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Narcissus: Program Synthesis Using Context-Aware LLM Approximations

arXiv:2608.25657v1 Announce Type: cross Abstract: Large language models (LLMs) excel at programming, but not when the task fixes the target language: prompted with a grammar rare in their training dat

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Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More

DGX agent

arXiv:2608.25332v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have exhibited impressive performance across diverse visual scenarios. However, this success comes at the cost of explosiv

researcharxiv-cs-cv

27 Aug 2026

On the Idiosyncrasies of the Mandarin Chinese Classifier System

arXiv:1902.10193v4 Announce Type: replace Abstract: While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lako

On the Representational Geometry of Dynamic Programs

arXiv:2608.25034v1 Announce Type: new Abstract: Standard neural architectures often fail to generalize to longer inputs for dynamic programming (DP) targets. We investigate what makes this hard geomet

On the Separation of Human and AI-Generated Images in CLIP Embedding Space

arXiv:2608.25609v1 Announce Type: new Abstract: We identify a previously unreported phenomenon in CLIP representations: human and AI-generated paintings spontaneously separate along the dominant princ

One Form to Transfer Them All: Pretraining Multilingual Language Models Beyond Native Orthography

arXiv:2608.25904v1 Announce Type: new Abstract: Multilingual language models transfer knowledge across languages through shared subword vocabulary, a mechanism that breaks down when related languages

One way to think of current AI is as a big sponge for patterns. It absorbs and operationalizes any pattern it is exposed to (albeit with ver…

researchfrancois-chollet--x

One way to think of current AI is as a big sponge for patterns. It absorbs and operationalizes any pattern it is exposed to (albeit with very low data efficiency at training time). Once you can progra

OpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning

arXiv:2608.25515v1 Announce Type: new Abstract: Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe extern

Opportunities of Self Supervised Learning for GNSS: Evaluation of a Deep Learning-Enhanced PVT Algorithm

arXiv:2608.25674v1 Announce Type: new Abstract: This work proposes a Deep Learning Enhanced PVT algorithm to mitigate multipath interference in dense urban areas. A supervised objective jointly predic

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs

arXiv:2410.10368v3 Announce Type: replace Abstract: We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels

Output Dilution: Redundant but Fragile Representations in MoE Models

arXiv:2608.25231v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models appear to encode moral content as robustly as dense models, yet prove far more fragile in their encoding. In OLMoE-1B-

Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models

arXiv:2608.25662v1 Announce Type: new Abstract: In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (extbf{S}hared-task on extbf{H}allucinations and extbf{R}elated e

PARAssist: A Framework for Personalized and Adaptive Robotic Assistance from Ambiguous User Requests

arXiv:2608.24905v1 Announce Type: cross Abstract: Service robots may encounter ambiguous user requests that require context-aware inference. Users may also have unique preferences with certain tasks w

Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals

arXiv:2601.17103v2 Announce Type: replace Abstract: Performance uncertainty quantification is essential for reliable validation and eventual clinical translation of medical imaging artificial intellig

PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections

arXiv:2608.25275v1 Announce Type: cross Abstract: Learned traffic-behavior models are commonly trained separately for each intersection, creating model portfolios that cannot share evidence across sit

Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs

arXiv:2608.25564v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) often rely on over-parameterized models to optimize coupled solution and differential-residual objectives, leav

PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence

arXiv:2608.25299v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly rely on point coordinates as a compact and executable interface for visual grounding in GUI interaction, robo

PonsRAG: A Pons-Inspired RAG Bridging Cognitive Islands for Coordinated Long Narrative Reasoning

arXiv:2608.25486v1 Announce Type: cross Abstract: Long Narrative Reasoning is an essential capability for processing and reasoning over complex narratives. While retrieval-augmented generation provide

Precipitation Downscaling Using Foundation Model-Conditioned Diffusion

arXiv:2608.25858v1 Announce Type: new Abstract: High-resolution precipitation fields are essential for hydrological impact assessment, yet global climate model outputs are too coarse and biased for di

Prefix Sliding for efficient test-time scaling

arXiv:2608.26070v1 Announce Type: new Abstract: Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models k

Recognising BSL Fingerspelling in Continuous Signing Sequences

arXiv:2603.19523v2 Announce Type: replace Abstract: Fingerspelling is a critical component of British Sign Language (BSL), used to spell proper names, technical terms, and words that lack established

Recurrence Meets Transformers for Universal Multimodal Retrieval

arXiv:2509.08897v2 Announce Type: replace-cross Abstract: With the rapid advancement of multimodal retrieval and its application in LLMs and multimodal LLMs, increasingly complex retrieval tasks have

ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability

arXiv:2510.09062v2 Announce Type: replace Abstract: Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects cr

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

arXiv:2603.15857v2 Announce Type: replace-cross Abstract: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are onl

Representing MAX functions using two-hidden-layer ReLU networks

arXiv:2608.25221v1 Announce Type: new Abstract: We study exact representations of MAX_N(x)=max{x_1,ldots,x_N} using two-hidden-layer ReLU neural networks. This problem has been studied in recent years

Resolving Multi-Modal Regression by Difference-Quotient-Based Clustering:Fast Coarse Conditional-Label Assignment

arXiv:2608.25467v1 Announce Type: new Abstract: Multimodal regression suffers from the mean-collapse pathology: under squared loss, an unconstrained regressor converges to the conditional mean, which

Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning

arXiv:2608.25133v1 Announce Type: new Abstract: The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machin

Rollout-Decoded Reconstruction for Long-Horizon Prediction in Latent World Models

arXiv:2608.25017v1 Announce Type: new Abstract: A latent world model trains its decoder on latents anchored to observations, then deploys it on the model's own free-running rollout, hundreds of steps

ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion

arXiv:2608.25160v1 Announce Type: cross Abstract: Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user cont

ROS2 Connect: A new ROS2 over WAN Solution

arXiv:2608.25102v1 Announce Type: new Abstract: The Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication

Same-Player Verification for Account Consistency in Counter-Strike 2

arXiv:2608.24893v1 Announce Type: cross Abstract: In competitive first-person shooter (FPS) games such as Counter-Strike 2 (CS2), account-integrity review often asks whether an account's recent behavi

SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping

arXiv:2608.25910v1 Announce Type: new Abstract: Machine Learning (ML) is increasingly used in virtual prototypes of embedded systems to model behaviors that are difficult to capture analytically. Howe

Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration

arXiv:2608.25095v1 Announce Type: cross Abstract: The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and ena

Scorpio: Serving Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference

arXiv:2505.23022v2 Announce Type: replace Abstract: Large Language Model (LLM) serving increasingly underpins online Web services such as conversational agents, Web search, and programming assistants,

Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models

arXiv:2603.16558v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on many multimodal tasks, but object hallucinations severely undermine their reliabi

SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation

arXiv:2608.25123v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often u

Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation

arXiv:2608.25819v1 Announce Type: new Abstract: Accurate 3D segmentation is central to quantitative lesion assessment and anatomy mapping for clinical planning and follow-up. Thin, elongated, and fine

The 'Curse of Knowledge' in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion

arXiv:2608.25245v1 Announce Type: cross Abstract: LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, v

The Frame Kernel Method for Multiscale Operator Learning

arXiv:2608.25084v1 Announce Type: new Abstract: We present a natively multiscale operator learning method for the surrogate modeling of (numerical solvers for) multiscale partial differential equation

Tool Verification for Test-Time Reinforcement Learning

arXiv:2603.02203v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning

Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events

arXiv:2608.25118v1 Announce Type: new Abstract: Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remain

TrustFormer: Cross-Temporal and Cross- Dimensional Transformer for Task-Specific Multi-Dimensional Trust Evaluation

arXiv:2608.25238v1 Announce Type: cross Abstract: In dynamic collaborative systems, the selection of reliable collaborators is critical to ensuring effective task execution. Existing trust evaluation

Two Dimensions Govern Agnostic Multiclass Transductive Learning

arXiv:2608.25326v1 Announce Type: new Abstract: In transductive classification, an adversary fixes a labeled population, one label is hidden uniformly, and the learner sees all remaining labels. For b

UltraPIPS: Improving model perception in B-mode ultrasound with foundation models

arXiv:2608.26033v1 Announce Type: new Abstract: In medical imaging, it is common to use learned perceptual image patch similarity (LPIPS) to compare images semantically in feature space. Although back

Unsupervised Post-Training of Foundation Models: A Survey

arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-T

Unveiling Spectral Mechanisms in Training-Free LLM Text Detection

arXiv:2608.25944v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training

VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

arXiv:2607.18098v2 Announce Type: replace Abstract: Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routi

VietAIDetector: An Open-Source Zero-Shot Detector for Vietnamese AI-Generated Text

arXiv:2608.25478v1 Announce Type: new Abstract: In recent years, distinguishing between AI-generated text and human-written text has remained a challenge. In this paper, we introduce VietAIDetector, a