Search: “inference”

CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image

arXiv:2609.25654v1 Announce Type: cross Abstract: Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects i

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Safety

CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning

DGX agent

arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r

safetyarxiv-cs-lg

23 Sept 2026

DVA-Neurons: Design and Verification of Adaptive LIF Neurons: From Single-Neuron Dynamics to Multi-Neuron Spiking Networks

model-releasesarxiv-cs-ai

arXiv:2609.22775v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computation

Agents

Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs

arXiv:2609.24352v1 Announce Type: new Abstract: Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, a

agentsarxiv-cs-ai

On Basis Function Selection for Sparse Gaussian Process Regression

arXiv:2609.26624v1 Announce Type: cross Abstract: Sparse Gaussian processes achieve O(N) inference by replacing the kernel with an appropriate expansion in a fixed basis {phi_j} on the input space. Gi

Recursive self-improvement of AI research agents

arXiv:2609.26457v1 Announce Type: cross Abstract: AI agents are beginning to automate research and development across the AI stack, from improving training efficiency to optimizing inference. A natura

safetyarxiv-cs-lg

SPECTRA: Adaptive Execution of Speculative Decoding on a Runtime-Reconfigurable Tiled Architecture

arXiv:2609.24847v1 Announce Type: cross Abstract: LLM inference on edge devices is constrained by computational and memory resources, making efficient autoregressive decoding challenging. Speculative

researcharxiv-cs-ai

STAR-VAE: A Scalable Latent-Variable Transformer for Controllable Molecular Generation

arXiv:2511.02769v2 Announce Type: replace Abstract: Many molecular Transformers lack probabilistic latent variables for posterior inference and latent interpolation. We introduce STAR-VAE, a SELFIES-e

Local Ai

When Recursive Models Finish Computing

arXiv:2609.26487v1 Announce Type: new Abstract: Recursive models can continue updating their latent states beyond their nominal inference budget, so an incorrect output at that budget does not show wh

local-aiarxiv-cs-lg

When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning

model-releasesarxiv-cs-ai

arXiv:2603.02266v2 Announce Type: replace-cross Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Lan

Agents

A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents

arXiv:2601.12538v2 Announce Type: replace-cross Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) de

agentsarxiv-cs-cl

Acceptance-Aware Draft Model Training for Speculative Decoding

arXiv:2609.24150v1 Announce Type: new Abstract: Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to generate multiple candidate tokens that are

AdaMem: Adaptive Memory Token Allocation for Soft Compression in Retrieval-Augmented Generation

arXiv:2609.22100v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce dis

Hardware

AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows

arXiv:2609.22951v1 Announce Type: cross Abstract: Enterprise agentic systems that route every trajectory step to a frontier model waste 60-80% of their inference budget on subtasks that smaller models

hardwarearxiv-cs-cl

Algebraic Consistency Alone Does Not Certify Temporal Structure in Latent Action Models

arXiv:2609.23478v1 Announce Type: new Abstract: Latent action models infer a code for the transition between two frames of action-free video. Recent methods regularise this code to compose additively

researcharxiv-cs-cv

Applications

Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities

arXiv:2609.24595v1 Announce Type: new Abstract: Neural Cellular Automata (NCAs) are a new type of neural network architecture which enable accurate and robust inference at extremely small model sizes.

applicationsarxiv-cs-cv

Tutorials

Authorship identification under domain shift: a survey of stylistic measures and learned author representations

arXiv:2310.00436v2 Announce Type: replace Abstract: Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This surve

tutorialsarxiv-cs-cl

Local Ai

Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation

arXiv:2609.22162v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation i

local-aiarxiv-cs-cl

Block-Sparse Attention with Semantic-Geometric Decoupled Routing

arXiv:2609.22884v1 Announce Type: new Abstract: Long-context inference has become a defining capability of large language models, but exact dense attention remains costly due to its quadratic scaling

Calibrating Lightweight Sparse Autoencoder Feature Steering

arXiv:2506.12576v3 Announce Type: replace Abstract: Sparse autoencoders (SAEs) can enable inference-time topic steering by modifying latent feature activations, but existing steering methods often fai

Causal Bayesian Optimization: Foundations, Methods, and Applications

arXiv:2609.24112v1 Announce Type: cross Abstract: Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems w

Correlation-Aware Structured Pruning for Large Language Models

arXiv:2609.22131v1 Announce Type: new Abstract: Structured pruning is a promising approach for reducing the substantial inference costs of Large Language Models (LLMs) while maintaining hardware effic

COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning

model-releasesarxiv-cs-cl

arXiv:2609.22697v1 Announce Type: new Abstract: Recently, text-to-speech systems have made significant progress in speech expressiveness and controllability. However, the speaking style of generated s

Custom Named Entity Recognition and Topic Classification for Global Health Publications

arXiv:2609.24625v1 Announce Type: cross Abstract: How should natural language processing models be selected and adapted for global health literature in environments where annotated data and computatio

Fast Graph Laplacian Estimation using Effective Resistance

arXiv:2609.23668v1 Announce Type: cross Abstract: Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constrai

FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding

arXiv:2609.24433v1 Announce Type: new Abstract: Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-trai

safetyarxiv-cs-ro

H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache

arXiv:2609.24197v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification by

Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving

arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectori

MIRA: Real-Time Full-Duplex Human-Robot Interaction for Embodied Companions

arXiv:2609.24547v1 Announce Type: new Abstract: Real-time embodied companion interaction requires a robot to infer user intent from streaming speech, generate timely responses, and execute expressive,

safetyarxiv-cs-ro

NAVIR: Neuromorphic Audio-Visual Speech Recognition for Robust Human-Robot Interaction on Edge Hardware

arXiv:2609.24391v1 Announce Type: new Abstract: Voice-controlled interaction in industrial settings is hampered by acoustic noise, which severely degrades audio-only speech recognition. Audio-visual s

Not All Ranks Are Equal: Budget-Aware LoRA Merging Across Tasks

arXiv:2609.22237v1 Announce Type: new Abstract: Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging meth

On the Efficiency-Safety Dilemma in Large Reasoning Models

arXiv:2609.23587v1 Announce Type: new Abstract: Large reasoning models (LRMs) incur high inference costs, often mitigated by efficiency techniques like quantization and pruning. However, the impact of

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine

arXiv:2410.17021v2 Announce Type: replace Abstract: Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. Ho

TreeSpark: Calibrated, Load-Adaptive Draft Trees for Semi-Autoregressive Speculative Decoding

arXiv:2609.22098v1 Announce Type: new Abstract: Speculative decoding accelerates language-model inference by letting a cheap drafter propose tokens that the target model verifies in parallel. Recent b

Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis

arXiv:2609.21214v1 Announce Type: new Abstract: Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive

safetyarxiv-cs-ai

Accelerating Dense LLMs via L0-regularized Mixture-of-Experts

arXiv:2609.21672v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeabl

researcharxiv-cs-ai

Bayesian classification of astronomical spectra with class uncertainties

arXiv:2609.21694v1 Announce Type: cross Abstract: Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spec

Agents

MIRAGE: Multi-Perspective Creative Language Model Reasoning with Reinforcement Learning Guidance

arXiv:2609.21554v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have revolutionized artificial intelligence and how human interact with AIs. Despite impressive advancem

agentsarxiv-cs-cl

PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models

model-releasesarxiv-cs-cv

arXiv:2609.22040v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--pla

Hardware

RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models

arXiv:2609.20971v1 Announce Type: new Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation

hardwarearxiv-cs-ai

RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding

arXiv:2609.21827v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hie

Local Ai

Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features

arXiv:2609.22060v1 Announce Type: new Abstract: Traffic sign recognition (TSR) is an important perception task for autonomous driving and advanced driver-assistance systems, where a system must both l

local-aiarxiv-cs-cv

Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals

arXiv:2609.21450v1 Announce Type: new Abstract: Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains d

When Online Adaptation Hurts: Parameter-Frozen Test-Time Ensembling for Continual Medical Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.21412v1 Announce Type: new Abstract: Medical image segmenters often get worse when sites, scanner vendors, or protocols change. Continual test-time adaptation (CTTA) addresses this problem

Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control

arXiv:2609.20761v1 Announce Type: cross Abstract: World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the p

safetyarxiv-cs-lg

Applications

Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence

arXiv:2609.19965v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest s

applicationsarxiv-cs-cl

Chronicle: Cut-Point Replay for Regression Testing of LLM Agents

model-releasesarxiv-cs-ai

arXiv:2609.20625v1 Announce Type: cross Abstract: Large language model responses are non-deterministic, so failures in LLM agents are hard to reproduce: a failure depends on inference that is not bitw

CoMa: Contextual Massing Generation with Vision-Language Models

arXiv:2601.08464v2 Announce Type: replace-cross Abstract: Context-aware building massing is an important early-stage design task: given a site for buildings, a generated massing should not only fit th

researcharxiv-cs-ai