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Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading

arXiv:2607.08964v3 Announce Type: replace Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple

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Human DGX agent

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Long-horizon Video Generation with Slot Memory Addressed By Region Tokens

DGX agent

arXiv:2610.19257v1 Announce Type: new Abstract: Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while

researcharxiv-cs-cv

15 Oct 2026

Applications

Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models

arXiv:2512.22814v2 Announce Type: replace Abstract: Accurate long-range weather forecasting remains a major challenge for AI models, both because errors accumulate over autoregressive rollouts and bec

applicationsarxiv-cs-lg

Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration

arXiv:2610.20384v1 Announce Type: new Abstract: Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and ha

agentsarxiv-cs-cv

LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering

arXiv:2608.28281v2 Announce Type: replace Abstract: Loop Engineering is emerging as a practice for organizing development work around coding agents. Instead of writing each prompt by hand, practitione

LoopRPT: Reinforcement Pre-Training for Looped Language Models

arXiv:2603.19714v2 Announce Type: replace Abstract: Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-

Loose Lips: Understanding Privacy Leakage in Agentic Dialogue

arXiv:2610.14071v1 Announce Type: cross Abstract: As LLM agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue ha

Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models

arXiv:2610.19042v1 Announce Type: new Abstract: Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling. In this paper, we

researcharxiv-cs-lg

Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale

arXiv:2610.18522v1 Announce Type: new Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We tes

Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models

arXiv:2610.20479v1 Announce Type: new Abstract: Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read

safetyarxiv-cs-cl

Lotus: Learning to Understand Time-series by Learning to Forecast

arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervisio

LURE: a Phase-Labelled Synthetic Corpus of Online-Grooming Exchanges

arXiv:2610.15908v1 Announce Type: new Abstract: Phase-annotated conversational data is essential for studying the progression of online grooming of minors and for developing early warning tools. Howev

LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement

arXiv:2610.14777v1 Announce Type: new Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing a

MA-Bench: Measuring Operational Planning in Language Models

arXiv:2610.19138v1 Announce Type: cross Abstract: How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We

MADDIE: Multi-Agent Drug Discovery Engine

arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to m

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr

safetyarxiv-cs-lg

MagicGarment: Benchmarking Single-Image Standalone 3D Garment Reconstruction

model-releasesarxiv-cs-cv

arXiv:2610.19623v1 Announce Type: new Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layered

Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels

arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th

MakSafe: A Spatiotemporal Semantic Framework for Formal Specification and Compliance Checking of Road Vehicle Safety Rules

model-releasesarxiv-cs-cl

arXiv:2610.19735v1 Announce Type: new Abstract: Scenario-based validation is essential for automated road vehicles but cannot cover the full variability of openworld traffic. We present MakSafe, a for

Manaca-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge

model-releasesarxiv-cs-cl

arXiv:2610.20570v1 Announce Type: new Abstract: Brazilian Portuguese remains underserved by open-source language models, and the few that are safety-aligned rarely document what did not work or valida

Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents

arXiv:2610.17544v1 Announce Type: cross Abstract: Thought templates, skill libraries, agent memories and caches turn many reasoning steps into one. We call this reasoning contraction. A contracted mem

safetyarxiv-cs-ai

MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation

arXiv:2610.19827v1 Announce Type: cross Abstract: LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their

Mapping Arctic Polynyas from Sea Ice Concentration Data Using Hierarchical Partitioning and Differentiated Region Growing: Sensitivity to Threshold and Product Choices

arXiv:2610.19647v1 Announce Type: new Abstract: Accurately delineating Arctic coastal polynyas from sea ice concentration data remains challenging because of complex coastal geometry and spatially het

researcharxiv-cs-cv

MARS: Harmonizing Multimodal Convergence via Adaptive Rank Search

model-releasesarxiv-cs-lg

arXiv:2603.00720v2 Announce Type: replace Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation

MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification

arXiv:2610.17613v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordi

MasalBench: A Benchmark for Contextual and Cross-Cultural Understanding of Persian Proverbs in LLMs

model-releasesarxiv-cs-cl

arXiv:2601.22050v2 Announce Type: replace Abstract: In recent years, multilingual Large Language Models (LLMs) have become an inseparable part of daily life, making it crucial for them to master the r

Maximum-Entropy Exploration with Future State-Action Visitation Measures

arXiv:2603.18965v2 Announce Type: replace Abstract: Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by pro

safetyarxiv-cs-lg

MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations

arXiv:2610.19679v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool in

agentsarxiv-cs-lg

MCPDisCo: Seesaw-Mitigated Tuning for MCP-Native Agents

arXiv:2610.14956v1 Announce Type: new Abstract: The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a

Measuring and Reducing Unusable Replies of LLM Agents

arXiv:2610.19659v1 Announce Type: new Abstract: People delegate multi-step work to LLM agents and read only the reply that the agent hands back. Current evaluation scores task completion and the faith

agentsarxiv-cs-cl

Measuring Epistemic Humility in Multimodal Large Language Models

model-releasesarxiv-cs-cv

arXiv:2509.09658v3 Announce Type: replace Abstract: Hallucinations in multimodal large language models (MLLMs), where a model generates content inconsistent with the input image, pose significant risk

Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware

arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 o

Applications

Mechanistic Knobs in LLMs: Retrieving and Steering High-Order Semantic Features via Sparse Autoencoders

arXiv:2601.02978v3 Announce Type: replace-cross Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models

applicationsarxiv-cs-ai

MedHarnessBench: A Unified Benchmark for Healthcare Agent Evaluation

arXiv:2610.15714v1 Announce Type: new Abstract: Healthcare agents must transform longitudinal clinical evidence into reliable actions through heterogeneous tools and stateful workflows, conditions und

MemDuet: Learning Dual-Process Memory for Long-Context Reasoning

arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-of

safetyarxiv-cs-cl

Tutorials

MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection

arXiv:2610.15463v1 Announce Type: new Abstract: With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly con

tutorialsarxiv-cs-ai

MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis

arXiv:2610.19267v1 Announce Type: cross Abstract: Online memes are often carriers of unsafe content such as disinformation and propaganda. Their rapid evolution in response to news events and strong d

safetyarxiv-cs-cl

Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation

arXiv:2610.17763v1 Announce Type: new Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions and

Memory-Efficient Integer-Only Training of Deep Neural Networks

arXiv:2610.16298v1 Announce Type: new Abstract: Training neural networks in reduced precision can yield significant efficiency gains, yet all existing methods that scale to standard computer vision be

Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon

model-releasesarxiv-cs-lg

arXiv:2610.19844v1 Announce Type: new Abstract: Memory-efficient subspace optimization has emerged as a practical approach to reducing the memory cost of LLM training while preserving optimization per

MHWA: Multi-timescale Hierarchical World-Action Model

arXiv:2610.14727v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increas

Applications

MicroSDF: Microfacet-Driven Hybrid Neural SDFs for Mixed-Reflectance Surface Reconstruction

arXiv:2610.19681v1 Announce Type: new Abstract: Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces,

applicationsarxiv-cs-cv

MIND: Instruction-Conditioned Incremental Grounding for Robots

model-releasesarxiv-cs-ro

arXiv:2610.19262v1 Announce Type: new Abstract: Robots following persistent language instructions must decide which cached room judgments remain valid as their environment changes. We present MIND (Ma

MIND: Lightweight and Effective Memory Injection Defense for LLM Agents via Intent-Aware Information Bottleneck

arXiv:2607.28103v2 Announce Type: replace Abstract: Memory-augmented LLM-based agents are vulnerable to memory injection attacks: Agents may retrieve poisoned memory from attackers, which diverts thei

Minimax estimation of the pseudo-spectral gap of continuous Markov chains, with applications to empirical concentration bounds

arXiv:2610.20007v1 Announce Type: cross Abstract: We study the minimax estimation of the pseudo-spectral gap gamma_{ps} of a Markov chain from the observation of a single trajectory. Extending earlier

researcharxiv-cs-lg

MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces

arXiv:2610.19457v1 Announce Type: new Abstract: Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-q

safetyarxiv-cs-lg

MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision

model-releasesarxiv-cs-cv

arXiv:2610.12568v2 Announce Type: replace Abstract: Feed-forward 3D priors enable high-quality reconstruction from image streams but often lack reliable metric scale and suffer from long-term trajecto

Misinformation Propagation in Benign Multi-Agent Systems

arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work

agentsarxiv-cs-cl