Search: “inference”

WorldGraph: Graph-Native World Modeling

model-releasesarxiv-cs-cv

arXiv:2609.34159v1 Announce Type: cross Abstract: World models infer latent states of an environment to capture its underlying dynamics and predict future evolution. Many real-world environments, howe

29 Sept 2026

Local Ai

Zero-Storage Procedural Neural Synthesis via Boundary Dynamics: Formal Verification in Lean 4 and Bare-Metal Gauntlet Validation

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DGX agent

arXiv:2609.33066v1 Announce Type: new Abstract: Contemporary neural inference architectures rely on dense floating-point weight matrices stored in high-bandwidth memory (VRAM), incurring severe memory

local-aiarxiv-cs-lg

29 Sept 2026

Acoustic-to-Text KV Compression for Full-Duplex Speech Models

arXiv:2609.31224v1 Announce Type: cross Abstract: Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During l

An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed

safetyarxiv-cs-lg

Asymmetric Classifier-Free Guidance for Target-Speaker ASR

arXiv:2609.30476v1 Announce Type: cross Abstract: Target-speaker automatic speech recognition (TS-ASR) must identify and transcribe a desired speaker under varying overlap and noise conditions. These

researcharxiv-cs-cl

ContraFM-S2O: Flow Matching-Based One-step SAR-to-Optical Image Translation Model with Contrastive Learning

arXiv:2609.31378v1 Announce Type: new Abstract: In recent years, diffusion models and GAN-based models have become the mainstream approaches for SAR-to-optical image translation, owing to their advant

DualManip: Agentic Dynamic Manipulation via Dual-Path Semantic Reasoning and Geometric Adaptation

arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic

agentsarxiv-cs-ro

Local Ai

DyMD: Preserving Interaction Dynamics through Distribution Matching Distillation in Few-Step Video World Models

arXiv:2609.31349v1 Announce Type: cross Abstract: Large video diffusion models offer expressive priors for embodied prediction and learning, yet their many-step sampling remains costly for interactive

local-aiarxiv-cs-ai

Equation discovery with Bayesian tree-adjoining grammars

arXiv:2609.31368v1 Announce Type: cross Abstract: Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as

Factorized axis convolutional gated recurrent unit with dynamic adaptive pooling for remaining useful life prediction of rolling bearings

arXiv:2609.30972v1 Announce Type: new Abstract: Convolutional neural networks (CNN) are widely used to predict the remaining useful life (RUL) of rolling bearings from time-frequency representations (

From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning

arXiv:2609.30391v1 Announce Type: new Abstract: Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task infere

safetyarxiv-cs-lg

Governed Deduction: Policy-Grounded Premise Authorization Beyond Relevance

arXiv:2609.31029v1 Announce Type: new Abstract: Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorizati

GraphWrit3R: End-to-End 3D Scene Graph Writing

model-releasesarxiv-cs-cv

arXiv:2609.31595v1 Announce Type: new Abstract: 3D scene graphs provide a structured representation of complex environments by encoding objects, their semantic attributes, and the spatial and function

Guarded Gradient-Based Activation Steering of Shutdown Responses in Qwen3.5-0.8B: A Minimum-Step Policy

arXiv:2609.30326v1 Announce Type: new Abstract: Activation steering changes a model's internal activations during inference without updating its weights, but a useful intervention must determine both

HuPER: A Human-Inspired Framework for Phonetic Perception

arXiv:2602.01634v2 Announce Type: replace-cross Abstract: We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic-phonetics evidence and lingui

Metacognitive Selective Ensemble for Mobile Systems

arXiv:2609.31031v1 Announce Type: new Abstract: Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few

MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

arXiv:2609.30698v1 Announce Type: new Abstract: Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented metho

safetyarxiv-cs-cv

MoSAR: Mixture of Semantic Attention Regimes for Learning Adaptive and Approximable Attention Geometries

arXiv:2609.31261v1 Announce Type: cross Abstract: The quadratic complexity of dense self-attention remains a central bottleneck for long-context language modeling. Many efficient alternatives address

Predicting Transmembrane Protein Topology from 3D Structure

arXiv:2609.30446v1 Announce Type: new Abstract: This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on th

Reliability-aware Cross-sample Enhancement for Robust Multimodal Sentiment Analysis

arXiv:2609.30470v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) aims to infer human emotions from multiple modalities such as text, audio, and vision. In practice, inputs are often

SkillFlow: Scalable and Efficient Agent Skill Retrieval System

arXiv:2504.06188v3 Announce Type: replace Abstract: AI agents can extend their capabilities at inference time by loading reusable skills into context, yet equipping an agent with too many skills, part

Spectral Feedback for Test-Time Alignment of Protein Diffusion Models

arXiv:2609.30456v1 Announce Type: new Abstract: Reward maximization alignment methods for discrete diffusion models have primarily focused on steering the reverse process, either by influencing token

safetyarxiv-cs-ai

Strategically Diverse Sampling for Self-Training

arXiv:2609.31571v1 Announce Type: new Abstract: Many LLM training and inference methods, including RL and test-time scaling, depend on repeated sampling, but benefit only when the responses meaningful

researcharxiv-cs-cl

Applications

Towards VLA-Dreamer: Refining VLA Behavior Using World Models

arXiv:2609.31313v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs), while showing strong potential for robot control, require massive amounts of high-quality imitation learning dat

applicationsarxiv-cs-ai

Accelerating Video Diffusion via Training-Free Trajectory Routing

arXiv:2609.30096v1 Announce Type: cross Abstract: Video diffusion is computationally expensive, as it requires executing a large model across many denoising steps. Even with step-distillation, inferen

safetyarxiv-cs-ai

BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting

arXiv:2609.29268v1 Announce Type: new Abstract: Temporal knowledge graph forecasting aims to infer future relational facts from the temporal structure of observed events. Existing forecasters mainly s

Local Ai

Canopy: Exploiting Piecewise Smooth Tree Priors for Multi-Fidelity Bandits

arXiv:2609.30017v1 Announce Type: cross Abstract: Many LLM inference problems, including model routing, prefix-cache management, prompt trimming, and test-time search, can be viewed as optimization ov

local-aiarxiv-cs-ai

CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment

arXiv:2609.29109v1 Announce Type: new Abstract: Reasoning-capable language models often produce long chains of thought when direct answers suffice, wasting inference compute. Many dual-mode models lea

safetyarxiv-cs-ai

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

arXiv:2609.29812v1 Announce Type: new Abstract: Looped Transformers have attracted substantial attention as a parameter-efficient approach to increasing computational depth through repeated applicatio

HelloWorld: Towards Practical Applications of Generative Driving World Models

arXiv:2609.28931v1 Announce Type: new Abstract: Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. R

HEXIS: Compiling Skills into Extended Finite State Machines

arXiv:2609.30123v1 Announce Type: new Abstract: Agent skills provide reusable knowledge and instructions, yet agents must repeatedly infer how to apply them and which operation should follow. This cou

Hardware

KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization

arXiv:2609.30059v1 Announce Type: cross Abstract: Deep learning inference and training performance depends critically on GPU kernel efficiency. Modern compilers such as PyTorch Inductor automatically

hardwarearxiv-cs-ai

Tutorials

Learning New Words from Unlabeled Test Data in Automatic Speech Recognition

arXiv:2609.28877v1 Announce Type: cross Abstract: New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This

tutorialsarxiv-cs-cl

Neural Transport Nested Sampling

arXiv:2609.29413v1 Announce Type: new Abstract: Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in

OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis

arXiv:2511.19667v2 Announce Type: replace Abstract: OncoVision is a privileged-information training framework that uses mammography images and clinical features during training and performs inference

TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction

arXiv:2602.11700v2 Announce Type: replace-cross Abstract: Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference

The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning

arXiv:2609.29045v1 Announce Type: cross Abstract: Open-weight LLMs give downstream users control over the inference stack, but this flexibility can undermine post-release guarantees that sensitive kno

Thinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models

arXiv:2609.28682v1 Announce Type: new Abstract: Post-training hybrid reasoning models in NoThink mode has attracted growing interest as a way to improve performance while keeping inference fast. Howev

When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression

arXiv:2609.29875v1 Announce Type: new Abstract: Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions h

BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport

arXiv:2609.27615v1 Announce Type: new Abstract: In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text M

CereVLA: Cerebellum-Inspired Consequence-Aware Residual Governance for Efficient Vision-Language-Action Execution

arXiv:2609.27468v1 Announce Type: new Abstract: Action-chunked vision-language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accum

Inverse Problems Conditioned on Observation Ensembles: Applications and Methods

arXiv:2601.22029v2 Announce Type: replace Abstract: We introduce a new multivariate statistical problem that we refer to as the Ensemble-conditioned Inverse Problem (EIP). The aim of EIP is to invert

KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling

arXiv:2609.27294v1 Announce Type: cross Abstract: Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, p

Recursive Uncertainty-Gated Image Registration for Learning-based Algorithms

arXiv:2609.28081v1 Announce Type: cross Abstract: Conventional image registration algorithms are robust to domain shifts and achieve low errors, but they are slow and computationally expensive. Deep-l

Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving

arXiv:2609.28263v1 Announce Type: new Abstract: The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computation

Rolling Conformal Prediction in Sequential Model Training

arXiv:2609.26951v1 Announce Type: cross Abstract: We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training.

SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving

model-releasesarxiv-cs-cl

arXiv:2609.27717v1 Announce Type: new Abstract: Human-written agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather t

Stable Geometry with Divergent Task Evidence for Efficient Long-Horizon Agent Compression

arXiv:2609.27332v1 Announce Type: new Abstract: Long horizon agents accumulate growing interaction histories that increase context and inference costs. We find that geometric redundancy alone is an in