Search: “research”

Discourse Dependency: A Continuous Criterion for Translation Difficulty

arXiv:2609.04959v1 Announce Type: new Abstract: Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeas

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Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One

DGX agent

arXiv:2609.04531v1 Announce Type: new Abstract: Language generation is almost universally treated as a sequential process: autoregressive models emit one token at a time, while diffusion language mode

researcharxiv-cs-lg

7 Sept 2026

Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory

arXiv:2609.05245v1 Announce Type: new Abstract: Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of its prerequisites, a principle formalized by

Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

arXiv:2609.05275v1 Announce Type: new Abstract: Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustness to zero-shot layer pruning in both lan

E-RGB-D: Real-Time Event-Based Perception with Structured Light

arXiv:2512.18429v2 Announce Type: replace Abstract: Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and eff

Embedded Graph Flows for Categorical Graph Generation

arXiv:2609.05328v1 Announce Type: new Abstract: Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generator

Enhancing Affine Maximizer Auctions with Correlation-Aware Payment

arXiv:2602.09455v2 Announce Type: replace-cross Abstract: Affine Maximizer Auctions (AMAs), a generalized mechanism family from VCG, are widely used in automated mechanism design due to their inherent

Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze

Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robots

arXiv:2507.17049v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA)-enabled robots integrate visual perception, natural language understanding, and action planning to interpret thei

researcharxiv-cs-ro

Evaluation of Phonetic Encoding Algorithms on Transcription Datasets

arXiv:2609.04391v1 Announce Type: new Abstract: In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the Hullermeier-Rifqi Index, is proposed in or

Explainable Clustering of Mixture Models

arXiv:2411.01576v3 Announce Type: replace Abstract: The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with K leaves

Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving

arXiv:2509.22480v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised f

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

arXiv:2609.04501v1 Announce Type: cross Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, wher

FailSAE: Towards Interpretable Failure Prediction for Vision-Language Models via Sparse Autoencoders

arXiv:2609.04276v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by aligning visual and textual representations in

FailureSpot: Label-Efficient Timestamp-Level Failure Detection for Vision-Language-Action Models

arXiv:2609.04277v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, but they can still fail unpredictably duri

Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates

arXiv:2609.04815v1 Announce Type: new Abstract: Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence

FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search

arXiv:2609.05207v1 Announce Type: cross Abstract: Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. H

Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks

arXiv:2512.14779v2 Announce Type: replace Abstract: Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In

Fractional-Order Adaptive Motion Magnification: Phase-Reliability Weighting for Noise-Constrained Video Amplification

arXiv:2609.04502v1 Announce Type: cross Abstract: Eulerian video amplification boosts sub-pixel motion by band-pass filtering per-pixel intensity traces and applying a uniform gain. That gain ignores

From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs

arXiv:2609.04543v1 Announce Type: new Abstract: A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the m

From Interpretability Methods to Interpretable Models

arXiv:2609.05399v1 Announce Type: new Abstract: More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and c

From Plausible to Actionable: A Position on LLM Self-Explanations

arXiv:2607.15957v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as s

From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making

arXiv:2609.05149v1 Announce Type: new Abstract: Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these decisions are grounded in visual evidence

GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity

arXiv:2609.05284v1 Announce Type: new Abstract: Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhi

HaptiNet: Networked Haptic Robots Enable Physical Co-presence in Geographically-Unconstrained Rehabilitation

arXiv:2609.04799v1 Announce Type: new Abstract: Cooperative rehabilitation enhances engagement, task performance, and social-motor interaction, yet it demands physical co-presence: users must transmit

researcharxiv-cs-ro

Harness-agnostic detection and immunization of reward hacking in self-evolving language models

arXiv:2609.04665v1 Announce Type: new Abstract: Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible score. When that score is an imperfect proxy

Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

arXiv:2609.05233v1 Announce Type: new Abstract: While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applic

Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR

arXiv:2609.04592v1 Announce Type: new Abstract: Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, and attention, but the same signals can als

Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

arXiv:2609.04803v1 Announce Type: new Abstract: Pass receiver selection is a fundamental task in football analytics, aiming to predict the intended receiver under a given game state. This task is chal

HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction

arXiv:2609.04718v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics and vision, supporting mapping, localizatio

How Do Language Models Represent and Use Phonological Information for Allomorph Selection?

arXiv:2609.04708v1 Announce Type: new Abstract: Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably produce morphemes whose form is phonological

How Faithful Is Attribution for Sales Forecasting? A Counterfactual Study

arXiv:2609.04797v1 Announce Type: new Abstract: Deep models for sales forecasting, such as WaveNet-style dilated convolutional networks, are accurate but opaque: when a single model predicts sales for

Improving Language Identification for Code-Switched Utterances with Integer Linear Programming

arXiv:2609.05099v1 Announce Type: new Abstract: Automatic identification of code-switched (CS) utterances remains a challenge for language identification (LID) systems, causing such texts to be underr

Inductive Venn-Abers and related regressors

arXiv:2605.06646v2 Announce Type: replace Abstract: Venn-Abers predictors are probabilistic predictors that enjoy appealing properties of validity, but their major limitation is that they have been ap

Influence Score and Transformers interpretability: Measure of the Effective Impact of Attention Heads at inference time

arXiv:2609.05074v1 Announce Type: new Abstract: We propose an influence score to quantify the contribution of attention heads to classification decisions in Transformer-based models designed for promp

Interpretability for Turing Machines

arXiv:2609.04661v1 Announce Type: new Abstract: We show that susceptibilities, an interpretability technique developed for neural networks, can identify the presence of algorithmic structure in Turing

JLIR: A Julia-Native MLIR-Inspired Intermediate Representation with Automatic JACC Kernel Extraction

arXiv:2609.04585v1 Announce Type: cross Abstract: The Multi-Level Intermediate Representation (MLIR) has made reusable compiler infrastructure practical for domain-specific computation. However, MLIR'

Knowing When Not to Answer: Pseudo-Ensembles for Abstention in Music Audio-Language Models

arXiv:2609.04362v1 Announce Type: cross Abstract: Music audio-language models are evaluated almost entirely by accuracy on multiple-choice questions. This protocol forces the model to commit to an opt

Large Language Models with At Most One Spike per Neuron

arXiv:2609.05151v1 Announce Type: cross Abstract: Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising path toward energy-efficient large languag

Learning 3D Editing without Paired Supervision via Generative Prior Distillation

arXiv:2609.04942v1 Announce Type: new Abstract: Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality

Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications

arXiv:2609.04787v1 Announce Type: new Abstract: Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This survey synthesizes prediction interfaces, err

LetOccVote: Learning Weakly Supervised 3D Occupancy through Consensus

arXiv:2609.04846v1 Announce Type: new Abstract: Weakly supervised 3D occupancy prediction reduces the reliance on costly 3D annotations by learning from 2D pseudo-labels generated by vision foundation

Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection

arXiv:2609.05025v1 Announce Type: cross Abstract: Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the source-is a major challenge for both prompte

LookThere! Sparse Vision by Reinforced Selection

arXiv:2609.04698v1 Announce Type: new Abstract: Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive c

Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements

arXiv:2609.05234v1 Announce Type: new Abstract: Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffusion sliders derive their axes from text or

MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis

arXiv:2609.04804v1 Announce Type: new Abstract: Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable clinical prediction models. However, existi

Methane Detection On Board Satellites from Unorthorectified Imagery

arXiv:2609.04906v1 Announce Type: cross Abstract: As a potent greenhouse gas, methane is a major driver of climate change. Its effective mitigation relies on timely detection. Conventional detection m

MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision

arXiv:2609.04958v1 Announce Type: new Abstract: Recovering camera and hand motion in world coordinates from egocentric video is a key capability for activity understanding, robot learning, and augment