All entries

arXiv:2609.11063v1 Announce Type: cross Abstract: Large language models learn similar behaviours, yet it remains unclear what structure they share or how to change one behaviour without disturbing oth

tutorialsarxiv-cs-cl

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement

DGX agent

arXiv:2609.11873v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and t

researcharxiv-cs-cl

11 Sept 2026

The Menu Is an Execution Prior: State-Path Tool Menus for Online Agents

arXiv:2609.09395v1 Announce Type: new Abstract: Language models act through tools, yet practical agents face libraries containing thousands of interfaces. We introduce the tool menu as the short, orde

agentsarxiv-cs-ai

The observational partial order of causal structures with latent variables

arXiv:2502.07891v3 Announce Type: replace-cross Abstract: For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distribution

The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems

arXiv:2609.11146v1 Announce Type: cross Abstract: AI-generated text is flowing back into the training corpora of the next generation of models. Recursive training on it drives model collapse, and rece

safetyarxiv-cs-cl

The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies

arXiv:2509.18052v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain lar

agentsarxiv-cs-cl

The Platonic brain bridge hypothesis: human brain networks as an architectural prior for omni models

model-releasesarxiv-cs-lg

arXiv:2609.10947v1 Announce Type: cross Abstract: We propose the Platonic brain bridge hypothesis: omni models, which process video, audio and text jointly like the brain, converge on brain-like repre

The Semantic Elevation Operator and the Closure of the Undecidable Class under Preservation

arXiv:2609.11326v1 Announce Type: cross Abstract: The undecidability of a program's static semantic properties is governed by Rice's theorem. Self-modifying systems, however, require analysing not whe

The Truth Was Never Gone: Perfect Aliasing in Compliant-Context Truth Probes

arXiv:2609.10739v1 Announce Type: cross Abstract: A truth probe fitted where truthful reporting and a task's prescribed action coincide cannot distinguish those targets from its fitting labels alone.

The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption

arXiv:2609.09560v1 Announce Type: cross Abstract: This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-

researcharxiv-cs-ai

The widening evaluation gap in medical large language model research 2023 to 2026

arXiv:2609.11770v1 Announce Type: new Abstract: Large language models are superseded every few quarters; clinical evidence takes years. We asked whether medical research is keeping pace with the syste

Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

arXiv:2609.10745v1 Announce Type: new Abstract: Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work m

Local Ai

Thinking with Looped Flows

arXiv:2609.11801v1 Announce Type: new Abstract: Humans and machines often solve harder problems by spending more time on computation. In deep learning, looped models implement this idea during inferen

local-aiarxiv-cs-lg

Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret

arXiv:2609.10981v1 Announce Type: new Abstract: Bakhtiari, Lattimore and Szepesvari (COLT 2025) proved that Thompson sampling (TS) has Bayesian regret ilde O(d^{5/2}sqrt n) for bandit convex optimisat

Time-Varying Graph Learning with Constraints on Graph Temporal Variation

arXiv:2001.03346v4 Announce Type: replace-cross Abstract: We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal beh

Topological Necessities: Mechanism-Invariant Strategic Subgoals for Cross-Embodiment Goal-Conditioned Control

arXiv:2609.11014v1 Announce Type: new Abstract: Long-horizon goal-conditioned reinforcement learning delegates control to a high-level module that proposes subgoals, but existing subgoals are implicit

Toward Interpretable Multimodal Fusion: Heat Conduction Modeling for Hyperspectral and LiDAR Joint Classification

arXiv:2609.11040v1 Announce Type: new Abstract: The fusion of hyperspectral (HS) and Light Detection and Ranging (LiDAR) data plays a crucial role in enhancing land-cover classification by jointly exp

Applications

Towards AI-Driven Policing: Interdisciplinary Knowledge Discovery from Police Body-Worn Camera Footage

arXiv:2504.20007v4 Announce Type: replace-cross Abstract: This paper proposes a novel interdisciplinary framework for analyzing police body-worn camera (BWC) footage from the Rochester Police Departme

applicationsarxiv-cs-cv

Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation

arXiv:2601.15645v2 Announce Type: replace Abstract: Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing stu

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

arXiv:2609.10806v1 Announce Type: new Abstract: The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehi

agentsarxiv-cs-cv

TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputs

arXiv:2609.11399v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for machine translation, yet their outputs often contain additional text beyond the translation itsel

Tri-DehazeGS: Scene--Medium Decoupled Gaussian Splatting with Transmittance-Aware Optimization

arXiv:2609.11223v1 Announce Type: new Abstract: Recovering clean 3D scenes from hazy multi-view images is challenging because haze attenuates scene radiance and introduces atmospheric scattering. Rece

Trust Me, I'm Your Developer: Self-Issued Authentication in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2609.03247v1 Announce Type: cross Abstract: Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to ver

Two-Parameter Flow Map Learning for Continuous-Time Diffeomorphic Image Registration

arXiv:2609.10789v1 Announce Type: new Abstract: Diffeomorphic image registration is central to medical image analysis, enabling anatomically consistent alignment across subjects. Most learning-based d

UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors

arXiv:2512.10835v2 Announce Type: replace Abstract: This paper introduces a reinforcement learning framework that enables controllable and diverse player behaviors without relying on human gameplay da

safetyarxiv-cs-lg

UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasound

arXiv:2609.11506v1 Announce Type: new Abstract: Three-dimensional ultrasound (US) is a safe, radiation-free complementary modality to CT and X-rays for longitudinal monitoring, yet its segmentation-de

Local Ai

Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation

arXiv:2609.11265v1 Announce Type: new Abstract: Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse: under the same prompt, diffe

local-aiarxiv-cs-cv

Understanding In-Context Multimodal Jailbreaks via Posterior Reweighting

arXiv:2609.10613v1 Announce Type: cross Abstract: In-context learning (ICL) jailbreaks reveal a critical vulnerability in multimodal large language models (MLLMs): harmful demonstrations in the prompt

safetyarxiv-cs-lg

Hardware

Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

arXiv:2609.10656v1 Announce Type: cross Abstract: Selecting LoRA rank for diffusion fine-tuning requires balancing quality and compute cost. We present a controlled study on CIFAR-10 using a DDPM U-Ne

hardwarearxiv-cs-lg

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

arXiv:2609.11156v1 Announce Type: new Abstract: All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Exist

UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling

arXiv:2609.11875v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have improved robotic manipulation, yet observation-to-action learning remains limited by a funda

researcharxiv-cs-ro

Using Automated Vehicles Operational Data to Confirm Safety and Anticipate Threats

arXiv:2609.11549v1 Announce Type: new Abstract: European Union (EU) policymakers adopted revolutionary data collection provisions for Automated Driving Systems (ADS) in the recently approved regulatio

safetyarxiv-cs-ro

Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

arXiv:2609.10934v1 Announce Type: new Abstract: Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of lingu

V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

arXiv:2602.06034v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improv

agentsarxiv-cs-cv

Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration

arXiv:2609.09418v1 Announce Type: new Abstract: World Action Models (WAMs) couple predictive world modeling with action generation, allowing anticipated future states to guide agent behavior. Although

agentsarxiv-cs-ai

Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

arXiv:2609.11638v1 Announce Type: new Abstract: We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing mode

VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents

arXiv:2609.11390v1 Announce Type: cross Abstract: State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial

agentsarxiv-cs-cl

Vision Transformer-Based Multi-Level Feature Fusion for Multi-Label Sewer Defect Classification

arXiv:2609.11375v1 Announce Type: new Abstract: Automated classification of sewer defects is essential for infrastructure condition assessment and maintenance decision-making, but existing deep learni

Local Ai

Visual-SLAM for the detection of hidden tomatoes in greenhouses by Hierarchical Localization and GLOMAPfor robotized harvesting

arXiv:2609.11766v1 Announce Type: new Abstract: Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or st

local-aiarxiv-cs-ro

Voice or Stereotype? Disentangling Acoustic and Content-Based Gender in Speech-to-Speech Models

arXiv:2609.09263v1 Announce Type: cross Abstract: Speech-to-speech (S2S) models now run inside dubbing, translation, and voice agents. Unlike text models, they hear the speaker's voice, which carries

safetyarxiv-cs-ai

Watermarks Without Verification: AI Text Watermarking After the EU AI Act

model-releasesarxiv-cs-ai

arXiv:2609.09604v1 Announce Type: cross Abstract: On August 2, 2026, the obligations of Article 50 of the EU AI Act took effect, requiring generative AI providers to mark the content their systems pro

Weighted Empirical Risk Minimization for Machine Learning under Long-Range Dependence: Exact Pathwise Rates and Learning-Error Geometry

model-releasesarxiv-cs-lg

arXiv:2609.10767v1 Announce Type: cross Abstract: We develop an exact almost-sure learning theory for smooth parametric models trained by regularly weighted empirical risk minimization on long-range d

What Language is This? Ask Your Tokenizer

arXiv:2602.17655v3 Announce Type: replace Abstract: Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus cura

What Should an Agent Forget? Separating What Is Stored from What Is Used

arXiv:2609.10263v1 Announce Type: new Abstract: Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question.

agentsarxiv-cs-ai

When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification

model-releasesarxiv-cs-lg

arXiv:2512.10147v2 Announce Type: replace Abstract: extbf{Motivation:} Pre-trained protein language models (PLMs) such as ESM-2 have become the default representation for biological sequence tasks, bu

When does a spectral prior help graph learning? Connectivity-loss estimation under road-network disruptions

arXiv:2609.11166v1 Announce Type: new Abstract: Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation

safetyarxiv-cs-lg

When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration

arXiv:2609.10726v1 Announce Type: new Abstract: Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operatin

researcharxiv-cs-ro

When is Test-Time Adaptation Identifiable From Unlabeled Evidence?

arXiv:2609.11235v1 Announce Type: new Abstract: Test-time adaptation (TTA) offers many ways to update a deployed model without labels, but choosing the wrong update can make a strong source model wors