Search: “inference”

Belief-Aware Multi-Agent Path Finding under Map Uncertainty

arXiv:2609.40269v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static ob

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Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models

DGX agent

arXiv:2606.13603v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individua

researcharxiv-cs-ai

1 Oct 2026

Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating

arXiv:2609.38356v1 Announce Type: new Abstract: Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continua

Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning

arXiv:2609.39390v1 Announce Type: new Abstract: Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previ

EFormer: Temporally Aligned Local Correction for Continuous sEMG-Based Hand Pose Tracking

arXiv:2609.38932v1 Announce Type: new Abstract: Surface electromyography (sEMG) provides a wearable, camera-free signal for continuous hand-motion inference. Mapping muscle activity to joint kinematic

ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction

model-releasesarxiv-cs-cv

arXiv:2609.39624v1 Announce Type: new Abstract: Single-image HDR reconstruction requires inferring missing detail while preserving the visible content of an LDR image. Differences in sensor dynamic ra

GeoGAT: Bidirectional Temporal Sampling Meets Hierarchical Graph Attention for Global Video Geo-localization

model-releasesarxiv-cs-cv

arXiv:2609.39128v1 Announce Type: new Abstract: Global video geo-localization aims to infer the geographic location of a video worldwide, evaluating performance across four geographic hierarchies: cit

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm, b

MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs

arXiv:2609.40170v1 Announce Type: new Abstract: MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple fre

local-aiarxiv-cs-lg

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

model-releasesarxiv-cs-cl

arXiv:2609.39346v1 Announce Type: new Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SL

Privacy in Personalized AI Is a System Property, Not Just a Model Property

arXiv:2609.38289v1 Announce Type: cross Abstract: In personalized AI applications, such as conversational assistants and recommender systems, users interact not with models in isolation but with broad

Prototype-Rule Neurosymbolic Regularization for Rank-Constrained Tensor Neural Networks under Label Scarcity

arXiv:2609.40131v1 Announce Type: cross Abstract: Rank-constrained tensor neural networks reduce the parameterization of high-order inputs, but they do not explicitly constrain class geometry in the l

safetyarxiv-cs-cv

Provable Test-Time Scaling for Beam Search in LLM Reasoning

arXiv:2609.38672v1 Announce Type: cross Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning i

Ready2Blend: From Natural-Language Instructions to Composable Alignment Prompts

arXiv:2609.39365v1 Announce Type: cross Abstract: Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are fle

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

model-releasesarxiv-cs-ro

arXiv:2609.39056v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous s

Switching Linear Attention

arXiv:2609.39034v1 Announce Type: cross Abstract: Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achie

The Evolution of Attention in Large Language Models: Mechanisms, Trade-offs, and Emerging Trends

arXiv:2609.39661v1 Announce Type: new Abstract: Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value ca

researcharxiv-cs-cl

The Golden Path Hypothesis: Reusable Schedules in Diffusion Caching

arXiv:2609.39343v1 Announce Type: new Abstract: Diffusion caching accelerates generation by replacing transformer computation with cached or predicted features at selected denoising steps. We introduc

Applications

UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding

arXiv:2609.39972v1 Announce Type: new Abstract: Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters hav

applicationsarxiv-cs-cl

What Was Said, Not What Was 'Thought': Type-6 Logic for CoT Verification

arXiv:2609.38420v1 Announce Type: cross Abstract: We introduce Type-6 logic, a variant of dynamic epistemic logic augmented with two operators (uncertainty and recurrence), designed to model the infer

When the Right Answer Is Missing: An Arithmetic-Dependent Rejection Bottleneck in Jev

arXiv:2609.39496v1 Announce Type: cross Abstract: Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making workflows by selecting directly from predefined

researcharxiv-cs-cl

A foundation model for energy and radiation systems built on heterogeneous scientific interfaces

arXiv:2609.38067v1 Announce Type: new Abstract: Scientific foundation models are commonly evaluated after heterogeneous physical problems have already been translated into a compatible gridded, tokeni

Hardware

BASE: Batch-Aware Selection of Experts Using Predicted Removal Error for Efficient MoE Decoding

arXiv:2609.36222v1 Announce Type: new Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferring

hardwarearxiv-cs-lg

Can AI Scientists Change Their Minds? Prior-Evidence Conflict in Synthetic Universes

arXiv:2609.36726v1 Announce Type: new Abstract: Can a scientific agent distinguish a law it inferred from evidence from one it merely recognizes? We introduce Synthetic Universes, a controlled benchma

Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

arXiv:2609.37837v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly eli

safetyarxiv-cs-cl

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering

arXiv:2609.36570v1 Announce Type: cross Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that s

Data Unlearning via Inverse Distillation

arXiv:2609.36099v1 Announce Type: new Abstract: Multi-step matching models, including flow and diffusion models, produce high-quality outputs but incur substantial inference costs and may reproduce un

Demistifying Data and Simulator Assumptions in Supervised Causal Discovery

arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pair

DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance

arXiv:2609.36472v1 Announce Type: new Abstract: Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically

DScale: Scaling Block-Diffusion Speculative Decoding with Adaptive Verification

arXiv:2609.37532v1 Announce Type: cross Abstract: Growing large language model applications demand efficient inference. At high concurrency, block-diffusion speculative decoding suffers from verificat

End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link

arXiv:2609.37551v1 Announce Type: cross Abstract: Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an oppo

FastGuide: Accelerating Reward Guidance for Diffusion Large Language Models

arXiv:2609.36202v1 Announce Type: new Abstract: Gradient-based reward guidance provides a flexible way to use downstream reward models to control masked diffusion language models at inference time. Ho

researcharxiv-cs-cl

FedSocket: Recipient-Executable Knowledge Exchange for Heterogeneous Multimodal Federated Learning

arXiv:2609.37582v1 Announce Type: cross Abstract: Federated knowledge must remain usable by recipients with different modalities, private architectures, and tasks. We present FedSocket, which makes re

local-aiarxiv-cs-ai

FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents

arXiv:2609.37590v1 Announce Type: new Abstract: LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation fr

safetyarxiv-cs-ai

Agents

In-Context Learning for Robots: Methods and Applications

arXiv:2609.36012v1 Announce Type: new Abstract: General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL)

agentsarxiv-cs-ro

Applications

In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks

arXiv:2609.38173v1 Announce Type: new Abstract: We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its

applicationsarxiv-cs-ro

LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization

arXiv:2609.38166v1 Announce Type: cross Abstract: Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Atten

LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter

arXiv:2609.37029v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive inference by verifying multiple draft tokens in a single target forward pass. However, as the context

On Trajectory-Aware Training for Masked Diffusion Language Models

arXiv:2609.37974v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The mo

local-aiarxiv-cs-ai

Hardware

Parameterized Stripe Attention for Efficient Video Generation

arXiv:2609.37001v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) enable high-quality video generation but suffer from substantial inference latency, primarily attributable to the comput

hardwarearxiv-cs-ai

PE-OPSD: Internalizing Prompt Enhancement into Flow-matching Models via On-Policy Self-Distillation

arXiv:2609.36638v1 Announce Type: cross Abstract: Text-to-image users often provide concise and underspecified prompts, whereas generative models benefit from detailed textual conditions for reliable

safetyarxiv-cs-cv

Periodic Weak Spots: Phase Sensitivity from Chunked KV-Cache Compression

arXiv:2609.36322v1 Announce Type: cross Abstract: Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer

PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units

arXiv:2603.15106v2 Announce Type: cross Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requir

researcharxiv-cs-cv

Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment

arXiv:2609.36540v1 Announce Type: new Abstract: Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can conflict

safetyarxiv-cs-ro

Reliability Testing of Medical Model Performance under Distributed Deployment

arXiv:2609.36525v1 Announce Type: cross Abstract: Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Althou

ReLOBGen: Replayable Limit Order Book Message Generation

arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-l

Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs

arXiv:2609.36916v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention

researcharxiv-cs-cv

RS-OPSD: Reliable Privileged On-Policy-Self-Distillation for Ultra-High-Resolution Remote Sensing VQA

arXiv:2609.38072v1 Announce Type: new Abstract: Ultra-high-resolution (UHR) remote sensing visual question answering (VQA) requires models to resolve small visual evidence within extremely large image

safetyarxiv-cs-cv