Search: “benchmark”

Learning to Simulate Individuals from Macro Social Signals

arXiv:2610.07062v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses

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Learning What to Distill: Bilevel Top-K Token Selection for Self-Distillation in Large Language Models

DGX agent

arXiv:2610.07247v1 Announce Type: new Abstract: Large language models have shown strong reasoning capabilities, but their high inference costs make knowledge distillation an important approach for tra

safetyarxiv-cs-lg

7 Oct 2026

LFHE: Local-First Heuristic Evolution for Bounded Local Topology Search in Decentralized Learning with Non-IID Data

arXiv:2610.08176v1 Announce Type: new Abstract: Decentralized learning is highly sensitive to communication topology under non-IID data. Adaptive peer-selection methods can exploit local model informa

local-aiarxiv-cs-ai

Linear Bandits under Exact Sliding-Window Constraints

arXiv:2610.08745v1 Announce Type: new Abstract: We study linear bandits under exact sliding-window constraints, where every consecutive block of actions must belong to a prescribed feasible set. In th

safetyarxiv-cs-lg

Localize Any Object in X-Ray Security Scans without Human Annotation

arXiv:2610.07326v1 Announce Type: new Abstract: Universal object localization in X-ray security inspection is critical for automated threat detection in safety-critical venues. However, unlike everyda

local-aiarxiv-cs-cv

M3SunAgent: Monocular 3D Spatial Understanding Agent for Metric Depth Estimation and 3D Visual Grounding

arXiv:2610.07982v1 Announce Type: new Abstract: Monocular metric depth estimation and 3D visual grounding represent the two complementary cornerstones of monocular 3D spatial understanding (M3Sun), fr

Machine Learning for German Redispatch Forecasting under Data Delays and Temporal Distribution Shift

arXiv:2610.08337v1 Announce Type: cross Abstract: Public redispatch records provide empirical data for grid congestion forecasting, but delayed reporting, zero-inflated distributions, and temporal shi

MASKerade: Token-Routed Mask Experts for Dense-to-MoE Upcycling

arXiv:2610.07809v1 Announce Type: new Abstract: Sparsely activated Mixture-of-Experts (MoE) models increase model capacity without a proportional increase in per-token computation. Dense-to-MoE upcycl

MemCo: Memory-Centric Collaboration for Generalizing LLM Agents to Unseen Environments

arXiv:2610.07376v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate in interactive environments, where they need to make sequential decisions through observation, ac

Mistral Large 4 runs general-purpose agents that gather information and produce finished deliverables across complex workflows.

model-releasesmistral-ai--x

Mistral AI introduced **Mistral Large 4** in October 2026 as a general‑purpose agentic model capable of gathering information and producing final deliverables across complex workflows. The launch high

MoonGS: High-quality Representation of the Lunar Surface via Gaussian Splatting Using Robust Depth Features from Image Pairs

arXiv:2610.07110v1 Announce Type: new Abstract: High-quality 3D reconstruction of lunar terrain from sparse rover images is indispensable for autonomous lunar exploration, but remains challenging beca

More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding

arXiv:2610.04753v2 Announce Type: replace-cross Abstract: Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Spar

MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning

arXiv:2610.07662v1 Announce Type: new Abstract: Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation

safetyarxiv-cs-lg

Multi-Label Perceptual Bug Detection in Video Games using Deep Learning on Gameplay Footage

arXiv:2610.08593v1 Announce Type: new Abstract: Traditional approaches for automated bug detection in video games, such as manual testing, can be beneficial for the improvement of quality assurance, b

Nord-Parl-TTS: Finnish and Swedish TTS Dataset from Parliament Speech

arXiv:2509.17988v2 Announce Type: cross Abstract: Text-to-speech (TTS) development is limited by scarcity of high-quality, publicly available speech data for most languages outside a few high-resource

researcharxiv-cs-cl

Applications

OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception

arXiv:2610.07569v1 Announce Type: cross Abstract: Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping a

applicationsarxiv-cs-ai

Partially Observable Zero-shot coordination by Predicting Intention of Partner

arXiv:2610.08142v1 Announce Type: new Abstract: Zero-shot coordination in embodied settings requires acting while the partner is intermittently out of view, leaving existing methods with ambiguous par

Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell

arXiv:2610.07782v1 Announce Type: new Abstract: Decomposing long-context inference across cooperating agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache

Probabilistic Truly Unordered Rule Sets

arXiv:2401.09918v2 Announce Type: replace Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First,

PRUE: A Practical Recipe for Field Boundary Segmentation at Scale

arXiv:2603.27101v2 Announce Type: replace Abstract: Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field ma

Applications

QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting

arXiv:2610.06936v1 Announce Type: new Abstract: Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregular

applicationsarxiv-cs-lg

R2RI: A Multi-View Event and RGB Dataset for Robot-to-Robot Interaction

arXiv:2610.07117v1 Announce Type: new Abstract: Understanding and modeling interactions between autonomous agents is a fundamental challenge in robotics, with broad implications for collaborative syst

agentsarxiv-cs-cv

RA-MoWE: Workflow-Affinity Embeddings for Query Clustering and Agentic Workflow Generation

arXiv:2610.07851v1 Announce Type: new Abstract: Agentic workflows enable large language models (LLMs) to solve complex tasks by coordinating reasoning, tool use, and verification. However, a workflow

RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation

arXiv:2610.06932v1 Announce Type: new Abstract: Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy-

safetyarxiv-cs-cv

ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents

arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with st

RefRoute: Decoupling Conditioning Cost from References via Compact Residual Conditioning and Spatial Routing

arXiv:2610.07720v1 Announce Type: new Abstract: Multi-reference image generation requires preserving the appearance of multiple subjects while composing them into a coherent scene. However, existing d

Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective

model-releasesarxiv-cs-cl

arXiv:2610.07894v1 Announce Type: new Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insuf

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models

arXiv:2512.23073v2 Announce Type: replace-cross Abstract: Fine-tuning has become the dominant paradigm for adapting Vision-Language Models (VLMs), yet most approaches rely on explicit weight updates t

Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster.

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RoboCap: A New Platform for Egocentric Robot Learning

arXiv:2610.07217v1 Announce Type: cross Abstract: Despite its promise for scaling robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integratin

Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures

arXiv:2610.07485v1 Announce Type: new Abstract: We study estimating rare-event probabilities I = P(g(mathbf{X}) > gamma) with mathbf{X} sim N(oldsymbol{mu}, oldsymbol{Sigma}) and general g : R^d o R.

researcharxiv-cs-lg

RSJEV: Discriminative Remote Sensing Scene Classification with Multimodal Large Language Models

arXiv:2610.08539v1 Announce Type: new Abstract: Remote sensing scene classification is a fundamental task in Earth observation and geospatial analysis. Existing approaches mainly follow three paradigm

Running an open-source agent platform with no third party in the chain: what each piece needs (Ollama/vLLM, local embeddings, local OCR)

Disclosure first: I work at Vstorm and I build AgenticOS, an open-source (Apache-2.0), self-hosted platform for building AI agents in a browser. This post was drafted with an LLM's help and edited by

model-releasesr-ollama

Safeguarding LLMs via Model-Agnostic Latent Safety Signals from Dark Knowledge

arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks incl

safetyarxiv-cs-ai

SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis

arXiv:2610.08093v1 Announce Type: cross Abstract: Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-qu

local-aiarxiv-cs-ai

SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images

arXiv:2606.12671v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used to detect whether AI-generated images contain visible artifacts, yet their ability to analyze su

Scale-Invariant Training for Time Series Foundation Models

arXiv:2610.07324v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This settin

Hardware

Scaling Decision Optimization to 100 Million Variables and Beyond with mPDLP in NVIDIA cuOpt

NVIDIA cuOpt’s Multi‑GPU Primal‑Dual Hybrid Gradient (mPDLP) solver distributes linear programming problems across NVLink‑connected GPUs, cutting per‑GPU peak memory by up to six times and handling up

hardwarenvidia-developer

Hardware

ScanSTL: Parallel Robustness Evaluation for Signal Temporal Logic

arXiv:2610.07346v1 Announce Type: new Abstract: Repeated evaluation and differentiation of Signal Temporal Logic (STL) robustness can become a computational bottleneck in robot planning and control. S

hardwarearxiv-cs-ro

Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming

arXiv:2610.07846v1 Announce Type: cross Abstract: The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the sce

researcharxiv-cs-lg

Secure Speculative Decoding for Large Language Models

arXiv:2610.08678v1 Announce Type: cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the target model, by first using a smaller model, referred

safetyarxiv-cs-ai

Selective Critique for Cost-Aware LLM Agents in Long-Horizon Decision Making

arXiv:2610.07335v1 Announce Type: cross Abstract: Improving the reliability of large language model (LLM) agents in long-horizon decision-making remains a key challenge. When deployed as autonomous ag

local-aiarxiv-cs-ai

Selective Transfer of RL Updates for Visual Reasoning

arXiv:2610.08659v1 Announce Type: cross Abstract: Model merging provides a training-free way to transfer reasoning capabilities from language models to vision-language models (VLMs), but endpoint-base

Semantic Behavioral Watermarking: Paraphrase-Robust and Forgery-Resistant Provenance for LLM Agents

arXiv:2610.08668v1 Announce Type: cross Abstract: Behavioral watermarking embeds an owner identifier in an LLM agent's high-level action choices, giving provenance without touching output tokens. Prio

Sensor-Language-Action Models

arXiv:2610.08244v1 Announce Type: new Abstract: Sensors are useful not only for understanding the world but also for deciding what to do next. Existing sensor models however largely stop at perception

Skillful Data-Driven Subseasonal Soil Moisture Forecasting: Prospects and Limits for Flash Drought Prediction

arXiv:2610.07060v1 Announce Type: new Abstract: Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge

researcharxiv-cs-lg

SkillPoison: Progressive Skill Poisoning via Successful Experiences

arXiv:2610.07645v1 Announce Type: cross Abstract: Self-improving LLM agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this lea

Small Language Models for Smart Data Model Classification at the Edge: A Cost-Aware Hybrid Approach

arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and en