Search: “framework”

Prediction Under Imperfect Compression: A Theory of Approximate MDL

arXiv:2606.04834v1 Announce Type: new Abstract: Minimum Description Length (MDL) formalizes the principle of Occam's razor by optimizing the total description length: L(model)+L(data | model). For seq

researcharxiv-cs-lg

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Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement

DGX agent

arXiv:2606.04249v1 Announce Type: new Abstract: Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fas

tutorialsarxiv-cs-cv

4 Jun 2026

Provably Auditable and Safe LLM Agents from Human-Authored Ontologies

arXiv:2606.04903v1 Announce Type: cross Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability. Using the ty

Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

model-releasesarxiv-cs-lg

arXiv:2606.04031v1 Announce Type: new Abstract: Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approxim

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606.04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in

QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

model-releasesarxiv-cs-lg

arXiv:2606.04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail p

Quantum entanglement provides a competitive advantage in adversarial games

arXiv:2603.10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question. Competitive zero-s

RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression

model-releasesarxiv-cs-lg

arXiv:2511.21035v2 Announce Type: replace Abstract: Holography offers significant potential for AR/VR applications. However, its adoption is limited by the high demand for data compression. Existing d

Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models

arXiv:2606.04822v1 Announce Type: new Abstract: Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects

applicationsarxiv-cs-lg

ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection

arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable

researcharxiv-cs-cv

REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning

arXiv:2606.04380v1 Announce Type: cross Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a diff

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606.05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and explo

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models

arXiv:2606.04739v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generatio

Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models

arXiv:2502.01576v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce h

safetyarxiv-cs-cv

Robust Multi-view Clustering against Imperfect Information

model-releasesarxiv-cs-cv

arXiv:2606.04343v1 Announce Type: new Abstract: Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV

RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration

arXiv:2606.04248v1 Announce Type: new Abstract: Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying s

safetyarxiv-cs-ro

RUBAS: Rubric-Based Reinforcement Learning for Agent Safety

arXiv:2606.04051v1 Announce Type: cross Abstract: The evolution of LLMs into tool-enabled agents creates a new class of safety challenges associated with real-world execution rather than simple text g

SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding

arXiv:2606.04974v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional conte

researcharxiv-cs-cl

SaliMory: Orchestrating Cognitive Memory for Conversational Agents

arXiv:2606.04120v1 Announce Type: cross Abstract: Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context win

SAM 3D: 3Dfy Anything in Images

arXiv:2511.16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single i

Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems

arXiv:2606.04444v1 Announce Type: cross Abstract: Existing datasets cannot support large-scale learning in multi-agent, multi-sensor, or multi-domain autonomy, where diversity and coordination are ess

agentsarxiv-cs-lg

Scaling Self-Evolving Agents via Parametric Memory

arXiv:2606.04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model

Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing

arXiv:2512.08094v2 Announce Type: replace Abstract: The goal of this work is to develop a universal approach for aligning subtitles (i.e., spoken language text with corresponding timestamps) to contin

safetyarxiv-cs-cl

Self-Distilled Policy Gradient

arXiv:2606.04036v1 Announce Type: new Abstract: On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense su

Self-Evolving Deep Research via Joint Generation and Evaluation

arXiv:2606.04507v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capab

Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models

arXiv:2606.04123v1 Announce Type: cross Abstract: Trajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration. As space missions increase

Local Ai

SemBlock: Semantic Boundary Dynamic Blocks for Diffusion LLMs

arXiv:2606.04964v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text through iterative denoising, and blockwise decoding improves their practicality by committing tokens in l

local-aiarxiv-cs-cl

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models

arXiv:2606.05004v1 Announce Type: cross Abstract: With the widespread deployment of public large language models (LLMs) such as ChatGPT, protecting user prompt privacy has become an increasingly criti

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv:2606.04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fu

SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization

arXiv:2505.11166v3 Announce Type: replace-cross Abstract: Despite advances in pretraining with extended context sizes, large language models (LLMs) still face challenges in effectively utilizing real-

Solving Zebra Puzzles Using Constraint-Guided Multi-Agent Systems

model-releasesarxiv-cs-cl

arXiv:2407.03956v3 Announce Type: replace-cross Abstract: Prior research has enhanced the ability of Large Language Models (LLMs) to solve logic puzzles using techniques such as chain-of-thought promp

SpliceBind: Isoform-Aware Prediction of Binding Pocket Druggability

arXiv:2606.04020v1 Announce Type: cross Abstract: Splice-mediated drug resistance occurs in up to 40% of patients on targeted kinase inhibitors, yet state-of-the-art druggability tools operate on sing

researcharxiv-cs-lg

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space

arXiv:2511.20102v3 Announce Type: replace Abstract: Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse atten

safetyarxiv-cs-cl

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models

arXiv:2606.04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative mas

researcharxiv-cs-lg

Stationarity-Aware Retrieval-Augmented Time Series Forecasting

arXiv:2606.04135v1 Announce Type: new Abstract: Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully param

applicationsarxiv-cs-lg

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

model-releasesarxiv-cs-cl

arXiv:2606.05165v1 Announce Type: cross Abstract: Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventio

StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT

arXiv:2606.04722v1 Announce Type: new Abstract: Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interva

applicationsarxiv-cs-cv

SUSD: Structured Unsupervised Skill Discovery through State Factorization

arXiv:2602.01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards. One of the most co

TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

arXiv:2602.06911v2 Announce Type: replace-cross Abstract: As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications,

Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM

arXiv:2606.04853v1 Announce Type: new Abstract: Low-cost 2D LiDARs lack the intensity channel that higher-end sensors use to diagnose measurement failures, yet they are widely used on educational and

researcharxiv-cs-ro

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

arXiv:2606.04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability. Existing testin

Local Ai

The Biomimetic Architecture of Software 4.0

arXiv:2606.04025v1 Announce Type: cross Abstract: Dominant programming paradigms inherit an execution model optimised for a bygone era of a single human mind instructing a local machine, leaving conte

local-aiarxiv-cs-ai

The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning

arXiv:2606.04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful

The Mechanistic Emergence of Symbol Grounding in Language Models

arXiv:2510.13796v3 Announce Type: replace Abstract: Symbol grounding (Harnad, 1990) describes how symbols such as words acquire their meanings by connecting to real-world sensorimotor experiences. Rec

applicationsarxiv-cs-cl

TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration

arXiv:2606.04743v1 Announce Type: cross Abstract: Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface on

Tomography by Design: An Algebraic Approach to Low-Rank Quantum States

arXiv:2602.15202v2 Announce Type: replace-cross Abstract: We present an algebraic algorithm for quantum state tomography that leverages measurements of certain observables to estimate structured entri

Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs

arXiv:2512.04668v4 Announce Type: replace-cross Abstract: Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introdu

Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification

arXiv:2606.04037v1 Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability bench