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Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model

arXiv:2606.26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector:

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HyperDFlash: MHC-Aligned Block Speculative Decoding with Gated Residual Reduction

DGX agent

arXiv:2606.26744v1 Announce Type: cross Abstract: We present HyperDFlash, a block-parallel speculative decoding framework tailored to the novel multi-hyper-connection (MHC) architecture proposed by De

model-releasesarxiv-cs-cl

26 Jun 2026

IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control

arXiv:2606.26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based

Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interaction

arXiv:2606.26829v1 Announce Type: new Abstract: Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world au

applicationsarxiv-cs-cv

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

model-releasesarxiv-cs-lg

arXiv:2606.26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run-

Improved Bounds for Private and Robust Alignment

arXiv:2512.23816v2 Announce Type: replace-cross Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the

Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization

arXiv:2606.27025v1 Announce Type: new Abstract: Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant par

safetyarxiv-cs-cl

Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision

arXiv:2606.26801v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervisio

safetyarxiv-cs-cv

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

arXiv:2606.26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off

Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure

arXiv:2606.26588v1 Announce Type: new Abstract: A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferenc

safetyarxiv-cs-ro

Information-Aware KV Cache Compression for Long Reasoning

arXiv:2606.26875v1 Announce Type: cross Abstract: Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling an

Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation

arXiv:2606.27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data. We show that

Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems

arXiv:2606.26356v1 Announce Type: new Abstract: Practitioners of prompt-composed agentic systems report a recurring failure mode: editing one prompt module silently shifts the behavior of others despi

Interpreting 'Interpretability' and Explaining 'Explainability' in Machine Learning in Physics

arXiv:2606.26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning t

researcharxiv-cs-lg

Intracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning

arXiv:2606.26706v1 Announce Type: new Abstract: Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both

researcharxiv-cs-cv

Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning

arXiv:2606.27002v1 Announce Type: cross Abstract: This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convo

researcharxiv-cs-ai

Investigating LLM's Problem Solving Capability -- a Study on Statics Questions

arXiv:2606.26103v1 Announce Type: cross Abstract: Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete as

applicationsarxiv-cs-ai

Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries

model-releasesarxiv-cs-cl

arXiv:2606.26936v1 Announce Type: cross Abstract: With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ('the average Jane') could elicit acti

Local Ai

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

arXiv:2606.27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has

local-aiarxiv-cs-ai

Local Ai

Joint Reward Modeling: Internalizing Chain-of-Thought for Efficient Visual Reward Models

arXiv:2602.07533v2 Announce Type: replace Abstract: Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative mod

local-aiarxiv-cs-ai

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

arXiv:2606.27023v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual

safetyarxiv-cs-cl

Kalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines

arXiv:2606.26710v1 Announce Type: new Abstract: Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact. However,

applicationsarxiv-cs-ai

KARLA: Knowledge-base Augmented Retrieval for Language Models

arXiv:2606.26807v1 Announce Type: new Abstract: We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation. This means that (1)

KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

arXiv:2606.26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pat

Know2Guess: A Contamination-Aware Multi-Zone Benchmark for Knowledge-Boundary Evaluation in Large Language Models

arXiv:2606.26101v1 Announce Type: cross Abstract: Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contami

Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking

arXiv:2606.26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records,

Kolmogorov Arnold networks (KAN) for aerodynamic prediction: a comparison with MLPs and GNNs

arXiv:2606.27126v1 Announce Type: new Abstract: Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation f

researcharxiv-cs-lg

KRVF: A Source-Aware Semantic Voxel World Representation for Edge Mobile Manipulation

arXiv:2606.26321v1 Announce Type: new Abstract: Mobile manipulators need world models that are current, queryable, semantically meaningful, and usable under edge-compute constraints. This technical re

researcharxiv-cs-ro

LA4VLA: Learning to Act without Seeing via Language-Action Pretraining

model-releasesarxiv-cs-ro

arXiv:2606.27295v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to

Agents

Lacuna: A Research Map for Machine Learning

arXiv:2606.26246v1 Announce Type: cross Abstract: Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements, research

agentsarxiv-cs-ai

LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

arXiv:2606.26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existin

Language-Based Digital Twins for Elderly Cognitive Assistance

arXiv:2606.27334v1 Announce Type: new Abstract: Digital twins have emerged as a promising paradigm for personalized healthcare, enabling modeling of individual behavior and health trajectories. In cog

applicationsarxiv-cs-ai

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

model-releasesarxiv-cs-lg

arXiv:2606.26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by part

Latent-Mark: An Audio Watermark Robust to Neural Codec Compression

arXiv:2603.05310v3 Announce Type: replace-cross Abstract: While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they

researcharxiv-cs-ai

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2606.26379v1 Announce Type: new Abstract: Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks.

Layered Outer-Loop Control for Disturbance-Robust Multi-Waypoint UAV Arrival

model-releasesarxiv-cs-ro

arXiv:2606.26315v1 Announce Type: new Abstract: Disturbance-robust UAV position control is easy to demonstrate in benign simulations but much harder to make fast in approach, well behaved near the tar

LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration

arXiv:2606.26647v1 Announce Type: new Abstract: 3D/2D registration serves as a cornerstone technique in surgical navigation. Traditional iterative optimization algorithms suffer from low efficiency an

safetyarxiv-cs-cv

LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation

arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi

LCG: Long-Context Consistent Image Generation with Sparse Relational Attention

arXiv:2606.26171v1 Announce Type: cross Abstract: Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs,

LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration

arXiv:2606.26778v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature ca

researcharxiv-cs-cv

Local Ai

Learning Adversarial Augmentation Policies for Robust Garlic Seedling Detection

arXiv:2606.26828v1 Announce Type: new Abstract: Accurate seedling detection during early growth stages is essential for timely replanting and effective crop management in precision agriculture. Howeve

local-aiarxiv-cs-cv

Learning from a Biased Sample

arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as th

safetyarxiv-cs-lg

Learning from Equivalence Queries, Revisited

arXiv:2604.04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction,

researcharxiv-cs-lg

Learning from the Self-future: On-policy Self-distillation for dLLMs

arXiv:2606.18195v2 Announce Type: replace Abstract: On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs

safetyarxiv-cs-cl

Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification

model-releasesarxiv-cs-cv

arXiv:2601.12062v2 Announce Type: replace Abstract: The core of video-based visible-infrared person re-identification (VVI-ReID) lies in learning sequence-level modal-invariant representations across

Learning Long-Range Dependencies with Temporal Predictive Coding

model-releasesarxiv-cs-lg

arXiv:2602.18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for

Learning Motion Feasibility from Point Clouds in Cluttered Environments

arXiv:2606.26700v1 Announce Type: cross Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this

Learning Probabilistic Filters with Strictly Proper Scoring Rules

arXiv:2606.26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical

safetyarxiv-cs-lg