Search: “models”

A Learning Stability Profile for Finite-Dimensional Learning Dynamics

arXiv:2512.21208v3 Announce Type: replace Abstract: We develop a finite-dimensional sensitivity framework for studying stability in learning systems whose states include representations, parameters, a

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Research

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism

DGX agent

arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-st

researcharxiv-cs-ai

26 May 2026

Applications

Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints

arXiv:2511.20236v3 Announce Type: replace Abstract: Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a

applicationsarxiv-cs-ai

Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution

arXiv:2605.25814v1 Announce Type: cross Abstract: Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in

Adversarial Error Correction for Visual Autoregressive Generation

arXiv:2605.24843v1 Announce Type: cross Abstract: Visual Autoregressive (VAR) models have emerged as a powerful paradigm for image synthesis by performing hierarchical next-scale prediction. However,

Applications

Afford-VLA: Action-Aligned Visual Planning via Internalized Affordance

arXiv:2605.24203v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential for generalist robot manipulation, yet they remain limited by insufficient spatial reaso

applicationsarxiv-cs-ro

An Effective-Rank Audit of Alignment-Induced Activation Shifts: Confound Control, Constructive Calibration, and Limits

model-releasesarxiv-cs-cl

arXiv:2605.24583v1 Announce Type: cross Abstract: We audit alignment-induced shifts in residual-stream activations of three open-weight instruction-tuned LLMs (Llama-3.1-8B-Instruct, Gemma-2-9B-it, Qw

Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents

model-releasesarxiv-cs-cl

arXiv:2605.25971v1 Announce Type: new Abstract: While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after exp

Architecting Agentic Communities using Design Patterns

arXiv:2601.03624v3 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLM) and subsequent Agentic AI technologies requires systematic architectural guidance for building so

agentsarxiv-cs-ai

Artificial Effort

arXiv:2605.23920v1 Announce Type: cross Abstract: Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experim

researcharxiv-cs-ai

ArtSplat: Feed-Forward Articulated 3D Gaussian Splatting from Sparse Multi-State Uncalibrated Views

arXiv:2605.24304v1 Announce Type: cross Abstract: Articulated object reconstruction from sparse-view images is an ill-posed problem that requires simultaneous inference of geometry and underlying arti

Auditing medical multi-agent AI reveals risks of false consensus

arXiv:2510.10185v2 Announce Type: replace-cross Abstract: Large language models are increasingly being assembled into medical multi-agent systems that emulate multidisciplinary consultation through sp

Authority Signals in Claude AI Health Citations: A Descriptive Analysis Using the Authority Signals Framework

arXiv:2605.23921v1 Announce Type: cross Abstract: This study seeks to determine the authority signals used by Anthropic's Claude AI in its presentation of sources when answering consumer health questi

AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery

arXiv:2605.24183v1 Announce Type: cross Abstract: We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through latent world recovery. AvalancheBench improves on existing benc

Axis-Aligned Semantics for ODRL: Resolving Dimensional Ambiguity in Policy Constraints

model-releasesarxiv-cs-cl

arXiv:2602.19878v3 Announce Type: replace Abstract: The Open Digital Rights Language (ODRL) represents policy constraints as triples of a left operand, an operator, and a value. Several spatial operan

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

arXiv:2605.24423v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT

Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling

arXiv:2605.25143v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to ma

researcharxiv-cs-ai

Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation

arXiv:2605.25937v1 Announce Type: cross Abstract: We present a dataset of adversarial malware samples derived from the public RawMal-TF collection of real-world malware binaries. Using a suite of adve

Byzantine-Robust Federated Learning with Learnable Aggregation Weights

arXiv:2511.03529v2 Announce Type: replace Abstract: Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicio

Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions

arXiv:2605.24055v1 Announce Type: cross Abstract: Real-world time-series data in industrial sensing, healthcare, and energy systems is often corrupted by a mixture of Gaussian noise and occasional lar

Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces

arXiv:2605.25352v1 Announce Type: cross Abstract: Deep learning models are vulnerable to adversarial perturbations, raising important concerns for safety-critical deployment. Empirical defenses can ac

ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning

arXiv:2605.24418v1 Announce Type: new Abstract: Federated learning is used in medical imaging where privacy prohibits centralizing data. Standard federated algorithms assume homogeneous hardware, iden

Applications

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks

arXiv:2605.24340v1 Announce Type: new Abstract: Production deep learning systems across enterprise domains operate under constraints that academic benchmarks routinely obscure: labeled data is expensi

applicationsarxiv-cs-lg

Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings

Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning

arXiv:2605.25006v1 Announce Type: cross Abstract: Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments wit

Local Ai

CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction

arXiv:2506.17326v3 Announce Type: replace Abstract: Class imbalance remains a practical obstacle in the development of clinical prediction models for conditions such as diabetes mellitus, where the nu

local-aiarxiv-cs-lg

Local Ai

CoRe-Code: Collaborative Reinforcement Learning for Code Generation

arXiv:2605.24812v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in code generation, but most methods rely on autoregressive decoding without global planni

local-aiarxiv-cs-ai

CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming

arXiv:2605.24693v1 Announce Type: new Abstract: Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive mu

agentsarxiv-cs-cl

DarkForest: Less Talk, Higher Accuracy for Multi-Agent LLMs

arXiv:2605.25188v1 Announce Type: new Abstract: Multi-agent LLM systems improve reasoning by combining outputs from multiple agents, but interaction-heavy methods can introduce error propagation and h

Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing

arXiv:2605.25221v1 Announce Type: cross Abstract: Reservoir computing typically relies on large, randomly generated reservoirs, enabling simple, often linear readouts. Over the past two decades, most

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

arXiv:2605.24860v1 Announce Type: cross Abstract: Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stabili

Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting

arXiv:2605.24548v1 Announce Type: new Abstract: Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observ

Local Ai

DTO: a Differentiable Training Objective for Effective Counterfactual Story Rewriting

arXiv:2605.24885v1 Announce Type: new Abstract: Counterfactual story rewriting is a natural language processing task that requires updating an existing story to reflect a chosen alternative event, yet

local-aiarxiv-cs-cl

DUEL: Adversarial Self-Play for Multimodal Reasoning

arXiv:2605.24794v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-

researcharxiv-cs-cl

Dynamics Reveals Structure: Challenging the Linear Propagation Assumption

arXiv:2601.21601v2 Announce Type: replace-cross Abstract: Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We invest

ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots

arXiv:2605.24225v1 Announce Type: new Abstract: In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixt

safetyarxiv-cs-ro

EfficientGraph-RAG: Structured Retrieval-State Management for Cross-Task Retrieval-Augmented Generation

arXiv:2605.25379v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the standard way to ground large language models in external knowledge, but many systems still organize

agentsarxiv-cs-cl

Estimating Mixture Distributions via Stochastic Mirror Descent

arXiv:2605.24929v1 Announce Type: cross Abstract: We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss.

EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions

arXiv:2605.24110v1 Announce Type: new Abstract: Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assess

EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery

arXiv:2605.24018v1 Announce Type: new Abstract: Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design

agentsarxiv-cs-ai

Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes

arXiv:2605.24912v1 Announce Type: cross Abstract: Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic

researcharxiv-cs-ai

Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

arXiv:2605.24678v1 Announce Type: new Abstract: Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We prese

Extreme Region Policy Distillation

arXiv:2605.25582v1 Announce Type: cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-polic

Faithful or Fabricated? A Causal Framework for Rationalization Bias in LLM Judges

arXiv:2605.23970v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such

safetyarxiv-cs-cl

Feature Learning in Wide Neural Networks under muP: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit

arXiv:2605.24710v1 Announce Type: new Abstract: We establish four structural results for feature learning in wide two-layer neural networks under the Maximal Update Parametrization (muP). First, we pr

Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs

arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused low-rank adaptation

From DPPs to k-DPPs: identifiability analysis via spectral decomposition

arXiv:2605.25526v1 Announce Type: cross Abstract: We study the geometry of determinantal point processes (DPPs) through the spectral decomposition L=ULambda U^{op}. The spectrum Lambda governs the car

From Reasoning to Code: GRPO Optimization for Underrepresented Languages

arXiv:2506.11027v3 Announce Type: replace-cross Abstract: Generating accurate and executable code using Large Language Models (LLMs) remains a significant challenge for underrepresented programming la