Search: “routing”

arXiv:2609.17910v1 Announce Type: new Abstract: We introduce Map2Route, a human-curated benchmark for compositional language-grounded route planning over pre-built semantic maps. Map2Route contains 1,

arXiv:2603.21913v2 Announce Type: replace Abstract: In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. Th

arXiv:2608.16384v1 Announce Type: new Abstract: Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specifi

arXiv:2608.03143v1 Announce Type: new Abstract: Vision-and-Language Navigation (VLN) requires an agent to follow a route-level instruction by executing its constituent steps from egocentric visual obs

arXiv:2510.14819v3 Announce Type: replace Abstract: Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time

arXiv:2606.01097v1 Announce Type: new Abstract: We describe Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R challenge. The method treats composed video retrieval as two coupled proble

arXiv:2605.08281v1 Announce Type: new Abstract: Implicit neural representations (INRs) encode images as neural-network weights, making image classification a problem of weight-space classifiability. A

arXiv:2604.26566v1 Announce Type: cross Abstract: Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumpti

arXiv:2610.19271v1 Announce Type: new Abstract: On-policy distillation provides dense supervision on the student's own samples, but its targets still come from the teacher. Verifier reinforcement lear

arXiv:2609.38936v1 Announce Type: cross Abstract: When a classifier is recalibrated from only a few thousand held-out examples, the capacity of the calibration map becomes a statistical design choice

arXiv:2609.37264v1 Announce Type: cross Abstract: Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separate

arXiv:2609.28322v1 Announce Type: new Abstract: Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on th

arXiv:2609.21316v1 Announce Type: new Abstract: Human-readable maps provide an intuitive interface for specifying robot destinations, but connecting their schematic geometry to egocentric observations

arXiv:2608.22644v1 Announce Type: cross Abstract: As the use of Artificial Intelligence (AI) and Large Language Models (LLMs) is becoming common in everyday applications, their ability to interpret na

arXiv:2606.14582v2 Announce Type: replace Abstract: Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations. It is very crucial particularly in het

arXiv:2608.11701v1 Announce Type: cross Abstract: Copying short-term memory (STM) into a slower store can preserve state across a context boundary, but persistence alone does not ensure that the retai

arXiv:2608.06031v1 Announce Type: new Abstract: Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, exist

arXiv:2607.22841v1 Announce Type: cross Abstract: We present DS@GT's submission to FinMMEval 2026 Task 1, a multilingual financial exam question answering benchmark spanning English, Spanish, Greek, C

arXiv:2607.13054v1 Announce Type: cross Abstract: Environmental monitoring with unmanned aerial vehicles (UAVs) requires route planning methods that maximize covered area while handling energy limits,

arXiv:2606.28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenario

arXiv:2606.14668v3 Announce Type: replace Abstract: Knowledge editing systems must update selected facts while preserving nearby but irrelevant behavior. This paper studies this problem in a memory-as

arXiv:2606.22325v1 Announce Type: new Abstract: Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to atte

arXiv:2606.21935v1 Announce Type: new Abstract: Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, mon

arXiv:2605.28654v1 Announce Type: new Abstract: Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated s

arXiv:2504.02993v2 Announce Type: replace-cross Abstract: In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we

arXiv:2605.08712v1 Announce Type: new Abstract: Action-conditioned surgical video generation is a critical yet highly challenging problem for robotic surgery. The core difficulty is that low-dimension

arXiv:2605.06918v1 Announce Type: cross Abstract: Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand

arXiv:2605.02124v1 Announce Type: new Abstract: Softmax-routed mixture-of-experts models approach hard routing as the temperature tends to zero, but this limit is singular near routing ties. This pape

arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR

arXiv:2610.14792v1 Announce Type: new Abstract: Online, same-day delivery requires operators to constantly update their routes, and the routing cost for deliveries increases with the number of custome

arXiv:2609.03501v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) architectures increase model capacity by combining a collection of expert predictors through input-dependent routing, while o

arXiv:2608.06396v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns useful for identifying task-relev

arXiv:2610.16625v1 Announce Type: new Abstract: Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-lay

arXiv:2610.09791v1 Announce Type: new Abstract: Head avatar modeling requires jointly optimizing multiple objectives with different dominant effects on geometry, appearance, and cross-view consistency

arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limi

arXiv:2610.06510v1 Announce Type: new Abstract: When searching for an object, people choose their next move by considering both likely target locations and places already explored. The current view ca

arXiv:2610.02813v1 Announce Type: new Abstract: Visuomotor imitation policies combine high-dimensional visual observations with compact signals such as proprioception, and their fusion topology determ

arXiv:2610.02091v1 Announce Type: cross Abstract: Despite progress in vision-language models, 3D spatial reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry

arXiv:2609.23979v1 Announce Type: cross Abstract: Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs con

arXiv:2609.05019v1 Announce Type: new Abstract: Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed. However, such pre-execution commitment

arXiv:2608.25277v1 Announce Type: new Abstract: Multi-agent LLM systems coordinate through natural-language messages that consume 40--60% of their token budget. Replacing these with structured graphs

arXiv:2608.15213v1 Announce Type: new Abstract: Crowd counting must recover reliable local density under severe variations in perspective, head scale, occlusion, and background clutter. Although moder

arXiv:2608.04183v1 Announce Type: new Abstract: When a language model follows an in-context conditional rule such as 'if P(x) then A else B,' does it assemble a runtime circuit with one module that te

arXiv:2608.03341v1 Announce Type: cross Abstract: Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially fo

arXiv:2607.29188v1 Announce Type: new Abstract: Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: p

arXiv:2607.29033v1 Announce Type: new Abstract: Existing methods for adapting 2D foundation models such as SAM to 3D volumes either process slices independently---ignoring inter-slice context---or req