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When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output

arXiv:2503.24191v4 Announce Type: replace-cross Abstract: Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers. Large Language Models (L

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Local Ai

When Is a Steerable Concept Representation Real? Measurement Confounds in a Cross-Family Audit of Neuroscience Parallels in LLMs

DGX agent

arXiv:2608.08159v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly reported to exhibit human-like neural and cognitive signatures, including concept cells, mental number lin

local-aiarxiv-cs-ai

11 Aug 2026

When Is Benchmark Contamination Detectable? Information Limits and Power-Calibrated Audits

arXiv:2608.07914v1 Announce Type: new Abstract: Behavioral contamination detectors can return 'no evidence' either because a benchmark is clean or because the audit has little power. We formalize this

Tutorials

When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution

arXiv:2608.09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR obser

tutorialsarxiv-cs-ai

When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains

arXiv:2608.07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably

When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

model-releasesarxiv-cs-lg

arXiv:2603.10950v2 Announce Type: replace Abstract: Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still e

When Skills Meet Safety: Benchmarking and Characterizing the Adaptive Jailbreak Robustness of Skill-Merged LLMs

model-releasesarxiv-cs-lg

arXiv:2608.08542v1 Announce Type: new Abstract: Model merging has become the default way to give an aligned language model new skills without retraining: a practitioner folds task vectors from math, c

When the Judge Should Not Decide: Evidence-Locked, Non-Compensatory Selection Bounds LLM-Judge Failure in Reasoning Pipelines

arXiv:2608.07813v1 Announce Type: new Abstract: An LLM judge deployed inside a reasoning pipeline does not merely measure quality, it decides which answer ships. We show that the cost of that decision

Where Is the Bee? Detecting Tiny Pollinators with a Single Collaborative-Head Transformer

arXiv:2608.08580v1 Announce Type: new Abstract: The CVPPA@ECCV 2026 BuzzSpot Challenge asks us to detect bees, bumblebees, hoverflies, and moths in 1920x1080 field keyframes. Its annotations carry 2 d

Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways

arXiv:2608.09095v1 Announce Type: new Abstract: Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial i

local-aiarxiv-cs-ai

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

arXiv:2608.07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships t

safetyarxiv-cs-ai

Who Verifies the Benchmark? Decentralizing Trust in Large Language Model Evaluation

arXiv:2608.07762v1 Announce Type: new Abstract: LLM benchmarks can build an organization's reputation and attract customers, but only when results are transparent and verifiable. Unverified claims tha

Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models

arXiv:2608.00591v2 Announce Type: replace Abstract: A calibrated stochastic world model can reveal how uncertain a future is without revealing why it branches. The same conditional future law can aris

Wiener Representation Filtering for VLM Hallucination Suppression

arXiv:2608.08167v1 Announce Type: new Abstract: Vision-language models (VLMs) excel at open-ended captioning and visual QA but often describe objects, attributes, or relations absent from the image, a

Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs

arXiv:2608.08090v1 Announce Type: new Abstract: Although multilingual approaches to figurative language identification are not new, the shift beyond language homogeneous training data requires a clear

researcharxiv-cs-cl

World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the World

arXiv:2608.07510v1 Announce Type: cross Abstract: Increasingly, AI video models are marketed as 'world simulators,' suggesting their ability to model infinite realities. Despite such claims, these mod

World Tokens: Enhancing Embodied Policies with Training-Time World Modeling

arXiv:2608.09730v1 Announce Type: new Abstract: Vision-language-action (VLA) models are a widely adopted paradigm for embodied policies. They excel at efficient closed-loop control but do not explicit

safetyarxiv-cs-cv

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation

arXiv:2608.09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data g

WRAP: Wasserstein-Robust Adaptive Plug-in for Robot Localization

arXiv:2608.09807v1 Announce Type: new Abstract: Robotic localization under changing sensing conditions can suffer from biased errors and miscalibrated covariances. We present WRAP, an adapter-agnostic

local-aiarxiv-cs-ro

WuYuEval: A Multi-Level Benchmark for Large Language Models in Solid Waste Management

arXiv:2608.07529v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as technical assistants, but their competence in solid waste management (SWM) remains difficult to

X2C: A Dataset Featuring Nuanced Facial Expressions for Realistic Humanoid Imitation

arXiv:2505.11146v3 Announce Type: replace-cross Abstract: Fine-grained facial expression transfer from humans to humanoid agents presents a unique pattern recognition challenge due to the significant

XClipGS: Exact Half-Space Clipping for Medical Volume Gaussian Splatting

arXiv:2608.07760v1 Announce Type: new Abstract: Gaussian-splatting proxies enable interactive rendering of volumetric medical scans, but a clipping plane exposes anatomy not constrained by external-vi

local-aiarxiv-cs-cv

XEns-CKD: An Explainable Ensemble-Based Approach for Chronic Kidney Disease Stage Detection

arXiv:2608.07561v1 Announce Type: new Abstract: Chronic kidney disease (CKD) is a silent disease. Its progression may not significantly hamper a person's daily routine. Human kidney function can be cl

XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher

arXiv:2608.09519v1 Announce Type: new Abstract: We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images e

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

model-releasesarxiv-cs-ro

arXiv:2608.09892v1 Announce Type: new Abstract: Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that

Yesterday's Shield, Today's Spear: A Self-Evolving Safety Guardrail in Production

arXiv:2608.08471v1 Announce Type: new Abstract: Deployed LLM safety guardrails are predominantly static: trained once and frozen at release, while new jailbreak techniques and previously un-addressed

safetyarxiv-cs-ai

You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvement

model-releasesarxiv-cs-ro

arXiv:2608.07555v1 Announce Type: new Abstract: Coding agents such as Claude Code and Codex close the software loop: a main agent manages the loop, subagents analyze and execute, tools do the work. We

You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows

arXiv:2608.09579v1 Announce Type: new Abstract: Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation

Your Prompt Is Not the Only Prompt: How Much Do LLMs Weight Structured-Output Schema Descriptions?

arXiv:2608.08254v1 Announce Type: new Abstract: Structured output, where an LLM populates a predefined JSON schema, has become a default mechanism for data labeling and information extraction, but it

Your VLM Already Knows When: Training-Free Temporal Grounding by Asking Yes or No

arXiv:2608.08315v1 Announce Type: new Abstract: Multimodal LLMs that recognise events reliably still fail to say when they happen. Prompted for timestamps, strong VLMs reach as little as 3.8% R@0.5 on

Zero-shot 2D Grounding with Novel Affordance Types

arXiv:2608.08929v1 Announce Type: new Abstract: 2D affordance grounding aims to locate the region of an object that a human can interact with. Existing research focuses on recognizing affordance types

Zero-Shot Traffic Accident Detection via a Coarse-to-Fine VLM-Tracking Pipeline

arXiv:2608.08867v1 Announce Type: new Abstract: Traffic surveillance cameras capture accidents continuously, yet converting raw CCTV footage into structured event records that pinpoint when, where, an

ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling

arXiv:2608.07974v1 Announce Type: new Abstract: Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy. Although existing studies propos

local-aiarxiv-cs-lg

ZetaGPT: A Reference Implementation of Positional--Encoding--Free State--Space--Attention Language Models

arXiv:2608.09432v1 Announce Type: cross Abstract: Transformer-based language models rely on self-attention, whose computation is permutation-equivariant and therefore lacks an intrinsic mechanism for

ZhuLong: Execution-Grounded LLM Agent for EDA Scripting with Offline API Self-Exploration

arXiv:2608.07925v1 Announce Type: new Abstract: EDA scripting with tool-specific, often undocumented APIs remains a long-tail bottleneck that existing LLMs fail to address. This paper presents ZhuLong

ZOMP: Zeroth-Order Multi-Modal Prompt Tuning for Vision-Language Models

arXiv:2608.08060v1 Announce Type: new Abstract: Fine-tuning vision-language models such as CLIP typically requires backpropagation (BP) through the full model, which is infeasible when only forward-pa

A Disturbance in the Force: Force Actuation on the RAVEN II Surgical Robot with Parallel Motor-Cable Units

arXiv:2608.06488v1 Announce Type: new Abstract: Difficulty in haptic feedback for surgical robots has been a long-term problem for decades. In recent years, learning-based force estimation from robot

researcharxiv-cs-ro

A Finite E-Group of Nilpotency Class Three

arXiv:2608.07275v1 Announce Type: cross Abstract: A group is an E-group if every element commutes with each of its endomorphic images. Caranti asked whether a finite E-group can have nilpotency class

A foundation-model approach to pediatric headache classification from rs-fMRI

arXiv:2608.07287v1 Announce Type: new Abstract: Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functiona

researcharxiv-cs-lg

A Haptic Robot Finger Designed for Guqin Instrument Playing

arXiv:2608.07002v1 Announce Type: new Abstract: With the rapid advancement of humanoid robotics and embodied intelligence technologies, numerous musical instrument-playing robots have emerged in recen

researcharxiv-cs-ro

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

arXiv:2608.07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity,

local-aiarxiv-cs-ai

A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers

arXiv:2608.06694v1 Announce Type: new Abstract: Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is la

A Physics-Inspired Classical Digital Twin of Cortical Dynamics: A Band-Stratified Metriplectic Port-Hamiltonian Neural Network Learned from Brain-Computer-Interface EEG

arXiv:2607.10439v3 Announce Type: replace-cross Abstract: We present a physics-inspired classical digital twin of brain-computer- interface (BCI) data: a graph neural network constrained to a band-str

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608.07427v1 Announce Type: new Abstract: LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network anal

A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation

arXiv:2606.15059v2 Announce Type: replace Abstract: Simultaneous speech-to-speech translation (SimulS2ST) enables real-time cross-lingual communication, but existing evaluation has focused largely on

safetyarxiv-cs-cl

A primer on optimal transport for causal inference with observational data

arXiv:2503.07811v3 Announce Type: replace-cross Abstract: The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ra

A proximal subgradient method for nonconvex stochastic optimization under the Kurdyka-{L}ojasiewicz condition

arXiv:2608.05460v1 Announce Type: cross Abstract: This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and

researcharxiv-cs-lg

A Rate Separation for Agnostic Direct Sums

arXiv:2608.06951v1 Announce Type: new Abstract: Hanneke, Moran, and Waknine ite{HannekeMoranWaknine2024} asked how the agnostic PAC learning curve of the direct sum C^r depends on the single-instance

researcharxiv-cs-lg