Search: “inference”

From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles

arXiv:2609.36406v1 Announce Type: new Abstract: Cell Painting is a high-content morphological profiling assay widely used for phenotype-based biological inference, with mechanism of action (MOA) predi

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GleanVID: Complementary Token Selection for Efficient Video Large Language Models

DGX agent

arXiv:2609.37042v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) have achieved strong video understanding capabilities but incur substantial inference overhead due to the large

local-aiarxiv-cs-cv

30 Sept 2026

IronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence

arXiv:2609.36860v1 Announce Type: new Abstract: We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architectu

Learning via Self-Consistency for Diffusion-based Video Reasoning

arXiv:2609.36826v1 Announce Type: new Abstract: Video generation models have demonstrated emerging zero-shot capabilities for visual reasoning, perception, and other vision tasks. However, diffusion-b

researcharxiv-cs-cv

More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses

arXiv:2609.35873v1 Announce Type: new Abstract: Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated

tutorialsarxiv-cs-ai

Purlin: Separating Orchestration from the Datapath of Collectives

arXiv:2609.36954v1 Announce Type: cross Abstract: Distributed inference depends on GPU collective communication that must keep pace with evolving hardware and specialized workloads. However, existing

hardwarearxiv-cs-ai

Applications

Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning

arXiv:2609.34270v1 Announce Type: new Abstract: Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete

applicationsarxiv-cs-ro

Do World Models Learn Global Understanding?

arXiv:2609.34058v1 Announce Type: new Abstract: AI systems often feel brittle and fragmented. A large language model (LLM) may correctly explain a concept but fail to apply it, or follow safety instru

Generative Priors Conditioned on Natural Language for Bayesian Inversion in PDEs

arXiv:2609.32941v1 Announce Type: new Abstract: Inferring quantities of interest (QoI) from data is a central task in Science and Engineering. In such contexts, we often have access to both quantitati

tutorialsarxiv-cs-lg

Instance-Adaptive Prompts as Context for Time-Series Foundation Models

arXiv:2609.34786v1 Announce Type: new Abstract: Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual

OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming

arXiv:2609.34653v1 Announce Type: cross Abstract: On-device streaming omni-modal inference safeguards user privacy and eliminates prohibitive per-token API costs, but faces a critical bottleneck: the

P4Q: Co-designing Token Pruning and Quantization for Vision-Language Model Acceleration

arXiv:2609.34867v1 Announce Type: new Abstract: Vision language models have achieved strong performance across a wide range of multimodal applications, yet their substantial computational and memory c

researcharxiv-cs-cv

Agents

PReCache: Efficient KV Cache Sharing for Multi-LoRA Agents via Low-Rank Precomputation and Neutral Reconstruction

arXiv:2609.34054v1 Announce Type: new Abstract: Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the growing s

agentsarxiv-cs-lg

Prioritizing Repeated LLM Evaluation for Hidden Failure Discovery

model-releasesarxiv-cs-lg

arXiv:2609.32547v1 Announce Type: new Abstract: Large language models are commonly evaluated by generating a small number of stochastic responses for each prompt in a benchmark. Because inference budg

SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models

arXiv:2609.34044v1 Announce Type: new Abstract: Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pr

safetyarxiv-cs-cv

TerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge

model-releasesarxiv-cs-lg

arXiv:2609.33548v1 Announce Type: new Abstract: Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient

Token-Disentangled Latent Test-Time Scaling for Vision-Language Reasoning

model-releasesarxiv-cs-cv

arXiv:2609.35228v1 Announce Type: new Abstract: Latent test-time scaling improves reasoning by refining hidden states during inference, but existing methods typically apply a single scalar reward to a

You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement

arXiv:2609.33609v1 Announce Type: new Abstract: In-context learning (ICL) is crucial for boosting the inference performance of large language models (LLMs). However, the effectiveness of ICL in LLMs i

Agents

ActKV: Efficient LLM Agents through Action-Guided KV Cache Management

arXiv:2609.31395v1 Announce Type: cross Abstract: Agentic LLM inference accumulates long KV caches across iterative observation-reasoning-action loops, imposing substantial memory overhead and limitin

agentsarxiv-cs-ai

FRESHLATENT: Channel-Aware Latent Adaptation for Resource-Constrained Embodied VLM Perception

arXiv:2609.30629v1 Announce Type: cross Abstract: Mission-critical UAVs increasingly rely on split vision-language model (VLM) perception under tight onboard-resource and wireless-communication constr

hardwarearxiv-cs-cv

PTC-Decoder: Towards Intelligent SLMs on Offline Resource-Constrained Edge Devices

arXiv:2609.30836v1 Announce Type: new Abstract: Deploying small language models (SLMs) on offline, resource-constrained edge devices such as remote sensing satellites presents a fundamental challenge:

Softmax Reparameterization for Output-Head Quantization

arXiv:2609.31291v1 Announce Type: cross Abstract: Large vocabularies make output heads a substantial inference cost in small language models. We propose softmax reparameterization, a post-training met

Towards Universal Representation-Based Process Control

arXiv:2609.30790v1 Announce Type: new Abstract: Many temporal process learning and monitoring pipelines operate in local windows, making window-level decisions unavoidable in practice. In such setting

UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification

arXiv:2605.06221v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer conte

researcharxiv-cs-cl

User Model Extraction via Belief Self-Distillation

arXiv:2609.31603v1 Announce Type: cross Abstract: Large language models (LLMs) implicitly infer attributes of their users and adapt their behavior accordingly, yet these beliefs remain difficult to in

safetyarxiv-cs-cl

Beyond Spatial Benchmarks: From Spatial Reasoning to Navigation

model-releasesarxiv-cs-cv

arXiv:2609.29934v1 Announce Type: new Abstract: Does progress on spatial reasoning benchmarks translate into better navigation? Existing benchmarks test isolated inferences from images or videos, with

25 Sept 2026

FounRef: Robust, Structure-Preserving, and Fast Metric Refinement of Frozen Monocular Foundation Priors with Sparse Anchors

arXiv:2609.29224v1 Announce Type: new Abstract: Dense metric depth from cameras is essential to real-world 3D applications, yet achieving accuracy, faithful surface geometry, and fast inference simult

local-aiarxiv-cs-cv

25 Sept 2026

The Domestic Unprotected Zone: Algorithmic Governance and the Reproduction of Perpetrator Discourse in Conversational AI

arXiv:2609.28479v1 Announce Type: cross Abstract: Conversational AI increasingly mediates intimate-partner communication, and refusal logic at the inference layer now functions as a governance thresho

25 Sept 2026

Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding

arXiv:2609.27678v1 Announce Type: new Abstract: Contract inference requires multiple judgments about a shared document, but aggregate accuracy can conceal changes in the individual decisions. Repeated

24 Sept 2026

Capability-Aware Arbitration for Semantic Intent-Based Shared Control

arXiv:2609.25369v1 Announce Type: new Abstract: Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumptio

safetyarxiv-cs-ro

Et Tu, Brute? Economic Misalignment in Personal AI Agents

arXiv:2609.24927v1 Announce Type: new Abstract: Personal AI agents make recommendations and take actions on people's behalf in high-stakes economic contexts, e.g., buying a flight, choosing health ins

Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs

arXiv:2609.26796v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text gene

hardwarearxiv-cs-cl

Learning 3D biophysical cell properties from 2D images and cell-population statistics

arXiv:2609.22410v1 Announce Type: new Abstract: Inferring 3D cellular properties from 2D microscopy is difficult when a reference instrument reports only population statistics rather than labels for i

local-aiarxiv-cs-ai

TSS: Target-Side Sparsification for Speculative Decoding in Domain-Specific Large Language Models

arXiv:2609.26100v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference through collaboration between a lightweight draft model and a target verifier. Existing

Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation

arXiv:2603.11984v2 Announce Type: replace Abstract: Diffusion-based visuomotor policies model complex action distributions through iterative denoising, but repeated inference adds latency to robotic c

safetyarxiv-cs-cv

ARM: Attention with Routed-Memory for Learnable Sparse Control

arXiv:2609.24417v1 Announce Type: new Abstract: Despite advances in long-context inference, large language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are

safetyarxiv-cs-lg

AVTR-1: Open Stack for Real-Time Interactive Avatars

model-releasesarxiv-cs-cv

arXiv:2609.22913v1 Announce Type: new Abstract: Talking-head and dyadic models now achieve real-time inference, yet fast motion generation alone does not produce an interactive conversation. A live sy

Bayesian Deck-of-cards-based Ordinal Regression with Sequential Preference Elicitation

arXiv:2609.23212v1 Announce Type: cross Abstract: The Deck-of-cards-based Ordinal Regression (DOR) infers a value function from a ranking of reference alternatives in which the Decision Maker (DM) ins

tutorialsarxiv-cs-lg

BEACON: Belief-Enabled Adaptive CONtrol for Imitation Learning under Uncertainty

arXiv:2609.22730v1 Announce Type: new Abstract: Robot manipulation tasks often involve hidden state information that cannot be directly observed and must be inferred through sequential physical intera

safetyarxiv-cs-ro

Efficient Reasoning Exploration via State-Conditioned Latent Steering with Progress Guidance

arXiv:2609.24066v1 Announce Type: new Abstract: Best-of-N is a widely used inference strategy for complex reasoning, whose effectiveness depends on whether sampled candidates can cover diverse and hig

tutorialsarxiv-cs-cl

GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model

arXiv:2609.23761v1 Announce Type: new Abstract: Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and ca

researcharxiv-cs-lg

KerColle: Unlocking Fine-Grained GPU Concurrency in Vision-Language-Action Models

arXiv:2609.22335v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as foundational models for next-generation robotics. High VLA inference throughput is critical for me

hardwarearxiv-cs-ro

LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication

arXiv:2609.23193v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become increasingly integrated into privacy-sensitive workflows, ensuring inference-time privacy without compromisi

researcharxiv-cs-cl

RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory

arXiv:2609.23286v1 Announce Type: new Abstract: 3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference contex

hardwarearxiv-cs-cv

SCoP: Structured Constraint Parsing for Evidence-Space Control in Temporal Knowledge Graph Question Answering

arXiv:2609.22213v1 Announce Type: new Abstract: Temporal Knowledge Graph Question Answering (TKGQA) requires answer inference from evidence that is both structurally valid and temporally admissible. E

Agents

StepKV: Step-Aware KV Cache Compression for LLM Agents

arXiv:2609.22158v1 Announce Type: new Abstract: Key-value (KV) caching is essential for efficient autoregressive large language model (LLM) inference, but the cache grows linearly with context length,

agentsarxiv-cs-lg

The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models

arXiv:2609.23369v1 Announce Type: new Abstract: Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving unc

researcharxiv-cs-cv

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

arXiv:2609.24485v1 Announce Type: new Abstract: Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction ofte

safetyarxiv-cs-cv