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Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization

arXiv:2608.16539v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation,

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LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

DGX agent

arXiv:2506.07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG)

model-releasesarxiv-cs-ai

18 Aug 2026

Agents

LLM-based Framework for Generating and Verifying Parallel DEVS Statecharts

arXiv:2608.14956v1 Announce Type: new Abstract: The development of models demands sound modeling and simulation knowledge as well as domain knowledge. Every model should accurately represent a system'

agentsarxiv-cs-lg

LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

arXiv:2608.15041v1 Announce Type: new Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational infor

LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review

arXiv:2608.14626v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved substantial progress in safety alignment, yet their safety guarantees remain significantly weaker in low-re

LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning

arXiv:2607.10139v3 Announce Type: replace-cross Abstract: Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each car

LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks

arXiv:2608.14927v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collabora

LLMs for Zero-Shot Threat Detection via Structured Risk Indicators

model-releasesarxiv-cs-lg

arXiv:2608.16508v1 Announce Type: cross Abstract: We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from het

Tutorials

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608.15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other

tutorialsarxiv-cs-ai

Local Ai

Local AI pre-screening for human triple-blind peer review in health sciences

arXiv:2608.14625v1 Announce Type: cross Abstract: Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107. Thi

local-aiarxiv-cs-ai

Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders

arXiv:2608.14717v1 Announce Type: new Abstract: A query-relation deletion can improve the edited slot while reducing the utility of the prediction set that contains it. We study this tension in two re

researcharxiv-cs-cv

Local Ai

Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN

arXiv:2608.16429v1 Announce Type: new Abstract: Foundational models for tabular data have made significant progress in recent years, with TabICLv2 reporting state-of-the-art performance on several tab

local-aiarxiv-cs-lg

Logical Embeddings for Argument Analysis

arXiv:2608.15325v1 Announce Type: cross Abstract: We propose a new framework for machine-learning-oriented argument analysis tasks. Our proposal involves replacing traditional contextualized word embe

LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents

arXiv:2608.15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence. Existing benchmarks focus on page-level element

Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

arXiv:2608.14578v1 Announce Type: new Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitud

LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures

arXiv:2608.15242v1 Announce Type: new Abstract: When a long-horizon agent execution fails, outcome-level evaluation reveals the unsuccessful result but not where the decisive error entered the traject

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

arXiv:2608.15388v1 Announce Type: new Abstract: Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hy

researcharxiv-cs-lg

Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment

arXiv:2608.14942v1 Announce Type: cross Abstract: This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluat

LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights

arXiv:2602.17510v2 Announce Type: replace-cross Abstract: We introduce LoRA-CRAFT (extbf{C}ross-layer extbf{R}ank extbf{A}daptation via extbf{F}rozen extbf{T}ucker), abbreviated CRAFT throughout, an e

Low Cost Two-Stage Fabric Defect Detection at the Edge

arXiv:2608.14727v1 Announce Type: new Abstract: Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small a

Local Ai

Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models

arXiv:2608.15156v1 Announce Type: cross Abstract: World models may predict the future without making clear which parts of their hidden state actually drive those predictions. We ask whether a small, d

local-aiarxiv-cs-ai

Applications

LUNG-KGMM: Knowledge-Guided Multimodal Learning for Lung Cancer Incidence Prediction

arXiv:2608.14657v1 Announce Type: cross Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single d

applicationsarxiv-cs-cv

M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection

arXiv:2608.14847v1 Announce Type: new Abstract: Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases.

researcharxiv-cs-lg

Machine Learning Approaches to Decoding Topological Quantum Codes

arXiv:2608.15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppres

researcharxiv-cs-lg

Applications

Macroeconomic Forecasting with Large Language Models

arXiv:2407.00890v5 Announce Type: replace-cross Abstract: This paper presents a comparative analysis evaluating the accuracy of Large Language Models (LLMs) against traditional macro time series forec

applicationsarxiv-cs-cl

Applications

Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling

arXiv:2608.15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks. However, the perf

applicationsarxiv-cs-lg

Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies

arXiv:2608.15748v1 Announce Type: new Abstract: A dual-representation flow-matching policy decodes each predicted motion into joint and end-effector spaces, and the residual between the two kinematica

safetyarxiv-cs-ro

MAPLE: MoE Adaptive Plug-and-play Layer-wise Expert allocation

arXiv:2608.15299v1 Announce Type: cross Abstract: Sparsely-activated Mixture-of-Experts (MoE) Transformers universally fix the same number of routed experts across all layers, a convention that ignore

Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

arXiv:2608.16281v1 Announce Type: new Abstract: Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper present

safetyarxiv-cs-ro

Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans

arXiv:2608.16514v1 Announce Type: cross Abstract: Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language

MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning

arXiv:2608.16715v1 Announce Type: new Abstract: In-context imitation learning enables few-shot policy generalization but struggles to maintain performance on unseen objects and novel scenarios. To add

safetyarxiv-cs-ro

MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

arXiv:2603.27959v3 Announce Type: replace Abstract: Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematica

Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning

arXiv:2608.15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the h

Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm

arXiv:2608.16177v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that operate equipment, execute instructions, and act inside institutional hierarchie

Measuring Reward Hacking and Reasoning-Answer Decoupling Under Position-Confounded Optimization

arXiv:2608.15445v1 Announce Type: new Abstract: When a reward is correct on every training example yet consistent with more than one goal, a model can acquire an unintended one, a failure known as goa

Measuring Structured Predictability in Neural Training Dynamics: A Cross-Regime Study

model-releasesarxiv-cs-lg

arXiv:2608.15483v1 Announce Type: new Abstract: Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than archit

Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling

arXiv:2602.18518v2 Announce Type: replace Abstract: Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user

safetyarxiv-cs-lg

Agents

MechMath: Sorrifier-Driven Formal Decomposition Workflow for Automated Theorem Proving

arXiv:2603.24465v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) and LLM-based agents have substantially improved the capabilities of automated theorem proving. Howe

agentsarxiv-cs-cl

MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration

arXiv:2601.23049v2 Announce Type: replace Abstract: Medical calculators are fundamental to quantitative, evidence-based clinical practice. However, their real-world use is an adaptive, multi-stage pro

MEDR: Query-Independent Frame Selection via Multi-Signal Event Modeling and Dynamic Rescoring

arXiv:2608.15058v1 Announce Type: new Abstract: Frame selection is a fundamental component of multimodal large language models, enabling long videos to be processed under limited visual-token and comp

MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling

arXiv:2608.14783v1 Announce Type: new Abstract: Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are repre

researcharxiv-cs-cv

Agents

MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories

arXiv:2608.16357v1 Announce Type: cross Abstract: Autonomous agents share a transport and can call each other's tools, but they cannot share what they know: no protocol lets two agents' memories recon

agentsarxiv-cs-ai

Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection

arXiv:2608.15277v1 Announce Type: new Abstract: Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring

researcharxiv-cs-cv

Mental Model Management: An Operator-Based Framework for LLM Memory

arXiv:2608.15451v1 Announce Type: new Abstract: Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual repres

Metaplasticity as adaptive gradient preconditioning for incremental learning

model-releasesarxiv-cs-lg

arXiv:2608.14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation proc

MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems

arXiv:2608.15006v1 Announce Type: cross Abstract: Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning. Some rec

MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations

arXiv:2608.15844v1 Announce Type: new Abstract: Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-c

safetyarxiv-cs-cl

MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery

arXiv:2602.12407v3 Announce Type: replace-cross Abstract: Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot