Search: “inference”

arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,

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Research

Made in Hungary: Comments on the performance of generative language models

DGX agent

arXiv:2609.18284v1 Announce Type: new Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian,

researcharxiv-cs-cl

17 Sept 2026

Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking

arXiv:2609.18716v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) match the event-driven nature of event cameras and naturally extract spatiotemporal features. These properties have motiv

Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI

arXiv:2609.17969v1 Announce Type: new Abstract: Long-term memory is becoming a core substrate for personalized AI, yet most systems still represent personalization as discrete records in a largely sta

researcharxiv-cs-ai

Agents

MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents

arXiv:2609.19059v1 Announce Type: cross Abstract: Multimodal large language model (MLLM) agents are increasingly used as personal assistants for long-running tasks. Their utility depends on continuity

agentsarxiv-cs-ai

Missing Bridges: Composition-Aware Active Imitation Learning

arXiv:2609.18004v1 Announce Type: new Abstract: Active imitation learning reduces expert effort by allowing a learner to request the demonstrations it needs. Existing methods typically select these re

safetyarxiv-cs-ai

MSR: Multiple Subject Reference for Video Generation

arXiv:2609.18393v1 Announce Type: new Abstract: Conditioning a video generator on multiple images requires preserving appearance while associating each reference with its intended role. We present MSR

Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency

model-releasesarxiv-cs-lg

arXiv:2609.18315v1 Announce Type: new Abstract: Non-intrusive load monitoring (NILM) estimates appliance power sequences from aggregate power, but models trained on source households commonly lose acc

Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective

arXiv:2609.18432v1 Announce Type: new Abstract: Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in p

OmniRisk: Omnidirectional Trajectory-Risk Learning for Agile Quadrotor Dynamic Avoidance

arXiv:2609.18191v1 Announce Type: new Abstract: Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reli

applicationsarxiv-cs-ro

Tutorials

One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG

arXiv:2609.17709v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differe

tutorialsarxiv-cs-ai

Peak-Aware Short-Term Load Forecasting Across Distribution Grid Aggregation Levels

arXiv:2609.18588v1 Announce Type: new Abstract: For distribution system operators, short-term load forecasting (STLF) supports congestion management, voltage control, and asset protection. Most existi

Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of H{alpha} 6562.8 A and Ca II 8542.1 A Spectra

arXiv:2609.18025v1 Announce Type: cross Abstract: Strong chromospheric absorption lines such as Halpha 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in

PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image

arXiv:2609.18920v1 Announce Type: new Abstract: Physical properties, such as friction, hardness, stiffness, and density, govern how robots should grasp, manipulate and interact with objects, yet estim

local-aiarxiv-cs-cv

Position Anchor Tuning: Towards Efficient Adaptation of Pre-Trained Point Cloud Transformers

arXiv:2609.18056v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has recently emerged as a pivotal research direction for adapting pre-trained point cloud transformers to diverse

Preservation of Log-Concavity and Convergence of Wasserstein-Fisher-Rao Gradient Flows

arXiv:2609.18118v1 Announce Type: cross Abstract: We study the convergence of Wasserstein-Fisher-Rao (WFR) gradient flows for sampling from probability distributions known up to a normalisation consta

ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks

arXiv:2609.18805v1 Announce Type: cross Abstract: Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents ma

Provable Guarantees for Spectral Structured Prediction

arXiv:2609.18527v1 Announce Type: new Abstract: Structured prediction is the simultaneous prediction of multiple labels, and is widely used in various fields, such as natural language processing and c

QuanText: Protecting Dataset-Level Secrets in Textual Data Sharing

arXiv:2609.17995v1 Announce Type: new Abstract: Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the un

applicationsarxiv-cs-lg

RAFAIL: Relationship-Aware Failure Detection for Robotic Manipulation

arXiv:2609.18324v1 Announce Type: new Abstract: Detecting failures during execution is essential for reliable robotic manipulation. Vision-language models (VLMs) can assess task outcomes semantically

safetyarxiv-cs-ro

RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control

arXiv:2609.18359v1 Announce Type: cross Abstract: Generalized morphology control requires a single policy to transform information across limbs with different physical roles, coordinate whole-body mot

safetyarxiv-cs-lg

Reinforcement Learning for Real-Time Vision-Language-Action Policies

model-releasesarxiv-cs-lg

arXiv:2609.18207v1 Announce Type: cross Abstract: Reinforcement learning fine-tuning on top of large, pretrained Vision-Language-Action (VLA) models offers promise for highly reliable robot deployment

Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings

arXiv:2601.11262v2 Announce Type: replace-cross Abstract: Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work, a task

StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction

arXiv:2609.18949v1 Announce Type: cross Abstract: We introduce StableEval Arena, a cost-aware benchmark framework for evaluating agentic AI systems on stablecoin peg-risk prediction. StableEval Arena

Structured Claim-Level Discourse Representations for Dense Health Narratives

model-releasesarxiv-cs-cl

arXiv:2609.18905v1 Announce Type: new Abstract: Health discourse in social media videos often contains densely entangled claims spanning multiple thematic aspects, stances, evidential frames, and rhet

SVMemAgent: A Streaming Video Memory Agent for Query-Agnostic Online Frame Selection

arXiv:2609.18540v1 Announce Type: new Abstract: Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming sce

safetyarxiv-cs-cv

Hardware

TabICLv2: A better, faster, scalable, and open tabular foundation model

arXiv:2602.11139v2 Announce Type: replace Abstract: Tabular foundation models, such as TabPFNv2 and TabICL, have recently dethroned gradient-boosted trees at the top of predictive benchmarks, demonstr

hardwarearxiv-cs-lg

The Attention Within: Consensus Dynamics in Selective State Space Models

arXiv:2609.17997v1 Announce Type: cross Abstract: Selective state space models (SSMs) have recently emerged as a compelling alternative to transformers, combining competitive performance with substant

local-aiarxiv-cs-ai

The Automaton Underneath: The Additive Input Pathway Is a Parasitic Attractor for State Tracking in Householder Linear RNN

arXiv:2609.18966v1 Announce Type: new Abstract: Linear RNNs with input-dependent Householder-product transitions (DeltaNet/DeltaProduct-class) can provably represent hard state-tracking automata, yet

Think Before You Comfort: Reflective Cognitive Alignment for Protocol-Grounded Elderly Stimulation Agents

arXiv:2609.17536v1 Announce Type: new Abstract: Cognitive Stimulation Therapy (CST) offers non-pharmacological support for elders with cognitive impairment, yet scalability remains constrained by reli

safetyarxiv-cs-cl

Track, Articulate, Act: Generating Articulation from Casual Human Videos

arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes i

researcharxiv-cs-cv

UMI-Bridge: Action-Anchored Latent Alignment across Human and Robot Manipulation Data

arXiv:2609.18232v1 Announce Type: new Abstract: Real-robot demonstrations are limited, motivating the use of human manipulation data collected without robots, including egocentric videos and handheld

safetyarxiv-cs-ro

Variational Approach for Job Shop Scheduling

arXiv:2602.00408v3 Announce Type: replace-cross Abstract: This paper proposes a novel Variational Graph-to-Scheduler (VG2S) framework for solving the Job Shop Scheduling Problem (JSSP), a critical tas

VibeAvatar: Aligning Phonetic Kinematics and Human Aesthetics for High-Fidelity Talking Avatar Synthesis

arXiv:2609.18632v1 Announce Type: new Abstract: Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion

local-aiarxiv-cs-cv

Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly

arXiv:2609.17714v1 Announce Type: new Abstract: Real-world robotic disassembly requires long-horizon execution, where robots must perform ordered sequences of manipulation tasks across multiple parts

safetyarxiv-cs-ro

When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments

arXiv:2609.17772v1 Announce Type: cross Abstract: AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified.

safetyarxiv-cs-ai

Agentic Search Spaces for Tabular Machine Learning

model-releasesarxiv-cs-lg

arXiv:2609.16309v1 Announce Type: new Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains

An Empirical Study of Counterfactual Self-Explanations in LLMs

model-releasesarxiv-cs-cl

arXiv:2609.17119v1 Announce Type: new Abstract: Large language models can easily generate explanations for their own outputs, but such self-explanations are not necessarily faithful to the model's beh

Assurance Envelopes for Autonomous Coding Agents: Minimum-Cost Evidence for Software Change

arXiv:2609.16302v1 Announce Type: cross Abstract: When a coding agent returns to existing software, it inherits evidence from earlier engineering work: tests, type checks, proofs, static analyses, and

Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery

arXiv:2609.16407v1 Announce Type: cross Abstract: On a delivery platform, personalized store ranking greatly influences what users find and order. Unlike digital-only domains, candidate stores are loc

local-aiarxiv-cs-ai

Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity

arXiv:2609.16040v1 Announce Type: new Abstract: Bilateral control-based imitation learning captures both position and force information, making it well suited to contact-rich manipulation. However, ex

applicationsarxiv-cs-ro

Bias Audits Detect Bias but Disagree on Ranking: Evidence from Ten Instruments and Ten Frontier Models

arXiv:2609.15995v1 Announce Type: new Abstract: Emerging AI regulation mandates bias audits of high-risk systems, and audit scores are beginning to be used to rank models. Both uses assume different a

safetyarxiv-cs-cl

Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels

arXiv:2609.16380v1 Announce Type: new Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule,

Breaking the 1.58-bit Barrier for Ternary LLMs

arXiv:2609.16338v1 Announce Type: new Abstract: Ternary Large Language Models (LLM) store every weight as one of three symbols {-1,0,+1}, so the cost of a ternary model is conventionally referenced to

applicationsarxiv-cs-ai

Causal neural set filtering for online multi-target tracking

arXiv:2609.16054v1 Announce Type: cross Abstract: Transformer-based multi-target tracking (MTT) jointly learns data association and state estimation, but MT3/Track-MT3-style trackers repeatedly re-enc

researcharxiv-cs-ai

CineScale: Tuning-Free High-Resolution Video Generation

arXiv:2508.15774v2 Announce Type: replace Abstract: Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data a

researcharxiv-cs-cv

Conditioning noise is a free regularizer for LoRA fine-tuning: no pathology encoder required for diffusion-based artifact detection in histopathology

arXiv:2609.16032v1 Announce Type: cross Abstract: Diffusion-based artifact detectors score whole-slide image patches by reconstruction error under a model fine-tuned on clean tissue. We show that cond

researcharxiv-cs-cv

Continuous-Time Machine Learning: A Unified Mathematical Perspective

arXiv:2609.16710v1 Announce Type: cross Abstract: Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when