross Abstract: Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction

Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens

arXiv:2608.21389v1 Announce Type: cross Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject stu

Investigating Relational Reasoning in VLMs

arXiv:2608.23518v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong performance in visual reasoning tasks, but it remains unclear whether they understand visual relations, or

Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data

arXiv:2608.23256v1 Announce Type: new Abstract: Recent work proposes next-chunk reasoning RL for leveraging no-CoT data---corpora such as worked solutions and textbook derivations that contain reasoni

iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems

arXiv:2602.06064v2 Announce Type: replace-cross Abstract: Scheduling precedence-constrained tasks under shared renewable resources is critical to modern computing platforms. It is often modeled as the

Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation

arXiv:2608.22651v1 Announce Type: new Abstract: Modern text-to-SQL systems have become increasingly elaborate, relying on schema-linking modules, retrieval-augmented prompting, candidate generation, a

researcharxiv-cs-cl

Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinement

arXiv:2608.23098v1 Announce Type: new Abstract: Classical Chinese poetry composition has long valued Tuiqiao, the iterative refinement of words, imagery, and prosody. However, many current AI poetry s

agentsarxiv-cs-ai

Tutorials

Joint Causal Structure and Cluster Discovery Using Variational Inference

arXiv:2608.22212v1 Announce Type: cross Abstract: Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate mod

tutorialsarxiv-cs-ai

K-Bench: measuring model performance on real scientific agent requests

arXiv:2608.21601v1 Announce Type: new Abstract: Benchmarks for scientific artificial intelligence are mostly written to be scored: multiple-choice questions, curated agent tasks with reference solutio

KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks

arXiv:2608.21488v1 Announce Type: cross Abstract: While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exp

KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees

arXiv:2608.23393v1 Announce Type: cross Abstract: KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the tr

Hardware

Kernel Token Contradiction: a Fast and Principled Approach for LLM Claim Uncertainty Quantification

arXiv:2608.22506v1 Announce Type: new Abstract: Claim-level Uncertainty Quantification (UQ) aims to mitigate the lack of reliability of Large Language Models (LLMs) by evaluating the factuality of eac

hardwarearxiv-cs-cl

KMGen: A Skill-based Approach for Synthetic Individual Patient Data Generation

model-releasesarxiv-cs-lg

arXiv:2608.22618v1 Announce Type: new Abstract: Individual patient data (IPD) from clinical trials is the substrate for survival modeling, meta-analysis, and safety research, yet IPD is rarely release

Knowing Isn't Always Saying: When Do Spatial Encodings Reach Answers in Vision-Language Models?

arXiv:2608.22916v1 Announce Type: new Abstract: Vision-language models are known to encode spatial information in their hidden states, yet often fail to use it when answering. However, it remains uncl

researcharxiv-cs-cl

KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget

arXiv:2608.22389v1 Announce Type: cross Abstract: Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This remov

KPI-Conditioned Generative Design of Automotive Hood Inner Panels: A Two-Stage Retrieval-Generation Pipeline with Surrogate-Based Performance Estimation

arXiv:2608.22457v1 Announce Type: new Abstract: An inner hood panel must meet a deflection target, stay below a stress limit, and hit a mass target. Machine-learned surrogates have made the forward di

KSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search

arXiv:2608.21365v1 Announce Type: cross Abstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in

KVBoost: Chunk-Level Key-Value Cache Reuse with Deviation-Guided Recomputation for Efficient Large Language Model Inference

arXiv:2608.21362v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) incur high prefill latency because key-value (KV) tensors must be recomputed for each request. Existing p

LaGEA: Language Guided Embodied Agents for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2509.23155v3 Announce Type: replace Abstract: Robotic manipulation benefits from foundation models that describe goals, but today's agents still lack a principled way to learn from their own mis

Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving

arXiv:2606.20274v2 Announce Type: replace Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and plan

agentsarxiv-cs-ai

LagrangeGS: Non-Conservative Lagrangian System on Dynamic 3D Gaussian Splatting

arXiv:2608.22773v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapol

safetyarxiv-cs-cv

Local Ai

LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

arXiv:2602.08793v2 Announce Type: replace Abstract: Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. Sta

local-aiarxiv-cs-cl

Language Chain in Alignment: Cross-Lingual Ranking Preference Optimization

arXiv:2608.23149v1 Announce Type: cross Abstract: The alignment of Large Language Models heavily relies on English-centric high-quality preference data, which often leads to suboptimal performance in

safetyarxiv-cs-ai

LanGuSTE: Language-Guided Coarse-to-Fine Patch Selection for Efficient Whole Slide Image Analysis

arXiv:2508.14537v2 Announce Type: replace Abstract: Whole slide images (WSIs) in computational pathology pose a major computational challenge due to their gigapixel scale, often requiring tens to hund

Applications

Large-Scale Evaluation of Advanced Imputation Methods for Missing Values in Smart Meter Data

arXiv:2608.21638v1 Announce Type: new Abstract: Accurate and reliable collection of electricity consumption data through Advanced Metering Infrastructure (AMI) is of great importance for the operation

applicationsarxiv-cs-lg

Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition

model-releasesarxiv-cs-cv

arXiv:2608.22879v1 Announce Type: new Abstract: Task-specific lightweight models for surgical phase recognition excel at capturing temporal dynamics but generalize poorly under domain shift. Conversel

Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression

arXiv:2606.14782v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with lon

researcharxiv-cs-cl

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

arXiv:2608.22403v1 Announce Type: new Abstract: Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to t

safetyarxiv-cs-ro

Learning from the Test: Self-Referential Differential Testing for Deep RL Agents

arXiv:2608.22284v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in re

safetyarxiv-cs-ai

Learning Generalizable Behaviors for Terminal Agents

arXiv:2608.22631v1 Announce Type: new Abstract: Terminal agents are a compelling application of large language models (LLMs), with the potential to integrate deeply into users' daily workflows. Reinfo

agentsarxiv-cs-lg

Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos

arXiv:2608.22102v1 Announce Type: cross Abstract: We present GCA (Gaussian Constitutive Alignment), a framework for learning implicit constitutive laws from monocular dynamic video of deformable objec

safetyarxiv-cs-ai

Learning in PINNs: Phase transition, diffusion equilibrium, and generalization

arXiv:2403.18494v2 Announce Type: replace Abstract: We investigate the learning dynamics of fully-connected neural networks through the lens of the neural gradient signal-to-noise ratio (SNR), examini

safetyarxiv-cs-lg

Tutorials

Learning Mamba as a Continual Learner: Meta-Learning Selective State Space Models for Continual Learning

arXiv:2412.00776v5 Announce Type: replace Abstract: Continual learning (CL) learns from a non-stationary data stream without storing or re-training on all seen samples. Meta-continual learning (MCL) c

tutorialsarxiv-cs-lg

Learning Reduced-Order Dynamics with Singularity via Latent-Augmented Neural Ordinary Differential Equations

arXiv:2608.22142v1 Announce Type: new Abstract: This paper addresses the issue of self-intersecting trajectories (in phase space) in industrial reduced-order modeling and proposes the Latent-Augmented

Learning Sample-wise Rank-aware Interpolation Weights for Composed Visual Data Retrieval

arXiv:2608.22500v1 Announce Type: new Abstract: At the heart of composed visual data retrieval is the fusion of a reference visual input and a textual modification into a single query. While current s

Learning Spherical Occupancy Profiles for Multi-View 3D Reconstruction and Generation

arXiv:2608.23206v1 Announce Type: new Abstract: We study spherical occupancy profiles-the ray-wise occupancy probability profiles P(r) = T(r) o(r) distilled from multi-view 3D Gaussian reconstructions

Learning to Control Coupled-Dynamics Environments with Joint Markov Decision Processes

arXiv:2608.22765v1 Announce Type: new Abstract: Coupled-dynamics environments expose the one-step outcomes that would follow from several possible counterfactual actions under a common realization of

safetyarxiv-cs-lg

Learning to Look Again: Loss-Gap Supervision for Free-form Crop Routing in Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2608.21762v1 Announce Type: cross Abstract: Vision-language models (VLMs) fail many detail-centric questions for a concrete reason: the answer is visible in the image, yet lost after the image i

Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach

arXiv:2307.09295v3 Announce Type: replace Abstract: We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially di

Learning with Boolean threshold functions

arXiv:2602.17493v2 Announce Type: replace-cross Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly pm 1, and the resulting models