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PAM-ToD: Plug-and-Play Appearance Modeling for Cross-Time-of-Day 3D Gaussian Splatting

arXiv:2610.11572v1 Announce Type: new Abstract: Adapting a pre-trained 3D Gaussian Splatting (3DGS) road scene to a new time of day requires learning appearance changes from a few anchor images while

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[Paper] DLoop: Looped Speculative Decoding

DGX agent

Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With

model-releasesr-localllama

9 Oct 2026

[Paper] EngramEdit: Decoupled Knowledge Updates in LLMs through Conditional Memory

model-releasesr-localllama

Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Be

[Paper] Stepped MoE: Segment-Level Routing with Configurable Inference Complexity

Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures

local-air-localllama

Tutorials

Parametric Trajectory Distillation for Few-Step Video Generation

arXiv:2610.11498v1 Announce Type: new Abstract: Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cos

tutorialsarxiv-cs-cv

PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics

arXiv:2602.19661v3 Announce Type: replace Abstract: Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence m

PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology

arXiv:2610.11329v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) have shown strong visual perception ability, but their robustness in the language domain remains poorly characte

PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies

arXiv:2610.11771v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies typically predict actions at fixed time intervals, coupling the route a robot follows with its execution pace. Thi

researcharxiv-cs-ro

PCAsplat: Gaussian Splatting with Local PCA Regularization

arXiv:2610.11011v1 Announce Type: new Abstract: Gaussian splatting has emerged as a flexible representation for 3D reconstruction from posed images. However, existing methods are optimized primarily u

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

arXiv:2606.17924v2 Announce Type: replace-cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding

Perception Test 2026: Challenge Summary and Extension to City-scale Audio-Visual Reasoning

arXiv:2610.12081v1 Announce Type: new Abstract: Continuing the Perception Test challenge series, we organised the fourth edition as a workshop at the European Conference on Computer Vision (ECCV) 2026

Perceptually Grounded and Semantics-Aware Evaluation for Holistic Co-Speech Gesture Generation

arXiv:2610.11669v1 Announce Type: new Abstract: Holistic and semantics-aware co-speech gesture generation has advanced rapidly, yet evaluation remains behind: objective metrics do not consistently ref

Phase-aware video generation for physics-grounded dynamics and interactions

arXiv:2610.11791v1 Announce Type: new Abstract: Generating physically plausible videos for solid-gas dynamics is challenging as different phases exhibit distinct dynamics yet remain coupled through ph

researcharxiv-cs-cv

Phase-HDC: Replacing Optimizer History with Gradient Thresholds in Discrete Phase Learning

model-releasesarxiv-cs-ai

arXiv:2610.10630v1 Announce Type: cross Abstract: Training a compact model often needs far more memory than storing it, because the optimizer keeps its own records of past gradients. For a hyperdimens

Phonological Interference in Multilingual Speech Models

arXiv:2610.11275v1 Announce Type: new Abstract: Phoneme-level models transcribe or generate speech as a sequence of phonemes, the smallest sound units that distinguish words. These models enable fine-

local-aiarxiv-cs-cl

Phonologically Informed Tokenization for German Speech Recognition: A Cross-Domain Study

arXiv:2610.11646v1 Announce Type: new Abstract: German is a morphologically rich language whose syllable structure is exceptionally well-predicted by the Knuth--Liang hyphenation algorithm. We ask whe

researcharxiv-cs-cl

Applications

PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization

arXiv:2603.13228v3 Announce Type: replace-cross Abstract: Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion da

applicationsarxiv-cs-ai

PIER: An Evidence-Gated Execution Interface for Robotic Manipulation

arXiv:2610.12123v1 Announce Type: new Abstract: Generating a plausible robot action does not establish that current observations justify its execution. Motivated by exploratory observations of high-co

safetyarxiv-cs-ro

PIVOT: Perplexity-Informed KD-to-RL Transition Scheduling for Vertical-Domain Few-Shot Distillation

arXiv:2610.11167v1 Announce Type: cross Abstract: Vertical-domain few-shot classification remains challenging for small language models, as limited supervision makes it difficult to acquire domain-spe

Plan-and-Patch: Diffusion Language Models for Agentic Planning

arXiv:2610.10786v1 Announce Type: new Abstract: Planning is increasingly important for long-horizon agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcom

agentsarxiv-cs-ai

PlanWAM: Planning-Shaped Future Representations for End-to-End Autonomous Driving

arXiv:2610.11382v1 Announce Type: cross Abstract: World models in end-to-end autonomous driving predict future scene evolution to provide foresight for trajectory planning. Existing methods mainly stu

agentsarxiv-cs-lg

PLaW-VLA: Predictive Latent World Modeling for Vision-Language-Action Policies

arXiv:2610.12285v1 Announce Type: new Abstract: Learning to predict how the world evolves can provide vision-language-action (VLA) policies with predictive context for long-horizon control, but its ef

safetyarxiv-cs-ro

PlurVA-LLM-2026 Shared Task Track-1: Pluralistic Value Alignment in LLMs via Multilingual Fine-Tuning and Threshold Calibration

model-releasesarxiv-cs-cl

arXiv:2609.32382v2 Announce Type: replace Abstract: We present our system for the PlurVA-LLM 2026 Shared Task Track-1, which focuses on pluralistic value alignment in the contexts of China, Indonesia,

PMTRM: Pseudo-Memory Temporal Re-encoding Module for Embodied Policy Learning

arXiv:2610.11168v1 Announce Type: cross Abstract: Robotic manipulation often contains repeated motions whose local observations look similar at different phases. When these phases require different ac

local-aiarxiv-cs-ai

Point-Focused Attention Meets Context-Scan State Space: Robust Biological Visual Perception for Point Cloud Representation

arXiv:2610.11342v1 Announce Type: new Abstract: Synergistically capturing intricate local structures and global contextual dependencies has become a critical challenge in point cloud representation le

local-aiarxiv-cs-cv

PointVGGT: Zero-Shot Multiview RGB-D Point Cloud Registration with Visual Geometry Foundation Priors

arXiv:2610.11612v1 Announce Type: new Abstract: This paper addresses multiview RGB-D point cloud registration, aiming to estimate global rigid poses for unordered RGB-D scans and align them in a metri

local-aiarxiv-cs-cv

Policy Alignment: New Signals for Membership Auditing in On-Policy Distillation

model-releasesarxiv-cs-lg

arXiv:2610.11423v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model by aligning its policy with a teacher model on trajectories generated by the student model itself. T

Policy Learning with a Language Bottleneck

arXiv:2405.04118v4 Announce Type: replace-cross Abstract: Modern AI systems such as self-driving cars and game-playing agents can achieve superhuman performance, but often lack human-like generalizati

Pose-Free Feed-Forward 3D Inpainting via Learnable Mask Attention and Support Token Refinement

arXiv:2610.11857v1 Announce Type: new Abstract: 3D scene inpainting aims to recover missing or occluded regions in edited 3D scenes, while ensuring geometric and textural consistency. Existing approac

researcharxiv-cs-cv

Poster: A Preliminary Study of LLM Distillation Inference

model-releasesarxiv-cs-lg

arXiv:2610.12137v1 Announce Type: cross Abstract: Unauthorized model distillation, in which a model is trained on the outputs of a proprietary large language model (LLM), is a growing threat to model

Power Side-Channel Membership Inference Attack on Embedded Machine Learning

arXiv:2610.10909v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning (ML) training data by determining whether a sample was used to train a ta

local-aiarxiv-cs-lg

PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives

arXiv:2610.11803v1 Announce Type: new Abstract: Self-improving learning systems adapt data selection, optimization, and auxiliary symbolic components, inducing nonstationary objectives outside standar

Predicting Alignment Generalization with Value Representations

model-releasesarxiv-cs-ai

arXiv:2610.12410v1 Announce Type: cross Abstract: LLM developers post-train their models to exhibit prosocial values and behavioral traits, which are enumerated in an alignment target. However, while

Predicting Cable Dynamics with Physical Attention Bias

arXiv:2610.11975v1 Announce Type: new Abstract: Learned simulators for deformable linear objects (DLOs) such as cables have to predict the motion of cables they were not trained on and stay stable ove

safetyarxiv-cs-ro

Prediction-Powered Data Fusion for Treatment Effect Estimation

arXiv:2610.12332v1 Announce Type: cross Abstract: Randomized controlled trials (RCTs) identify treatment effects without confounding but are often small, whereas observational studies (OBS) are large

Predictive Inorganic Synthesis based on Machine Learning using Small Data sets: a case study of Hydrodynamic Diameter-controlled Cu Nanoparticles

model-releasesarxiv-cs-lg

arXiv:2512.16545v3 Announce Type: replace-cross Abstract: Cu NPs have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with

Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification

arXiv:2610.11185v1 Announce Type: new Abstract: Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can ass

Prior or Feedback? What an LLM Uses When Adapting Neural Operators

arXiv:2610.12325v1 Announce Type: new Abstract: Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback? We study this q

Probabilistic Sensing, Deterministic Authority: Admitting Model-Produced Observations into Sufficiency-Checked Governance Contracts

arXiv:2610.10978v1 Announce Type: cross Abstract: When a field that an authority contract needs exists only in unstructured evidence, a model can sense it. We admit the model's output only as an obser

Probability-Signature Dynamics: Unpacking Modular Addition Learning Within Two-Layer Networks

arXiv:2610.11833v1 Announce Type: new Abstract: Neural networks trained on modular addition tasks often develop Fourier-structured representations that support exact generalization. While prior work h

Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog

arXiv:2610.11592v1 Announce Type: new Abstract: Current frontier LLMs can theoretically process long contexts with 1M tokens or more. But to what extent can they go beyond simple retrieval and perform

researcharxiv-cs-cl

Project Greenhouse: Progress Toward Fully Open and Sovereign Agentic Search

arXiv:2610.11922v1 Announce Type: cross Abstract: Project Greenhouse represents our exploration of a simple thesis: We believe that it is possible to build fully open and sovereign models for agentic

agentsarxiv-cs-cl

Prompts versus Rules: Auditing and Controlling Speech Naturalness Behaviors in Voice User Simulators

arXiv:2610.11015v1 Announce Type: new Abstract: As voice agents gain more popularity commercially, the user simulators used to evaluate the deployed agents are also being developed to include more rea

agentsarxiv-cs-cl

Proof-of-Use: Mitigating Tool-Call Hacking in Deep Research Agents

arXiv:2510.10931v3 Announce Type: replace Abstract: While reinforcement learning (RL) enhances their ability to plan and reason across retrieval steps, we identify a critical failure mode in this sett

Tutorials

Prosody-to-Text: Predicting text from low-pass filtered speech

arXiv:2610.11544v1 Announce Type: new Abstract: While predicting prosody from text is an established task in the field, the opposite direction, predicting text that fits a given prosodic pattern, rema

tutorialsarxiv-cs-cl

Prospective Prediction of OOD Degradation from Source-Side Training Dynamics

arXiv:2610.12397v1 Announce Type: new Abstract: We study whether persistent out-of-distribution (OOD) degradation can be predicted before it is directly observed using only source-side training dynami

ProtoSemImage: Image-Valued Prototypes with Deformable Row Alignment for Interpretable Document Classification

model-releasesarxiv-cs-ai

arXiv:2610.11460v1 Announce Type: cross Abstract: Prototypes in classification models are almost always vectors, and a vector has no readable form. This paper asks what happens when a prototype is an

PSI-SINDy: Post-Selection Inference for Sparse Identification of Nonlinear Dynamics

arXiv:2610.11486v1 Announce Type: cross Abstract: Sparse identification of nonlinear dynamics (SINDy) is a data-driven framework for discovering governing dynamics from time-series data by identifying

safetyarxiv-cs-lg