Search: “routing”

Distilling Physical Priors into Streaming World Models

arXiv:2608.07981v1 Announce Type: new Abstract: Streaming world models predict future visual states online while maintaining physically coherent dynamics over long horizons. However, their rollouts of

applicationsarxiv-cs-cv

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DocPure: Prompt-Free Unified Document Restoration via Degradation-Aware Structure-Guided Wavelet Modulation

DGX agent

arXiv:2608.09536v1 Announce Type: new Abstract: High-quality document images are pivotal for information archiving and downstream automatic processing. However, they are frequently compromised by dive

researcharxiv-cs-cv

11 Aug 2026

Agents

Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems

arXiv:2608.07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in adv

agentsarxiv-cs-ai

FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients

arXiv:2608.09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per

Geometry Beats Estimated Depth: RGB-Only Multi-Camera 3D Tracking under Sim2Real

arXiv:2608.07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth

Safety

KumbhDoot: A Scale-Ready, LLM-Bounded Architecture for Mass-Gathering Public-Service Assistants

arXiv:2608.07520v1 Announce Type: cross Abstract: Mass religious gatherings such as the Kumbh Mela concentrate tens of millions of people into a single region over a few weeks, producing intense, repe

safetyarxiv-cs-ai

LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction

arXiv:2602.23290v2 Announce Type: replace Abstract: Extracting routable road networks from satellite imagery requires accurate topology recovery beyond pixel-level segmentation. Recent methods decompo

applicationsarxiv-cs-cv

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608.07427v1 Announce Type: new Abstract: LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network anal

CyberForge: Verified Vulnerability Injection at Repository Level for Cybersecurity Agent Training

arXiv:2608.06471v1 Announce Type: cross Abstract: Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world s

HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification

arXiv:2608.07204v1 Announce Type: new Abstract: Scientific claim verification over a cited paper requires predicting the claim--paper relation and identifying the paragraphs that justify that predicti

Tutorials

LoRAScan: Detecting Backdoor Prompts in Low-Rank Adapters for Large Language Models via Down-Projection Activation Spikes

arXiv:2608.06795v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) enables efficient specialization and distribution of large language models through compact adapters. However, untrusted ada

tutorialsarxiv-cs-ai

RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs

arXiv:2608.07088v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) encode images as long visual token sequences, making prefilling and KV-cache storage expensive. Existing trai

Soft Redaction of Image Provenance via Zero-Knowledge Proofs

arXiv:2608.07063v1 Announce Type: cross Abstract: Content provenance standards, such as C2PA, are increasingly used to attach signed records of origin, editing history, and rights to digital images. H

Constraint-First Reasoning: A Training-Free Protocol for Exploiting Answer-Space Constraints in Mathematical Problem Solving

arXiv:2608.05254v1 Announce Type: new Abstract: Large language models can derive a plausible mathematical object yet still violate explicit requirements--for example, by omitting a modular reduction,

Local Ai

EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding

arXiv:2608.05303v1 Announce Type: cross Abstract: On-device deployment of Large Language Models (LLMs) has become essential for personalized edge applications. A primary bottleneck is external memory

local-aiarxiv-cs-cl

OrchestraBench: Evaluating Multi-Agent Orchestration Failure Modes, Recovery, and Decomposition Quality

arXiv:2608.05263v1 Announce Type: new Abstract: Multi-agent orchestration frameworks are moving from demos to production, yet benchmarks typically report task accuracy without diagnosing why a pipelin

StyleComposer: Training-Free Multi-Reference Style Composition

arXiv:2608.05213v1 Announce Type: new Abstract: The style of a painting is not monolithic: color, texture, and structure may come from different sources. Existing reference-guided methods transfer the

researcharxiv-cs-cv

Personalized Federated Sparse Adaptation of Time-Series Foundation Models

arXiv:2608.04695v1 Announce Type: cross Abstract: Federated adaptation of time-series foundation models (TSFMs) is attractive for building energy forecasting because meter data are private, distribute

6 Aug 2026

Talk2Sensors: 3D Visual Grounding in Autonomous Driving via Sensor-Adaptive Physical Cue Matching

model-releasesarxiv-cs-cv

arXiv:2608.04568v1 Announce Type: new Abstract: As a key capability for embodied intelligence, 3D visual grounding (3DVG) has been predominantly studied in indoor scenes with RGB-D or point-cloud inpu

6 Aug 2026

Agents

When Shared Rollouts Fail in Defensive Driving Evaluation: A NAVSIM Score Basis Audit

arXiv:2608.04896v1 Announce Type: new Abstract: Defensive driving scores are useful only when they preserve distinctions between policies that observe surrounding actors and those that do not. Re-simu

agentsarxiv-cs-ai

6 Aug 2026

Hardware

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding

arXiv:2608.02989v1 Announce Type: cross Abstract: Speculative decoding verifies a tree of draft tokens in one target-model forward pass. For a mixture-of-experts (MoE) target, however, parallel verifi

hardwarearxiv-cs-cl

CrossScope: A Role-Asymmetric World Model for Joint Dual-Scope Surgical Video Prediction

model-releasesarxiv-cs-cv

arXiv:2608.03211v1 Announce Type: new Abstract: Visual world models typically learn future dynamics from a single observation stream, limiting their ability to model cooperative systems with multiple

FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations

arXiv:2608.03436v1 Announce Type: cross Abstract: Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topolo

researcharxiv-cs-lg

Local Ai

Keep the Needle, Prune the Haystack: Defect-Preserving Token Pruning for Efficient Zero-Shot Anomaly Detection

arXiv:2608.03681v1 Announce Type: new Abstract: Zero-shot visual anomaly detection has achieved remarkable progress, with recent vision-only approaches further improving performance while simplifying

local-aiarxiv-cs-cv

Reachability Is Not Realization: Tracing the Sources of LLM Benchmark Gains

arXiv:2608.03219v1 Announce Type: new Abstract: Benchmark gains are often treated as evidence of greater LLM capability. Yet the same gain can reflect different changes in model behavior. A model may

UniEvo-RS: Omni-Prompt Unified Remote Sensing Segmentation with Representative Exemplar-Driven Prototype Evolution

arXiv:2608.03911v1 Announce Type: new Abstract: Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from se

researcharxiv-cs-cv

BiKAN: Restoring Collapsed Basis of Binary Kolmogorov--Arnold Networks

arXiv:2608.01490v1 Announce Type: new Abstract: Binarizing a polynomial Kolmogorov--Arnold Network (KAN) not only changes parameter precision, but also alters the function space available to each laye

CARNet: Channel-Adaptive Receiver Network for Robust NextG Communications

arXiv:2608.02172v1 Announce Type: cross Abstract: Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static

researcharxiv-cs-lg

Don't Offer What Can't Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale

arXiv:2608.01050v1 Announce Type: cross Abstract: Production LLM agents that select from large skill libraries face a limitation that semantic relevance alone cannot resolve: a skill may match a user'

applicationsarxiv-cs-cl

Global Optimization and Inference-Time Region Grafting for Agentic Workflows

arXiv:2608.02353v1 Announce Type: new Abstract: Recent advances in agentic workflow optimization automate workflow design through task-specific workflow search or input-conditioned architecture select

Illuminating Visual Identity in Universal Multimodal Embeddings

arXiv:2608.01794v1 Announce Type: cross Abstract: Universal Multimodal Embeddings (UMEs) aim to unify various modalities and tasks into a shared representation space. In recent years, this field has w

Linear Multi-Timescale Retention as a Memory-Efficient Vision-Language Bridge

model-releasesarxiv-cs-cv

arXiv:2608.01614v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of So

LLM generation novelty through the lens of semantic similarity

arXiv:2510.27313v3 Announce Type: replace-cross Abstract: Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally

Revisiting Generalization Across Difficulty Levels: It's Not So Easy

arXiv:2511.21692v2 Announce Type: replace Abstract: We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and e

SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agentic Learning

arXiv:2608.00485v1 Announce Type: new Abstract: Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods u

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach

arXiv:2608.00030v1 Announce Type: new Abstract: Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query rem

TrimMoE A communication aware and adaptive depth framework for distributed edge inference

arXiv:2608.00573v1 Announce Type: cross Abstract: Serving Mixture-of-Experts (MoE) large language models across distributed edge servers is bottlenecked by the cross-server expert transmission. The ex

Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation

arXiv:2608.01676v1 Announce Type: new Abstract: Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific cont

Agentic Harness for Real-World Compilers

arXiv:2603.20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable

Cross-Resolution Semantic Learning for Graph Domain Adaptation

arXiv:2607.29365v1 Announce Type: new Abstract: Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing me

researcharxiv-cs-lg

Dense Temporal Contrast Synthesis via Conditioned Latent Transport

arXiv:2607.29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast ag

RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Images

arXiv:2607.28974v1 Announce Type: cross Abstract: The rapid advancement of image generation models has made it increasingly difficult for people to distinguish AI-generated images from real ones. To p

Safety

SAF-OPD: Stable Advantage Fusion for On-Policy Distillation

arXiv:2607.29209v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) broadcasts a single response-level reward to every token, while on-policy distillation (OPD) sco

safetyarxiv-cs-ai

CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance

arXiv:2607.28292v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in dynamic financial environments face a critical challenge: maintaining factual accuracy as market conditions, re

applicationsarxiv-cs-cl

Critical attention scaling in long-context transformers

arXiv:2510.05554v2 Announce Type: replace Abstract: As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity

Divergence Decoding: Training-Free Capability Fusion

arXiv:2607.27248v1 Announce Type: cross Abstract: While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(

Efficient LLMs with AMP: Attention Heads and MLP Pruning

arXiv:2504.21174v2 Announce Type: replace Abstract: Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Mod

Safety

Fidelity Is Not Safety: Gently-Compressed LLMs Pass Every Data-Free Quality Guard Yet Invent Procedure Steps in Agentic Execution

arXiv:2607.28196v1 Announce Type: new Abstract: Practitioners accept a compressed language model once it clears a stack of data-cheap quality guards: perplexity within a small factor of the original,

safetyarxiv-cs-cl