Search: “inference”

How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology

model-releasesarxiv-cs-cv

arXiv:2606.12407v1 Announce Type: new Abstract: General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs).

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Implicit Neural Representations of Individual Behavior

DGX agent

arXiv:2606.12200v1 Announce Type: cross Abstract: We study policy representation learning from unlabeled multi-policy behavioral data. Each episode is generated by a fixed policy, but policy labels ar

safetyarxiv-cs-ai

11 Jun 2026

ISAP-3D: Identity-Slot Aligned Part-Aware 3D Generation

arXiv:2606.12099v1 Announce Type: new Abstract: Part-aware 3D generation aims to synthesize structured objects with semantically meaningful components, yet often suffers from structural ambiguity due

safetyarxiv-cs-cv

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking

arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but

Latent World Recovery for Multimodal Learning with Missing Modalities

arXiv:2606.12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are o

LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

arXiv:2606.11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native,

Measuring Semantic Progress in Multi-turn Dialogue via Information Gain

arXiv:2606.12332v1 Announce Type: new Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses. We focus on a key dimension

safetyarxiv-cs-cl

MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning

arXiv:2606.12018v1 Announce Type: new Abstract: We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM), specifically designed for social intel

local-aiarxiv-cs-ai

Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction

arXiv:2510.22335v2 Announce Type: replace-cross Abstract: Reconstructing visual stimuli from fMRI signals is a central challenge bridging machine learning and neuroscience. Recent diffusion-based meth

local-aiarxiv-cs-ai

Multi-Agent Reasoning with Adaptive Worker Allocation for Stance Detection

model-releasesarxiv-cs-cl

arXiv:2606.11609v1 Announce Type: new Abstract: Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorica

MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models

arXiv:2606.11792v1 Announce Type: cross Abstract: Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated respo

local-aiarxiv-cs-ai

Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents

arXiv:2606.11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be en

PAWS: Preference Learning with Advantage-Weighted Segments

arXiv:2606.11982v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demon

safetyarxiv-cs-lg

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

model-releasesarxiv-cs-lg

arXiv:2505.08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we int

PIGEON: VLM-Driven Object Navigation via Points of Interest Selection

arXiv:2511.13207v2 Announce Type: replace-cross Abstract: Object navigation in unseen indoor environments requires agents to perform semantic search under partial observability. Vision-language models

local-aiarxiv-cs-cv

PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.11396v1 Announce Type: new Abstract: Dexterous manipulation with multi-finger hands can be sensitive to physical parameters such as object shape, pose, and friction coefficients. While simu

Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts

arXiv:2602.02229v2 Announce Type: replace Abstract: We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powe

Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices

arXiv:2606.11556v1 Announce Type: cross Abstract: Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deploya

applicationsarxiv-cs-ai

Re-evaluating Confidence Remasking in Masked Diffusion Language Models

arXiv:2606.12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faste

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

arXiv:2509.23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and a

SheafStain: Sheaf-Theoretic Schrodinger Bridge for Spatially and Biologically Coherent Virtual Staining

model-releasesarxiv-cs-cv

arXiv:2606.11846v1 Announce Type: new Abstract: Current virtual staining approaches offer the potential for time- and cost-efficient biomarker quantification in cancer diagnostics and prognostics. How

SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing

arXiv:2606.11674v1 Announce Type: cross Abstract: We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-s

SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation

arXiv:2606.11969v1 Announce Type: new Abstract: Flow Matching has enabled robust text-to-video generation via latent ODE sampling. However, velocity approximation and numerical discretization errors i

local-aiarxiv-cs-cv

SwiftCTS: Fast Cross-Design Prediction and Pareto Optimization of Clock Tree Metrics via Few-Shot Calibration

model-releasesarxiv-cs-lg

arXiv:2606.11348v1 Announce Type: new Abstract: Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast

System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5

arXiv:2606.12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical

TAHOE: Text-to-SQL with Automated Hint Optimization from Experience

arXiv:2606.12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real d

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

arXiv:2606.11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples

applicationsarxiv-cs-ai

The Power of Test-Time Training for Approximate Sampling

arXiv:2606.11437v1 Announce Type: cross Abstract: Efficiently sampling from a complex probability distribution is a fundamental problem which has become increasingly pertinent in recent years with the

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning

arXiv:2505.03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning i

Toward Preference-aligned Large Language Models via Residual-based Model Steering

arXiv:2509.23982v2 Announce Type: replace-cross Abstract: Preference alignment is a critical step in making Large Language Models (LLMs) useful and aligned with (human) preferences. Existing approache

Tree-Structured Orthonormal Decomposition of the Aitchison Simplex

arXiv:2606.11646v1 Announce Type: new Abstract: Compositional data -- vectors encoding relative proportions -- arise across scientific domains, including ecology, geochemistry, and genomics. The featu

local-aiarxiv-cs-lg

TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search

arXiv:2606.11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge

Understanding Cross-Sensor Feature Variations for Generalizable 3D Perception

arXiv:2606.11573v1 Announce Type: new Abstract: Radar-camera BEV perception often suffers from degraded performance when evaluated across datasets, as changes in driving scenes, sensor configurations,

researcharxiv-cs-cv

UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA

arXiv:2606.11740v1 Announce Type: cross Abstract: We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a

safetyarxiv-cs-cl

VICX: Generalizable Robot Manipulation via Video Generation and In-Context Operator Network

model-releasesarxiv-cs-ro

arXiv:2606.12028v1 Announce Type: new Abstract: Generalizable robot manipulation requires not only task-level reasoning over unseen scenes, but also reliable grounding of visual plans into embodiment-

When Context Returns: Toward Robust Internalization in On-Policy Distillation

arXiv:2606.11627v1 Announce Type: cross Abstract: Recent work has shown that on-policy distillation can internalize privileged context, such as system prompts or task hints, into a student model so th

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models

model-releasesarxiv-cs-cl

arXiv:2606.11906v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic varia

Tutorials

A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems

arXiv:2606.10782v1 Announce Type: cross Abstract: The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and ava

tutorialsarxiv-cs-ai

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606.10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for year

ABot-Earth 0.5: Generative 3D Earth Model

arXiv:2606.09967v1 Announce Type: new Abstract: We present ABot-Earth 0.5, a generative 3D framework designed to synthesize vast, seamless 3D environments from ubiquitous, geospatially referenced sate

applicationsarxiv-cs-cv

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privac

AnimaSpark: A Feed-Forward Method for Animating Arbitrary 3D Objects

arXiv:2606.10988v1 Announce Type: new Abstract: While recent advancements in generative AI have substantially accelerated static 3D model creation workflows, the synthesis of category-agnostic 3D anim

safetyarxiv-cs-cv

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

arXiv:2602.13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support rel

local-aiarxiv-cs-lg

ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents

arXiv:2602.04935v3 Announce Type: replace-cross Abstract: Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy t

Assessment of Personality Dimensions Across Situations in Dyadic Role-Play Scenarios

arXiv:2507.19137v2 Announce Type: replace-cross Abstract: Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in autom

Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

arXiv:2606.10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich conte

AuRA: Internalizing Audio Understanding into LLMs as LoRA

arXiv:2606.11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models

BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression

arXiv:2606.10135v1 Announce Type: cross Abstract: Transitioning bidirectional video diffusion models into an autoregressive paradigm improves the interactivity of video world models, but existing caus

applicationsarxiv-cs-ai