Search: “model-releases”

ESPO: Early-Stopping Proximal Policy Optimization

arXiv:2605.29860v1 Announce Type: cross Abstract: When a large language model under reinforcement learning commits a wrong reasoning step early in a trajectory, standard algorithms force it to keep ge

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Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR

DGX agent

arXiv:2605.29637v1 Announce Type: new Abstract: Large language models recall knowledge reliably in English but often fail on the same query posed in a lower-resourced language -- a crosslingual consis

model-releasesarxiv-cs-cl

29 May 2026

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach

arXiv:2511.19316v2 Announce Type: replace-cross Abstract: Recent fine-tuning techniques for diffusion models enable them to reproduce specific image sets, such as particular faces or artistic styles,

EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation

arXiv:2605.29977v1 Announce Type: new Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by ex

Evolutionary Dynamics of Cooperation in Next-Generation LLM Agent Systems: A Cross-Provider Empirical Extension

arXiv:2605.29874v1 Announce Type: cross Abstract: Do next-generation LLM agents inherit the cooperative biases documented in their predecessors, or does scale and provider diversity reshape equilibriu

Evolutionary Rule Extraction from Corporate Default Prediction Models

arXiv:2605.29478v1 Announce Type: cross Abstract: Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerabil

ExCAM: Explainable Cultural Awareness Metrics

arXiv:2605.29897v1 Announce Type: new Abstract: Evaluating the cultural awareness of large language models is crucial to ensure the fairness of generated text and the generalizability of applications

Fairness-Aware Federated Learning with Trajectory Shapley Value

arXiv:2605.30336v1 Announce Type: new Abstract: Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple

Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging

arXiv:2605.29827v1 Announce Type: new Abstract: Medical image analysis models can exhibit performance disparities across patient subgroups, threatening clinical safety and fairness. Existing methods t

FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models

arXiv:2511.11505v3 Announce Type: replace Abstract: Blocking communication presents a major hurdle in running MoEs efficiently in distributed settings. To address this, we present FarSkip-Collective w

Feature Geometry of LoRA Adapters: A Sparse Autoencoder Analysis of Representational Divergence in Fine-Tuned Language Models

arXiv:2605.28896v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has emerged as a widely adopted approach for adapting large language models, yet the internal representational changes induce

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning

arXiv:2605.29002v1 Announce Type: new Abstract: Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories

FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions

arXiv:2605.29427v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in financial services, a single non-compliant interaction can expose institutions to regulator

FinVerBench: Benchmark Validity and Calibration in Large Language Model Financial Statement Verification

arXiv:2605.29586v1 Announce Type: new Abstract: We introduce FinVerBench, a benchmark and validity study for financial statement verification: determining whether a set of corporate financial statemen

First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope

arXiv:2605.28916v1 Announce Type: cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a si

FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning

arXiv:2605.29317v1 Announce Type: new Abstract: Parameter-efficient fine-tuning(PEFT) has largely focused on LoRA and its accuracy-oriented variants, leaving the original goal of reducing trainable pa

FormInv: A Measurement Protocol for Semantic Invariance in Mathematical Reasoning Benchmarks

arXiv:2605.29001v1 Announce Type: cross Abstract: A paraphrase-quality audit of MathCheck (ICLR 2025) detected 4 semantically incorrect paraphrases in 129 groups (3.1%); removing them drops GPT-4o fro

FPLIER: Federated Pathway-Level Information Extractor

arXiv:2605.29587v1 Announce Type: cross Abstract: In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on la

From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions

arXiv:2507.21429v3 Announce Type: replace-cross Abstract: We study local linear convergence of gradient descent for finite-width feedforward networks under the squared empirical loss. Prior work shows

From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks

arXiv:2605.29768v1 Announce Type: new Abstract: Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by y

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration

GenEraser: Generalizable Video Object Removal via Balanced Text-Mask Guidance and Decoupled Locator-Preserver

arXiv:2605.30045v1 Announce Type: new Abstract: Video object removal frequently struggles to simultaneously eliminate target objects and their associated physical effects (e.g., smoke, reflections, li

GEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization

arXiv:2605.29107v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a gr

Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders

arXiv:2605.30022v1 Announce Type: cross Abstract: Positional encoding (PE) underpins how permutation-invariant Transformers represent sequence order, yet how positional information is processed and st

Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning

arXiv:2602.01058v2 Announce Type: replace-cross Abstract: Post-training of reasoning LLMs is a holistic process that typically consists of an offline SFT stage followed by an online reinforcement lear

GPF-LiveNews: A Streaming Evaluation Protocol for Group-Conditioned Framing in Large Language Models

arXiv:2605.28848v1 Announce Type: cross Abstract: Deployed language models are evaluated in a non-stationary environment: model versions, retrieval layers, safety systems, and real-world inputs all ch

GPIC: A Giant Permissive Image Corpus for Visual Generation

arXiv:2605.30341v1 Announce Type: cross Abstract: Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image

Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training

arXiv:2605.29494v1 Announce Type: new Abstract: Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradie

Gram: Assessing sabotage propensities via automated alignment auditing

arXiv:2605.30322v1 Announce Type: cross Abstract: We introduce Gram, an automated alignment auditing framework to assess the propensity of AI agents to engage in sabotage. We evaluate Gemini models ac

GRASP: Gated Regression-Aware Skill Proposer for Self-Improving LLM Agents

arXiv:2605.29668v1 Announce Type: new Abstract: LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the

GroundAct: Can LLM Agents Ground Actions in Environmental States?

arXiv:2508.05614v2 Announce Type: replace-cross Abstract: LLM agents achieve 85-96% success on tasks where instructions fully specify the action, but drop to 29-53% when action feasibility depends on

GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human

arXiv:2605.28882v1 Announce Type: cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important. However,

GTA: Generating Long-Horizon Tasks for Web Agents at Scale

arXiv:2605.29218v1 Announce Type: new Abstract: Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limi

GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing

arXiv:2605.29532v1 Announce Type: cross Abstract: Exploratory GUI testing is a particularly demanding setting for MLLM agents: without predefined test scripts, an agent must autonomously navigate an a

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605.28910v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect stat

Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching

arXiv:2605.29055v1 Announce Type: new Abstract: Hallucination remains a major reliability barrier for production LLM systems, particularly in multi-agent pipelines where unsupported claims can propaga

HEART-Bench: Do LLM Agents Exhibit Human-like Psychology?

arXiv:2605.30058v1 Announce Type: new Abstract: While LLM agents have demonstrated remarkable task-oriented abilities such as planning, reasoning, and action, few works have treated them as complete h

Hista and Numca: Estimate State Value Effectively for LLM Reinforcement Learning

arXiv:2605.29782v1 Announce Type: cross Abstract: Reinforcement learning (RL) refines large language models (LLMs) by directly optimizing model behavior through reward signals. While accurate state va

How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions

arXiv:2605.29442v1 Announce Type: cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectories that m

How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning

arXiv:2602.02103v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning has become a central mechanism for eliciting multi-step reasoning in Large Language Models (LLMs). Yet recent

How LoRA Remembers? A Parametric Memory Law for LLM Finetuning

arXiv:2605.30260v1 Announce Type: cross Abstract: Large Language Models (LLMs) must continuously learn and update knowledge to remain effective in dynamic real-world environments. While Low-Rank Adapt

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency

arXiv:2605.30096v1 Announce Type: cross Abstract: Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials

iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis

arXiv:2605.30179v1 Announce Type: cross Abstract: Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not

Improving Adversarial Robustness of Attribution via Implicit Regularization

arXiv:2605.29983v1 Announce Type: cross Abstract: The adversarial robustness of attributions is a fundamental requirement for reliable explainability in deep learning, yet existing approaches typicall

Improving Full Waveform Inversion in Large Model Era

arXiv:2603.00377v2 Announce Type: replace Abstract: Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismi

Indexing the Unreadable: LLM-Native Recursive Construction and Search of Service Taxonomies

arXiv:2605.29270v1 Announce Type: new Abstract: The era of the Internet of Agents (IoA) is taking shape: LLM agents are expected to fulfill user goals by orchestrating fast-growing populations of Mode

Inferring the Size of Large Language Models From Popular Text Memorization

arXiv:2605.29223v1 Announce Type: new Abstract: The parameter counts of the most widely used large language models (LLMs) are often withheld by their developers, leaving model size -- a primary refere

Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles

arXiv:2605.29473v1 Announce Type: cross Abstract: Language models are increasingly being deployed for conversational support in informal caregiving contexts, where interactions often extend beyond inf