Search: “inference”

AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning

arXiv:2610.03223v1 Announce Type: cross Abstract: Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contributi

local-aiarxiv-cs-cl

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Model Releases

Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI

DGX agent

arXiv:2610.02369v1 Announce Type: cross Abstract: Human-computer interaction (HCI) is in the middle of a transition: large language models can now generate functional user interfaces (UIs) on demand f

model-releasesarxiv-cs-ai

5 Oct 2026

Bridging Frontier Reasoning and Robot Execution: From Autonomous Demonstration Generation to Dense Language Supervision

arXiv:2610.03615v1 Announce Type: new Abstract: Recent advances in frontier models enable robot manipulation from only a few demonstrations, but high inference latency limits their use for real-time r

local-aiarxiv-cs-ro

Capability Scaling-Down Laws for LLM Compression

arXiv:2610.02462v1 Announce Type: cross Abstract: LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable

researcharxiv-cs-cl

Safety

DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift

arXiv:2610.03390v1 Announce Type: cross Abstract: Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step te

safetyarxiv-cs-ai

ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation

arXiv:2610.02538v1 Announce Type: cross Abstract: Inference-time control steers a pretrained generative model towards a target distribution without retraining. We study tilted targets pi_0propto G_0,p

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

arXiv:2610.03474v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches as

local-aiarxiv-cs-cv

Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems

arXiv:2610.02377v1 Announce Type: cross Abstract: Sampling from the posterior is the central task of computational Bayesian inverse problems. The standard workhorse in Bayesian inference - Markov chai

Foresight: planning future perception in streaming VLMs without retraining

arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed t

tutorialsarxiv-cs-ai

Hardware

Hardware-Native Joint Sparse-Quantization for Trillion-Scale Mixture-of-Experts

arXiv:2610.02241v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures allow frontier language models to scale to trillions of parameters, but their deployment is constrained by mass

hardwarearxiv-cs-ai

HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning

model-releasesarxiv-cs-cl

arXiv:2610.03039v1 Announce Type: new Abstract: Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high infere

Invariance of Clustering Operations in Causal Effect Identification

arXiv:2610.03101v1 Announce Type: cross Abstract: Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial c

KV^2: A Self-Refining KV Cache

arXiv:2610.03198v1 Announce Type: new Abstract: The memory footprint of the key-value (KV) cache constrains the practical use of long-context models, and it dominates cost when one prefilled context m

Neuron merging via inverse-activation regression for post-training compression of sigmoid neural networks

arXiv:2610.02559v1 Announce Type: new Abstract: As neural networks continue to grow in scale, model compression is becoming increasingly important for efficient inference under limited computational r

PaMIR: Open Benchmark of Public Credit-Default Datasets

arXiv:2610.03259v1 Announce Type: new Abstract: We release PaMIR (Public Arrival-ordered Measurement for Inference in Risk), an open benchmark for credit-default prediction when labels are scarce and

Power-SMC: Low-Latency Sequence-Level Power Sampling for Training-Free LLM Reasoning

arXiv:2602.10273v3 Announce Type: replace-cross Abstract: Reasoning ability in large language models is often attributed to distribution sharpening: concentrating output probability on high-likelihood

Applications

Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation

arXiv:2610.02968v1 Announce Type: new Abstract: Large language models (LLMs) can infer user preferences from interaction histories and reviews, yet the rationales they generate may not reflect the inf

applicationsarxiv-cs-ai

Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.02270v1 Announce Type: cross Abstract: Medical vision-language model (VLM) evaluation is sensitive to workflow design, prompting strategy, and benchmark construction, yet most studies treat

Hardware

Tailoring the Quantization Space for 1-Bit KV Cache Compression

arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidt

hardwarearxiv-cs-ai

The Geometry of Knowledge Accessibility in Large Language Models

arXiv:2610.03052v1 Announce Type: new Abstract: Large language models (LLMs) contain broad knowledge, but they cannot access all of it reliably. We study this problem through knowledge accessibility,

researcharxiv-cs-cl

Safety

VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models

arXiv:2610.03218v1 Announce Type: new Abstract: Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and inference. They combine low-precision elements with sh

safetyarxiv-cs-cv

ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework

arXiv:2512.04888v4 Announce Type: replace Abstract: Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detectio

local-aiarxiv-cs-cv

Benchmarking Generative Models for Weather Data Assimilation on Real Station Observations

arXiv:2610.00728v1 Announce Type: cross Abstract: Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecast t

Hardware

CAST: Cost-Aware Speculative Trees from One-Pass Block Drafters

arXiv:2610.00321v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting future tokens cheaply and verifying them with the target model in parallel

hardwarearxiv-cs-lg

Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning

arXiv:2610.00296v1 Announce Type: cross Abstract: Token-level certainty is widely used as a proxy for correctness in LLM training and inference. However, the performance of certainty-based methods dep

local-aiarxiv-cs-ai

Constant-Memory Recall: Learned Associations in a Fixed Matrix State

arXiv:2610.00232v1 Announce Type: new Abstract: Fixed-size recurrent memory limits storage growth during inference, but successful recall depends on the task and training. We study a small DeltaNet va

Distillation of Tabular Foundation Models into Efficient Predictors

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in

tutorialsarxiv-cs-lg

Distilling LLM Reasoning into Graph of Concept Predictors

arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active d

DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation

model-releasesarxiv-cs-cv

arXiv:2601.22153v2 Announce Type: replace-cross Abstract: Manipulating dynamic objects remains an open challenge for Vision-Language-Action (VLA) models. Although recent VLAs generalize well in static

EchoPress: Query-Agnostic KV Cache Pruning via Virtual Context Reconstruction

arXiv:2610.00412v1 Announce Type: new Abstract: KV cache pruning reduces long-context inference memory usage by evicting less important key-value pairs. KVzip estimates importance through context reco

FocusGraph: Graph-Structured Frame Selection for Embodied Long Video Question Answering

arXiv:2603.04349v2 Announce Type: replace Abstract: Understanding long videos is crucial for embodied intelligent agents, as their performance depends on effectively accumulating and using long-horizo

researcharxiv-cs-cv

Heteroskedastic Canonical Polyadic Tensor Decomposition

arXiv:2610.00498v1 Announce Type: cross Abstract: When minimizing the squared-error loss, the popular CP decomposition can be interpreted as parameter inference in a Gaussian model with a low-rank mea

HHR: Hierarchical Hash Retrieval for Efficient LLM Generation

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval off

How Much Can Language Models Gain from Test-Time Computation?

arXiv:2610.01110v1 Announce Type: new Abstract: How much can test-time computation improve a language model, and at what cost? Test-time scaling is widely proposed as a substitute for larger models, b

IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs

arXiv:2610.00426v1 Announce Type: new Abstract: We introduce IrekoGPT, a post-hoc method for converting pretrained LLMs into slimmable models whose width can be adjusted at inference time. Building on

Kepler: Auditable World Models for ARC-AGI-3

arXiv:2610.00834v1 Announce Type: new Abstract: ARC-AGI-3 evaluates agents in interactive environments whose rules and objectives must be inferred from observation. We present Kepler, an open-source h

Prediction-powered Neural Architecture Search

arXiv:2610.01317v1 Announce Type: new Abstract: Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are lim

tutorialsarxiv-cs-lg

ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting

arXiv:2610.01320v1 Announce Type: new Abstract: Generative modeling has shown strong promise for multivariate time mseries (MTS) forecasting, especially scale to high-dimensional settings. Diffusion-b

RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

arXiv:2610.01780v1 Announce Type: new Abstract: A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the

Agents

Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

arXiv:2508.08882v5 Announce Type: replace Abstract: Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-int

agentsarxiv-cs-ai

RelationVGGT: Visual Geometry Transformers for 3D Spatial Relation Segmentation

arXiv:2610.00970v1 Announce Type: cross Abstract: Recent advances in 3D reconstruction have progressed from per-scene optimization to feed-forward inference, and semantic scene understanding has follo

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

arXiv:2610.01140v1 Announce Type: new Abstract: Sampling multiple solutions spends computation on intermediate deductions and unfinished arguments as well as final answers. We introduce ReSolve, a tra

Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM Urban Sensing

arXiv:2610.00031v1 Announce Type: new Abstract: Street-view imagery is increasingly used to infer urban attributes, but predictive accuracy alone does not reveal how much a photograph contributes beyo

tutorialsarxiv-cs-cv

STATERA: Hidden Mass Estimation via Zero-Shot Sim-to-Real Kinematics using Frozen Temporal Tubelets

arXiv:2610.00003v1 Announce Type: cross Abstract: Vision models pretrained for frame-level appearance often struggle to infer hidden physical properties from motion. We study center-of-mass (CoM) loca

When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization

arXiv:2610.00654v1 Announce Type: new Abstract: Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historic

Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration

arXiv:2603.01623v2 Announce Type: replace Abstract: Diffusion models have become the dominant tool for high-fidelity image and video generation, yet are critically bottlenecked by their inference spee

researcharxiv-cs-cv

1 Oct 2026

Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

arXiv:2609.39183v1 Announce Type: new Abstract: This paper studies extbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target

researcharxiv-cs-cv

1 Oct 2026

Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering

arXiv:2609.38962v1 Announce Type: new Abstract: Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to esti