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Divergent strategies and convergent outcomes in autonomous materials discovery

arXiv:2609.23957v1 Announce Type: new Abstract: Scientific agents are mostly evaluated on whether they complete tasks or recover known results; we instead study variation across repeated open-ended ca

agentsarxiv-cs-ai

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Human DGX agent

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Agents

Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning

DGX agent

arXiv:2505.13994v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy tra

agentsarxiv-cs-ai

23 Sept 2026

Djinnlang: Higher-Level Programming by Unambiguous Specification with an LLM in the Compiler

arXiv:2609.23954v1 Announce Type: cross Abstract: Programmers write formal specifications, and LLMs implement them, proving that each implementation matches its spec. Taken to its extreme, this makes

Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs

arXiv:2601.06362v3 Announce Type: replace Abstract: Personalized large language models are often expected to follow explicit style instructions, yet we find that such instructions can undermine the us

Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation

arXiv:2609.23201v1 Announce Type: new Abstract: Benchmark scores increasingly influence the development, marketing, and selection of large language models (LLMs). Yet an overall score is interpretable

Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation

arXiv:2609.25010v1 Announce Type: cross Abstract: Marketers increasingly use large language models (LLMs) as 'synthetic personas' to predict how an audience will react to a piece of copy before it shi

Do Vision Model See Like the Brain? A Comparison Across EEG Encoding Model

arXiv:2609.26512v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) and vision transformers are both used to model the human visual system, but whether the two architectures diverge a

researcharxiv-cs-cv

DocMIDE: Learning Multi-Hop Implicit Derivation in Visually Rich Documents

arXiv:2609.24092v1 Announce Type: new Abstract: Real-world document processing systems rely on rigid, predefined schemas, yet critical target fields often lack direct visual counterparts on the page.

Domain-Adaptive Pretraining Enhances Water Treatment Semantic Representation for Large-Scale Structured Literature Mining

arXiv:2609.26034v1 Announce Type: new Abstract: Water treatment research is expanding rapidly, but much of the knowledge acquired from this research remains scattered across unstructured literature. T

Double Descent and Malign Overfitting in Diffusion Models

arXiv:2609.26392v1 Announce Type: new Abstract: Conventional wisdom in deep learning holds that overparameterization---having more parameters p than training samples n---is benign: larger models gener

safetyarxiv-cs-lg

DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting

arXiv:2607.10912v2 Announce Type: replace Abstract: 3D Gaussian Splatting represents scenes as finite mixtures of anisotropic Gaussians whose number of components K is set by heuristic density control

researcharxiv-cs-cv

Local Ai

Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization

arXiv:2609.26423v1 Announce Type: new Abstract: This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in SE(3) against 3D lidar maps.

local-aiarxiv-cs-ro

DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Driving

model-releasesarxiv-cs-ro

arXiv:2609.26792v1 Announce Type: new Abstract: Faithfully evaluating end-to-end driving policies in simulation requires observations that are not merely photo-realistic, but preserve the scene featur

DTOC: Dynamic Tool Output Compression for Adaptive Context Management in AI Agents

arXiv:2609.26121v1 Announce Type: cross Abstract: As agent capabilities have grown, practical limitations increasingly stem from constrained context windows rather than model capacity. Common strategi

Dual Covariance Gaussian Splatting SLAM: Decoupling Rendering and Registration for Robust Real-Time Tracking

arXiv:2609.25746v1 Announce Type: new Abstract: ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covariance

researcharxiv-cs-ro

Dual-GNN Multilevel Coarsening for Maximum Independent Set

arXiv:2609.25149v1 Announce Type: new Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive. Researchers often employ graph sparsificatio

Dual-Locking Learned AI Models: A PIN-Based Sparse QIM Watermarking and Adaptive Index Permutation Approach

arXiv:2609.22981v1 Announce Type: cross Abstract: We present a dual-locking method for securing trained neural networks that combines key-driven index permutation with PIN-based watermarking based on

safetyarxiv-cs-ai

DUMA-Bench: A Dual-Control Multi-Agent Benchmark for Evaluating LLM Agent Security

arXiv:2609.24662v1 Announce Type: new Abstract: LLM-based agents increasingly operate in environments where they interact with users, tools, and external systems. Yet most security evaluations assume

DVA-Neurons: Design and Verification of Adaptive LIF Neurons: From Single-Neuron Dynamics to Multi-Neuron Spiking Networks

arXiv:2609.22775v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer a promising path toward ultra-low-power artificial intelligence inference by emulating the event-driven computation

DynaForge: Planning-Guided Residual Learning for Dynamic Manipulation Demonstration Generation

arXiv:2609.25631v1 Announce Type: new Abstract: Dynamic object manipulation is essential for robots operating in real-world environments, yet methods for generating high-quality demonstrations remain

safetyarxiv-cs-ro

Dynamic Deep Prompt Optimization for Defending Against Jailbreak Attacks on LLMs

arXiv:2609.26185v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit har

safetyarxiv-cs-cl

Echo State Network (ESN) for Signal Recovery in RF-Impaired IBFD MIMO Systems

arXiv:2609.23945v1 Announce Type: new Abstract: In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, impr

EDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation

arXiv:2609.24115v1 Announce Type: new Abstract: Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diver

Effects of Assistance Delay on Joint Mechanics and Energetics in Biological Torque Control of a Hip Exoskeleton

arXiv:2609.25417v1 Announce Type: new Abstract: Biological torque control directly maps an estimated human joint moment to exoskeleton assistance, providing a task-agnostic strategy for supporting div

researcharxiv-cs-ro

Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices

arXiv:2609.25645v1 Announce Type: cross Abstract: Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configurat

Efficient Iterative Retrieval with Heterogeneous Batching

arXiv:2609.25405v1 Announce Type: cross Abstract: Modern information retrieval increasingly employs both embedding and generative models to handle complex queries. However, current serving systems suf

ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy

arXiv:2412.03800v2 Announce Type: replace Abstract: Reinforcement learning agents depend on reward signals whose density is rarely under the designer's control, and when such signals are absent, an ag

agentsarxiv-cs-lg

EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion

arXiv:2609.26039v1 Announce Type: cross Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing

researcharxiv-cs-lg

EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding

arXiv:2609.25582v1 Announce Type: new Abstract: Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pretr

Empirical Auditing of Edge-Private Graph Generators

arXiv:2609.25155v1 Announce Type: cross Abstract: We empirically audit privacy leakage by testing whether outputs from edge-neighbouring inputs remain distinguishable, using statistically valid lower

researcharxiv-cs-lg

End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks

arXiv:2609.25044v1 Announce Type: cross Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom).

researcharxiv-cs-lg

End-to-End Visual Odometry with RNNs and Attention

arXiv:2609.26188v1 Announce Type: new Abstract: Video Odometry (VO) is the process of estimating the ego-motion of an object by analyzing visual information such as a sequence of frames from one or mu

agentsarxiv-cs-cv

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

arXiv:2606.19980v2 Announce Type: replace Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bo

Enriching Speech Emotion Representations with Conversational Context

arXiv:2609.26422v1 Announce Type: new Abstract: Detecting emotions is necessary for building systems that can accurately and adaptively interact with humans. Speech Emotion Recognition (SER) has becom

Local Ai

Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow

arXiv:2609.25131v1 Announce Type: new Abstract: Uniform discrete flow permits repeated updates at every generation position. While continued revision supports correction of wrong tokens, it also expos

local-aiarxiv-cs-lg

Epi-Logic: A Conceptual Framework for Epistemic Runtime Control, Schema Validity Checking, and Controlled Accommodation in Autonomous AI Agents

arXiv:2609.24755v1 Announce Type: new Abstract: Autonomous AI agents are increasingly deployed in areas where wrong decisions are hard to reverse. This paper examines schema mismatch: the condition in

agentsarxiv-cs-ai

Applications

EquiSELD: Efficient training of equivariant sound event localization and detection networks

arXiv:2609.23156v1 Announce Type: cross Abstract: First-order Ambisonics (FOA) signals exhibit exact O(3) symmetry: The rotation or reflection of the FOA signal modifies the direction of arrival of th

applicationsarxiv-cs-ai

EquivSVA: A Formally Verified Dataset of Behavioral Assertions Across Equivalent RTL Implementations

arXiv:2609.26751v1 Announce Type: new Abstract: Large language models are increasingly used to generate SystemVerilog Assertions from natural-language specifica- tions and register-transfer-level desi

Error Bounds for Statistical Estimators in BTL Model with Parametric Multivariate Utility Functions

arXiv:2609.26326v1 Announce Type: cross Abstract: We study preference elicitation under the Bradley-Terry-Luce (BTL) model where the true partworth vector is unknown and has to be estimated as a param

Et Tu, Brute? Economic Misalignment in Personal AI Agents

arXiv:2609.24927v1 Announce Type: new Abstract: Personal AI agents make recommendations and take actions on people's behalf in high-stakes economic contexts, e.g., buying a flight, choosing health ins

Evaluating Accuracy and Probabilistic Reliability of Zero-Shot Time Series Foundation Models

arXiv:2609.25788v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) promise a paradigm shift toward zero-shot forecasting by eliminating task-specific training. However, existing wor

Evaluating the Effectiveness of SechKAN on 1D Data

arXiv:2609.25876v1 Announce Type: new Abstract: The connection between the Kolmogorov-Arnold representation theorem (KART) and neural network design has led to the development of Kolmogorov-Arnold Net

Evaluating the Semantic-to-Geometric Gap in Adversarial Defenses Against Vision-Language Model-Based Plagiarism

arXiv:2609.26733v1 Announce Type: new Abstract: The rapidly advancing capabilities of vision-language models (VLMs) present a systemic challenge to academic integrity. VLMs now allow students to bypas

researcharxiv-cs-cv

Evidence-gated multimodal parsing and vectorization of architectural floor plans

model-releasesarxiv-cs-cv

arXiv:2609.25615v1 Announce Type: new Abstract: Architectural floor plans remain a high-friction barrier to archive digitization and early design-model preparation because heterogeneous graphics encod

EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability

arXiv:2609.22537v1 Announce Type: new Abstract: Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) over

safetyarxiv-cs-ai

Expanding the Workspace of Electromagnetic Navigation Systems Using Dynamic Feedback for Single- and Multi-agent Control

arXiv:2511.18486v2 Announce Type: replace Abstract: Electromagnetic navigation systems (eMNS) enable a number of magnetically guided surgical procedures. A challenge in magnetically manipulating surgi

safetyarxiv-cs-ro

Expansion Counts under Standard A* Tie-Breaking Strategies on the Final Plateau

arXiv:2609.23293v1 Announce Type: new Abstract: In the A* search algorithm, the tie-breaking strategies for nodes with the same f-value determines which states A* expands on the final f-layer. For nin

Applications

Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery

arXiv:2609.23877v1 Announce Type: new Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users freq

applicationsarxiv-cs-ai