Search: “routing”

Smoothing the Top-k Exposure Boundary for Sparse Mixture-of-Experts

model-releasesarxiv-cs-cl

arXiv:2610.11575v1 Announce Type: new Abstract: Sparse Mixture-of-Experts models scale parameter capacity efficiently while maintaining a fixed compute budget per token. However, traditional training

9 Oct 2026

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Model Releases

AirGroundVLN: A Large-Scale Benchmark for Goal-Oriented Air-Ground Collaborative Vision-and-Language Navigation

DGX agent

arXiv:2610.10421v1 Announce Type: new Abstract: Goal-oriented Vision-and-Language Navigation (VLN) requires agents to locate and reach targets described in natural language without prescribed routes.

model-releasesarxiv-cs-ro

8 Oct 2026

Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillation through Teacher-Relative Shifts

arXiv:2610.10460v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) is used in two settings. In common-domain composition, several teachers score each student rollout from one

Hardware

Democratizing MoE inference on commodity GPUs with CoMoE

arXiv:2610.09424v1 Announce Type: cross Abstract: Deploying Mixture-of-Experts (MoE) models relies heavily on Expert Parallelism, which generates intense inter-GPU communication. Consequently, state-o

hardwarearxiv-cs-lg

Learning joint probabilistic weather forecasts from station observations alone

arXiv:2610.09898v1 Announce Type: cross Abstract: Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing Attention) learns j

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning

arXiv:2607.18130v2 Announce Type: replace Abstract: Finetuning methods for foundation models usually change weights, prompts, or hidden states, while leaving the residual topology fixed. We ask whethe

SpikingVLA: Asynchronous Spiking Vision-Language-Action Models

arXiv:2610.09710v1 Announce Type: new Abstract: ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of

researcharxiv-cs-cl

A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral

arXiv:2610.07084v1 Announce Type: cross Abstract: An inaccurate expert can still provide useful information after correction. We study online learning to defer in which the learner chooses an expert a

safetyarxiv-cs-lg

Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism

arXiv:2604.02691v2 Announce Type: replace Abstract: Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed

AnyBottle: A Recipe to Only Keep the Concepts You Really Need

arXiv:2610.08552v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) make predictions inspectable and intervenable by routing them through human-interpretable concepts, but originally requ

researcharxiv-cs-ai

Axiom Satisfiability of Linear Rewards in Alignment

arXiv:2610.06892v1 Announce Type: cross Abstract: Learning from human preference data is the dominant route to aligning language models with human values. In linear social choice, where rewards are li

CUSP: CUSUM-Governed Survival Hazard Alarms at the Perception Onset for Off-Road Navigation

arXiv:2610.07882v1 Announce Type: new Abstract: Off-road navigation exposes a robot to potentially hazardous terrain en route. Although learning-based navigation uses safety supervision to choose whic

safetyarxiv-cs-ro

Explainable Rule Mining of IPv6 Extension-Header Presence Patterns from Paired-Vantage Captures

arXiv:2610.08090v1 Announce Type: cross Abstract: IPv6 extension headers (EHs), such as fragmentation, segment routing, and in-situ telemetry, are operationally important yetwidely dropped in transit,

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

arXiv:2610.08358v1 Announce Type: cross Abstract: Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become

The Right Memory in the Wrong Context: Verifying Retrieval Admissibility in Long-Term Agent Memory

arXiv:2610.07309v1 Announce Type: new Abstract: Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates

Agents

Token-Efficient Multi-Agent Collaboration via System One-Guided Computational Division of Labor

arXiv:2610.08155v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have become a promising paradigm for complex information-seeking and reasoning tasks by ena

agentsarxiv-cs-ai

TSRN-RTVD: Real-Time Video Deblurring System

arXiv:2610.08230v1 Announce Type: new Abstract: As video capture moves to handheld and edge devices, motion blur from camera shake has become a pervasive degradation that lowers perceptual quality and

local-aiarxiv-cs-cv

Applications

Advectra: Asymmetric Latent Transport for Non-Stationary Physics

arXiv:2610.05098v1 Announce Type: new Abstract: Many latent neural operators represent input and output fields in a stationary latent chart. In particular, common latent routing mechanisms use fixed o

applicationsarxiv-cs-lg

Benchmarking Generative Trajectory Models for Active-Inference Control

model-releasesarxiv-cs-ro

arXiv:2610.05692v1 Announce Type: new Abstract: Learning from trajectory demonstrations offers a route to active-inference control of complex systems whose dynamics are difficult to model explicitly.

EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation

model-releasesarxiv-cs-cv

arXiv:2610.05418v1 Announce Type: cross Abstract: Most vision-language-action (VLA) models rely on current observations and lose task-relevant evidence once it leaves view, limiting performance on lon

Largest Rashomon sets of decision trees for robust contextual optimization

arXiv:2610.04385v1 Announce Type: cross Abstract: Many decision trees fit the same data almost equally well, yet they can route a query point to different leaves and induce different local empirical d

P3: Persistent Particle Planning for Constrained Diffusion Control

arXiv:2610.06002v1 Announce Type: cross Abstract: Diffusion models provide expressive priors over trajectories, but adapting these priors to test-time constraints requires maintaining feasibility and

PreAct-Nav: Agentic Reasoning Before Action for Urban Navigation

arXiv:2610.04916v1 Announce Type: new Abstract: Urban navigation requires embodied agents to pursue long-horizon goals through local decisions based on egocentric observations. However, existing agent

local-aiarxiv-cs-ro

Agents

RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation

Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selection strategies often

agentsapple-ml-research

Agents

Synergizing Drone Delivery Order Pooling and Road Network Monitoring through Monitoring-Task Orderization

arXiv:2610.05270v1 Announce Type: cross Abstract: This paper investigates the real-time dispatch of a shared drone fleet for on-demand food delivery and urban road network monitoring. We consider a co

agentsarxiv-cs-lg

Transfer-Stratified On-Policy Distillation for RL-Improved Reasoning Teachers

arXiv:2610.05974v1 Announce Type: new Abstract: Reinforcement learning can substantially improve a reasoning teacher, but it is unclear which of those improvements survive when the teacher supervises

safetyarxiv-cs-lg

Universal Test-Time Training

arXiv:2610.05484v1 Announce Type: cross Abstract: Recent Test-Time Training (TTT) architectures compress context into fast weights that are updated online and queried as memory. Existing TTT designs k

researcharxiv-cs-cv

TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control

arXiv:2610.02867v1 Announce Type: new Abstract: Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment

safetyarxiv-cs-ai

5 Oct 2026

Syntheses

Synthesis: Arxiv-Cs-Cl

synthesisarxiv-cs-clauto-generated

Auto-generated synthesis of 10865 entries about arxiv-cs-cl

4 Oct 2026

Anomaly Detection and Localization for the Pantograph-Catenary System

arXiv:2610.01721v1 Announce Type: new Abstract: Monitoring the Pantograph-Catenary System (PCS) provides insight into the health conditions of the pantograph and the railway infrastructure. Recent ind

safetyarxiv-cs-lg

From Task Mixtures to Specialized Experts

arXiv:2610.00580v1 Announce Type: new Abstract: In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data d

local-aiarxiv-cs-lg

Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident

arXiv:2610.00902v1 Announce Type: cross Abstract: One way to make AI systems safe is to shape what the system is: its objective and dispositions. We take a complementary route: treat the agents' chara

safetyarxiv-cs-ai

RISED: RubrIcs for agentic multi-environment Selection and sElf-Distillation

arXiv:2610.00979v1 Announce Type: new Abstract: Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing

local-aiarxiv-cs-ai

Applications

UniTrackPLA: Unified Panorama-Language-Action Model for Instruction-Guided Navigation and Dynamic Person Tracking

arXiv:2610.00878v1 Announce Type: cross Abstract: General-purpose embodied robots should support both navigation toward language-specified destinations and dynamic person tracking under arbitrary init

applicationsarxiv-cs-cv

ASENA: Self-evolving Agents for Embodied Navigation

arXiv:2609.39207v1 Announce Type: new Abstract: We present ASENA, an embodied agent system that connects general-purpose coding agents to robot sensing, computation, supervised execution, and persiste

safetyarxiv-cs-ro

Cascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure

arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources.

ID Balancing: Stable Training of Extremely Sparse MoE via PID-Based Load Control

arXiv:2609.39137v1 Announce Type: new Abstract: Scaling Large Language Models (LLMs) via Mixture-of-Experts (MoE) enables massive parameter growth with nearly constant per-token computation. However,

Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction

arXiv:2609.39757v1 Announce Type: cross Abstract: Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into

safetyarxiv-cs-cv

Scaling Laws for Looped Mixture of Experts

arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed pa

Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head

model-releasesarxiv-cs-cl

arXiv:2609.38832v1 Announce Type: new Abstract: Scaling attention parameters can improve language model quality, but retaining full token histories makes additional heads costly at long contexts. Furt

Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languages

arXiv:2609.38574v1 Announce Type: new Abstract: Large language models are frequently proposed as a route to AI-powered services for African communities, but they are least reliable exactly where the n

local-aiarxiv-cs-ai

Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection

arXiv:2609.38274v1 Announce Type: new Abstract: The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive

researcharxiv-cs-cl

Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening

arXiv:2609.38818v1 Announce Type: new Abstract: Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spo

safetyarxiv-cs-ai

Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants

arXiv:2609.35812v1 Announce Type: new Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. En

safetyarxiv-cs-cl

Better Behavioral Prediction, More Faithful Model Ablations? Evidence from Sequential Choice

model-releasesarxiv-cs-cl

arXiv:2609.36097v1 Announce Type: cross Abstract: Using predictive models to explain cognition requires more than accurate behavioral predictions. Input ablations offer an appealing route: remove info

Beyond Readability: Evaluating Task Information Recoverability

arXiv:2609.36957v1 Announce Type: new Abstract: Direct visual readability and task-information recoverability are different quantities. Failure to decode a target from a fixed observation need not eli

researcharxiv-cs-cv

Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark

arXiv:2609.37938v1 Announce Type: cross Abstract: Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggre

Hardware

Expanding AI Storage Access with NVIDIA cuObject and the NVIDIA SCADA Server SDK

NVIDIA has released its cuObject client and server libraries, providing RDMA‑based APIs that allow GPUs to access object storage directly without routing data through the host CPU. The xio‑sig consort

hardwarenvidia-developer