Search: “research”

StreamDecisionBench: Evaluating Decisions in Force on Evolving Language Streams

arXiv:2609.38612v1 Announce Type: new Abstract: As natural language drives more applications, language models increasingly run inside programs as decision components: the program sends them the curren

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Research

Structural Limits of the Information-Theoretic Uncertainty Decomposition

DGX agent

arXiv:2609.39591v1 Announce Type: new Abstract: Uncertainty estimation in machine learning typically decomposes uncertainty into aleatoric uncertainty (AU) and epistemic uncertainty (EU) using the sta

researcharxiv-cs-cv

1 Oct 2026

Structure-aware Reinforcement Learning for Protein Directed Evolution

arXiv:2609.39048v1 Announce Type: cross Abstract: Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely

Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

arXiv:2609.39049v1 Announce Type: cross Abstract: A large language model (LLM) can rate depression severity directly from a social media post or mark which clinical criteria the post shows and let cod

Superficial Reflection or Genuine Thought? A Fine-Grained Cognitive Analysis of Large Reasoning Models

arXiv:2512.00729v2 Announce Type: replace Abstract: Motivated by the observed human-like behaviours in Large Reasoning Models (LRMs), this paper introduces a comprehensive taxonomy to characterise ato

Switching Linear Attention

arXiv:2609.39034v1 Announce Type: cross Abstract: Designing expressive sequence layers with efficient inference remains a central challenge in modern machine learning. Standard softmax attention achie

SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

arXiv:2609.38225v1 Announce Type: cross Abstract: Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when d

Synthetic Data Characterization via Training Dynamics

arXiv:2609.39447v1 Announce Type: new Abstract: Interpreting properties of LLM-generated data is important for understanding its utility and limitations across learning tasks. In this work, we charact

Tacit-TTS: From Autoregressive Decoding to Masked Prediction for Efficient Transcript-Free Voice Cloning

arXiv:2609.38658v1 Announce Type: cross Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but thei

Tactile Curiosity Drives Robot Interaction

arXiv:2609.40134v1 Announce Type: cross Abstract: Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random a

Taming Speculative Search for Test-Time Scaling in LLM Serving

arXiv:2609.39334v1 Announce Type: cross Abstract: Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, subst

Targeted Retrieval, Compact Representations: How CoT Reasoning Improves Long-Context Counting

arXiv:2609.38958v1 Announce Type: new Abstract: Large language models (LLMs) have been rapidly improving in long-context tasks, powered by Chain-of-Thought (CoT) reasoning. However, the internal mecha

TexTailor: Texture-Preserving Video Virtual Try-On via Adaptive Garment Conditioning

arXiv:2609.39335v1 Announce Type: new Abstract: Video virtual try-on has attracted increasing attention due to its broad potential in digital fashion and intelligent e-commerce. However, existing meth

The Advantages of Fresh Sketching for Ridge Regression

arXiv:2609.38565v1 Announce Type: new Abstract: Over the past 25 years, sketching and sampling have become widely used tools for accelerating large-scale regression. In iterative randomized solvers, a

The Golden Path Hypothesis: Reusable Schedules in Diffusion Caching

arXiv:2609.39343v1 Announce Type: new Abstract: Diffusion caching accelerates generation by replacing transformer computation with cached or predicted features at selected denoising steps. We introduc

The Row Normalization Puzzle in Muon

arXiv:2609.39114v1 Announce Type: new Abstract: This paper examines how row-wise renormalization affects Muon, focusing on the gap between NorMuon's worst-case guarantees and its practical performance

Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression

arXiv:2510.01581v2 Announce Type: replace-cross Abstract: Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in li

ThinkV2V: Unleashing the Reasoning Capability of MLLMs for Instruction-Guided Video Editing

arXiv:2609.38541v1 Announce Type: new Abstract: Instruction-guided video editing has made significant progress, yet existing methods use multimodal large language models (MLLMs) primarily as semantic

Time-adaptive infinite-dimensional Gaussian process regression on manifolds

arXiv:2603.21144v2 Announce Type: replace-cross Abstract: This paper proposes a new formulation of functional Gaussian Process regression on manifolds, based on an Empirical Bayes approach, in the spa

Token Space: A Category Theory Framework for AI Computations

arXiv:2404.11624v2 Announce Type: replace-cross Abstract: We introduce the Token Space, a categorical framework for AI computations. A Token is a finite tuple whose entries are elements of a carrier s

Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation

arXiv:2609.39822v1 Announce Type: new Abstract: Vision-language-action (VLA) models face a timing gap between low-rate inference and high-rate robot execution. We characterize this gap through end-to-

researcharxiv-cs-ro

Towards Better Exploration in Sequential Test-Time Scaling

arXiv:2609.39632v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute at inference. However, both classes of existing methods often fail to

Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification

arXiv:2609.39429v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi

TRACE: Target-Aware Retrieval, Attributed Evidence, and Contract-Constrained Extraction for LitTraceQA

arXiv:2609.38861v1 Announce Type: new Abstract: Finding a relevant paper is not the same as producing a verifiable answer from it. LitTraceQA requires canonical paper identifiers, exact evidence at th

Training LLM Judges from Language Feedback via Position-Selective Self-Distillation

arXiv:2609.38792v1 Announce Type: new Abstract: We study training LLM judges from natural language feedback, especially for subjective tasks where the verdict depends strongly on which evaluation crit

TSMD: Temporal-Stream Modality Dropout for Robust Video Highlight Detection

arXiv:2609.39051v1 Announce Type: new Abstract: Existing multimodal video highlight detectors typically assume that visual, audio, and textual streams are continuously available. In practice, however,

TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic

arXiv:2609.39385v1 Announce Type: cross Abstract: Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic. This paper presents esttt{STAR-Ar}, a BERT-BiLSTM-CR

TutlAit v1: a crowdsourced Moroccan Tamazight speech dataset with Arabic transcriptions and regional accent labels

arXiv:2609.38219v1 Announce Type: new Abstract: Tamazight (Amazigh) is, together with Arabic, one of the two official languages of Morocco, yet it remains severely under-resourced for speech technolog

Typographic Attack Against VLM-based AI-generated Image Detection

arXiv:2609.39662v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity ju

UGOD: Uncertainty-Guided Opacity and Dropout for Sparse-View 3D Gaussian Splatting

arXiv:2609.39089v1 Announce Type: new Abstract: Sparse-view 3D Gaussian Splatting is prone to overfitting because limited observations leave many Gaussian primitives weakly constrained, yet their cont

Uncovering Uncontrolled Repetition through Residual Stream Dynamics

arXiv:2609.38802v1 Announce Type: cross Abstract: Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses

Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Ito Frameworks

arXiv:2609.38395v1 Announce Type: cross Abstract: Stochastic differential equations (SDEs) can be studied via Ito calculus and rough path theory. For stochastic optimal control, these two frameworks g

Unlearnable, or Unmeasured? On the Reliability of Difficulty Labels in RLVR

arXiv:2609.40115v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become an important approach for improving reasoning during post-training. Recent work suggest

Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets

arXiv:2609.38909v1 Announce Type: cross Abstract: Large language models often know the truth and say otherwise: a model that answers correctly when asked neutrally will affirm a user's mistaken belief

URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres

arXiv:2602.10124v2 Announce Type: replace-cross Abstract: Cycling is reported by an average of 35% of adults at least once per week across 28 countries, and as vulnerable road users directly exposed t

Validity-Preserving Hierarchical RL for Joint Routing and Switch Placement in EDA

arXiv:2609.39749v1 Announce Type: new Abstract: Routing and switch placement are fundamental combinatorial optimization problems in chip design, requiring the joint optimization of routing topology an

Variance-Corrected Multi-Asset Equity Simulation with Hybrid Hidden Markov Marginals

arXiv:2603.10202v3 Announce Type: replace-cross Abstract: Synthetic multi-asset equity data must reproduce each asset's return distribution and its relationship with the market. Reusing a generator fi

Visualizing Distribution Coverage in Generative Diffusion Models

arXiv:2609.38853v1 Announce Type: new Abstract: Diffusion distillation is widely adopted to accelerate sampling, and the resulting few-step models are broadly believed to match or even surpass their m

Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears

arXiv:2504.05008v3 Announce Type: replace Abstract: The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their

VOSSA: Voiceprint Optimization for Streaming Speech Architectures

arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effecti

Warm-starting PDE solvers with any-dimensional machine learning

arXiv:2609.38916v1 Announce Type: cross Abstract: Any-dimensional machine learning models, such as graph neural networks (GNNs), can be naturally trained and evaluated on inputs of different sizes and

What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

arXiv:2609.39232v1 Announce Type: cross Abstract: EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streamin

What Was Said, Not What Was 'Thought': Type-6 Logic for CoT Verification

arXiv:2609.38420v1 Announce Type: cross Abstract: We introduce Type-6 logic, a variant of dynamic epistemic logic augmented with two operators (uncertainty and recurrence), designed to model the infer

When a Flatness Proxy Is Not a Function: Robustness Certificates and Training Interventions

arXiv:2609.38540v1 Announce Type: new Abstract: A valid curvature upper bound need not justify either a robustness certificate or an intervention on an intrinsic predictor property. We demonstrate thi

When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning

arXiv:2609.39142v1 Announce Type: new Abstract: Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information the

When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion

arXiv:2609.39004v1 Announce Type: new Abstract: Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a high-quality fused image and support downstr

When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning

arXiv:2609.38817v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verifi

When the Right Answer Is Missing: An Arithmetic-Dependent Rejection Bottleneck in Jev

arXiv:2609.39496v1 Announce Type: cross Abstract: Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making workflows by selecting directly from predefined