Search: “inference”

Structured LLM Reasoning for Zero-Shot Human--Robot Coordination Under Hidden Goals

arXiv:2608.04309v1 Announce Type: new Abstract: We present a structured large-language-model (LLM) architecture for zero-shot human--robot coordination in a cooperative construction task with private

safetyarxiv-cs-ro

6 Aug 2026

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Research

AIDE: Automated Instruction via Distilled Expertise for Reference-Free Motor Skill Coaching

DGX agent

arXiv:2608.03047v1 Announce Type: new Abstract: Generating natural-language coaching feedback on motor skills can accelerate learning, yet expert coaches are scarce and expensive. Existing reference-b

researcharxiv-cs-cv

5 Aug 2026

AnchorKV: Anchor-Residual KV Cache Compression

arXiv:2608.02901v1 Announce Type: cross Abstract: The key-value (KV) cache is the primary memory bottleneck in long-context LLM inference. Existing approaches attack it from opposite ends: eviction me

DriftWorld: Fast World Modeling through Drifting

arXiv:2607.15065v2 Announce Type: replace-cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating man

safetyarxiv-cs-cv

Geospatial-Prior Guidance for 3D Semantic Scene Completion

arXiv:2608.03618v1 Announce Type: new Abstract: Inferring complete 3D geometry and semantics from onboard images remains challenging because occlusions and restricted fields of view leave large scene

GSTEP: Global Spatio-Temporal Density-Driven Visual Token Pruning for Efficient Video Large Language Models

arXiv:2608.03083v1 Announce Type: cross Abstract: Video large language models (VideoLLMs) achieve strong video understanding performance, but their inference remains expensive due to the large number

local-aiarxiv-cs-cl

Implementing Causal Perception: Competing SCMs and Situated Fairness

arXiv:2608.03917v1 Announce Type: new Abstract: Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, includ

safetyarxiv-cs-ai

Information-Geometric Forward Policy Training in GFlowNets

arXiv:2608.03967v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects,

local-aiarxiv-cs-lg

MoECa: Aligning Feature Reuse with Expert Decomposition in Diffusion Transformers

arXiv:2606.15615v2 Announce Type: replace-cross Abstract: Diffusion Transformers with Mixture-of-Experts (DiT-MoE) improve model capacity under sparse activation, but diffusion inference is still bott

NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory

arXiv:2608.02700v1 Announce Type: cross Abstract: Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit qua

researcharxiv-cs-ai

One-Point Contraction: Erasing Representational Separability toward Irreversible Deep Forgetting

model-releasesarxiv-cs-ai

arXiv:2507.07754v3 Announce Type: replace-cross Abstract: Machine unlearning is usually evaluated by what the classifier outputs: forget-set accuracy, confidence, membership-inference scores. We show

ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs

model-releasesarxiv-cs-cl

arXiv:2608.04010v1 Announce Type: cross Abstract: Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation,

Rethinking Modality Reliability in Multimodal Sentiment Analysis with Incomplete Observations

arXiv:2608.03611v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) integrates text, audio, and vision to infer human affect, yet real-world multimodal observations are often incomplet

safetyarxiv-cs-ai

Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models

arXiv:2608.02893v1 Announce Type: cross Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from laten

safetyarxiv-cs-ai

Tutorials

Style-Aware Gloss Control for Generative Non-Photorealistic Rendering

arXiv:2602.16611v3 Announce Type: replace-cross Abstract: Humans can infer material characteristics of objects from their visual appearance, and this ability extends to artistic depictions, where simi

tutorialsarxiv-cs-cv

Tutorials

TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning

arXiv:2608.03276v1 Announce Type: new Abstract: Long-context inference with large language models is constrained by the linear growth of the key-value cache to sequence length. While pruning offers mi

tutorialsarxiv-cs-ai

Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data

arXiv:2604.07635v2 Announce Type: replace-cross Abstract: This research considers a scalable inference for spatial data modeled through Gaussian intrinsic conditional autoregressive (ICAR) structures.

When Efficiency Becomes Fragility: Exploiting Dynamic Routing Vulnerabilities in Adaptive UAV Tracking

arXiv:2608.03902v1 Announce Type: new Abstract: Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficien

local-aiarxiv-cs-ai

A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks

arXiv:2608.02274v1 Announce Type: cross Abstract: Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but a

ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

arXiv:2608.01269v1 Announce Type: new Abstract: Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context constructio

safetyarxiv-cs-cl

Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale

arXiv:2608.00101v1 Announce Type: cross Abstract: AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different fr

Belief-Contraction-Driven Active Inverse Source Localization and Characterization

arXiv:2501.13084v2 Announce Type: replace Abstract: Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, wh

local-aiarxiv-cs-lg

Beyond Token-Level Cross-Entropy: Frechet Distributional Post-Training for Autoregressive Image Generation

arXiv:2608.00562v1 Announce Type: new Abstract: Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional qualit

Constrained Co-Design for Photonic Bayesian Neural Networks

arXiv:2608.02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each

safetyarxiv-cs-lg

CriPO: Enhancing Rubric-based RL via Self-Distillation

arXiv:2607.18082v3 Announce Type: replace Abstract: Rubric-based RL has recently shown promise in improving LLMs on open-ended tasks. A widely recognized limitation of rubric-based RL is limited explo

safetyarxiv-cs-lg

Distill What RGB Can Recover: Privileged 3D Evidence for RGB-Only Vision-Language Models

arXiv:2608.00110v1 Announce Type: new Abstract: 3D scene understanding requires reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient

Applications

DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation

arXiv:2608.00793v1 Announce Type: new Abstract: Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on th

applicationsarxiv-cs-ro

EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation

model-releasesarxiv-cs-cv

arXiv:2608.02474v1 Announce Type: new Abstract: Audio-driven video generation (A2V) has achieved promising progress in synthesizing temporally coherent and audio-visually aligned videos, yet its infer

Faster-WAM: Do World Action Models Need Deep Action Modules?

arXiv:2608.02365v1 Announce Type: cross Abstract: World Action Models (WAMs) couple robot action prediction with video world models. Existing WAMs with shared-backbone and Mixture-of-Transformers desi

Length Penalties Make Chain-of-Thought Less Monitorable

arXiv:2607.09786v3 Announce Type: replace-cross Abstract: To curb overthinking and reduce inference costs, researchers now train reasoning models with penalties on chain of thought length. We find tha

Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation

arXiv:2608.01953v1 Announce Type: new Abstract: On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference

safetyarxiv-cs-cl

Hardware

MiniWorld: Democratizing the Training of Video World Models from Scratch

arXiv:2608.01127v1 Announce Type: new Abstract: Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through auto

hardwarearxiv-cs-cv

MonitorVLM-v2: A Deployed Vision-Language Framework for Real-Time Safety Violation Detection

arXiv:2608.00975v1 Announce Type: new Abstract: Large vision--language models (VLMs) can reason step by step about complex visual scenes, but this open-ended, autoregressive chain-of-thought (CoT) app

safetyarxiv-cs-cv

Orchestrating Dual-Boundaries: An Arithmetic Intensity Inspired Acceleration Framework for Diffusion Language Models

arXiv:2511.21759v2 Announce Type: replace Abstract: Diffusion-based large language models (dLLMs) have recently gained significant attention for their exceptional performance and inherent potential fo

PixelSR: Efficient Screen Content Super-Resolution via Pixel Classification

arXiv:2608.00646v1 Announce Type: new Abstract: Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp

local-aiarxiv-cs-cv

PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning

arXiv:2608.02129v1 Announce Type: new Abstract: In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input--output'' prompt examples

RAP: KV-Cache Compression via RoPE-Aligned Pruning

arXiv:2602.02599v4 Announce Type: replace Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the memory and compute of the key-value (KV) cache. Structured pruning is

Residual-Based Adaptive Kalman Filtering for Legged Robot State Estimation

model-releasesarxiv-cs-ro

arXiv:2608.02316v1 Announce Type: new Abstract: State estimation is a key component in model-based control of walking robots and, more broadly, applicable wherever hidden variables must be inferred. T

Stabilized Best-of-K Training for Neural Combinatorial Optimization

arXiv:2608.00296v1 Announce Type: new Abstract: Leader Reward modifies POMO training to emphasize the best trajectory produced by repeated inference. We test a narrow extension: replace its binary lea

Agents

Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents

arXiv:2608.01285v1 Announce Type: new Abstract: The continued development of LLMs toward persistent and adaptive intelligence increasingly requires long-term memory mechanisms that preserve and reuse

agentsarxiv-cs-lg

TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory

arXiv:2608.01922v1 Announce Type: new Abstract: Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as

TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion

arXiv:2608.01288v1 Announce Type: new Abstract: Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However,

Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation

arXiv:2608.01676v1 Announce Type: new Abstract: Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific cont

Where Does Generative Difficulty Reside? An Empirical Study of Target Representations

arXiv:2608.00626v1 Announce Type: new Abstract: The target representation defines the distribution an image generator must learn, yet it is often treated as an interchangeable interface. This assumpti

local-aiarxiv-cs-cv

WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model

arXiv:2608.01845v1 Announce Type: cross Abstract: Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse

Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data

arXiv:2607.29120v1 Announce Type: new Abstract: Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on mill

3 Aug 2026

Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation

arXiv:2510.12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are ada

researcharxiv-cs-ai

3 Aug 2026

Matterhorn: Masked Time-to-First-Spike Encoding by Reassigning the Silent State for Sparse and Energy-Efficient Spiking Transformers

arXiv:2601.22876v2 Announce Type: replace Abstract: Spiking neural networks (SNNs) promise energy-efficient inference for large language models (LLMs), yet most reported savings rely on compute-operat