Search: “evaluation”

IFMTBench: A Comprehensive Benchmark for Multilingual Translation Instruction Following

arXiv:2605.28218v1 Announce Type: new Abstract: Modern translation workflows demand more than semantic equivalence. Users routinely require models to preserve JSON or HTML schemas, honor curated gloss

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Research

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

DGX agent

arXiv:2510.15859v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has driven recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be compute

researcharxiv-cs-ai

28 May 2026

Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals

arXiv:2605.27583v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal

safetyarxiv-cs-lg

Informing AI Policy Assessment using Large-Scale Simulation of Interventions

arXiv:2605.27395v1 Announce Type: cross Abstract: As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has be

safetyarxiv-cs-ai

Interpretability-Guided Layer Selection over Subspace Projection: SAEs as Stethoscopes, Not Scalpels, for Raw Task Vector Model Editing

arXiv:2605.28649v1 Announce Type: cross Abstract: LLMs increasingly require surgical model editing to enhance domain-specific capabilities without incurring the computational cost or catastrophic forg

Tutorials

Inversely Learning Transferable Rewards via Abstracted States

arXiv:2501.01669v4 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous

tutorialsarxiv-cs-lg

IPO-Mine: A Toolkit and Dataset for Section-Structured Analysis of Long, Multimodal IPO Documents

arXiv:2605.28714v1 Announce Type: cross Abstract: An Initial Public Offering (IPO) filing is a document released when a private firm goes public, allowing individual (retail) investors to purchase its

JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models

arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their

safetyarxiv-cs-cv

Tutorials

Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies

arXiv:2605.27546v1 Announce Type: new Abstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth di

tutorialsarxiv-cs-cl

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

arXiv:2507.09466v2 Announce Type: replace Abstract: Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating f

researcharxiv-cs-lg

LACUNA: Safe Agents as Recursive Program Holes

arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime o

safetyarxiv-cs-ai

LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation

arXiv:2605.27570v1 Announce Type: new Abstract: Parallel LLM test-time scaling techniques (e.g., best-of-N) require drawing N>1 sequences conditioned on the same input prompt. These methods boost accu

Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles

arXiv:2512.20780v3 Announce Type: replace Abstract: Recent work has explored the use of large language models (LLMs) to generate tutoring responses in mathematics, yet it remains unclear how closely t

agentsarxiv-cs-cl

Learning Compositional Latent Structure with Vector Networks

arXiv:2605.28007v1 Announce Type: cross Abstract: Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult

Tutorials

Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization

arXiv:2605.28309v1 Announce Type: new Abstract: In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but

tutorialsarxiv-cs-lg

Applications

LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments

arXiv:2605.27403v1 Announce Type: cross Abstract: Written reflection assignments give students valuable opportunities for critical self-assessment, meaning making, and learning processing. Additionall

applicationsarxiv-cs-ai

Local Ai

Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values

arXiv:2605.28170v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a

local-aiarxiv-cs-ai

Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization

arXiv:2605.28109v1 Announce Type: new Abstract: Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning task

safetyarxiv-cs-lg

Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection

arXiv:2605.27748v1 Announce Type: cross Abstract: Industrial visual anomaly detection is usually one-class: normal images are abundant, while defects are rare, heterogeneous, and often unavailable dur

MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation

model-releasesarxiv-cs-cv

arXiv:2605.28173v1 Announce Type: new Abstract: End-to-end manga generation is a structured visual storytelling task that requires story decomposition, recurring character and scene grounding, page la

Applications

Measuring Massive Multitask Chinese Understanding

arXiv:2304.12986v3 Announce Type: replace-cross Abstract: The development of large-scale Chinese language models is flourishing, yet there is a lack of corresponding capability assessments. Therefore,

applicationsarxiv-cs-ai

MemCog: From Memory-as-Tool to Memory-as-Cognition in Conversational Agents

arXiv:2605.28046v1 Announce Type: new Abstract: Existing agent memory systems universally follow what we term a Memory-as-Tool paradigm where a single query triggers one-shot retrieval of flat passage

Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization

arXiv:2605.27389v1 Announce Type: cross Abstract: We study how conditioning context shapes personalization behavior in a teacher-facing educational recommender system. We compare contextual conditioni

MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content

arXiv:2605.28116v1 Announce Type: cross Abstract: Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from wh

Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training

arXiv:2605.28467v1 Announce Type: new Abstract: As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jai

safetyarxiv-cs-lg

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation

arXiv:2605.12515v2 Announce Type: replace Abstract: Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's langua

safetyarxiv-cs-cl

Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation

arXiv:2602.03515v2 Announce Type: replace-cross Abstract: Asynchronous pipeline parallelism maximizes hardware utilization by eliminating the pipeline bubbles inherent in synchronous execution, offeri

MRMMIA: Membership Inference Attacks on Memory in Chat Agents

arXiv:2605.27825v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) test whether a target data record belongs to a system's private data, and have become a standard tool to measure p

agentsarxiv-cs-lg

Multi-Agent LLM-based Metamorphic Testing for REST APIs

arXiv:2605.28321v1 Announce Type: cross Abstract: As REST APIs become an increasingly significant part of software systems, their validation is becoming more critical. Hence, testing and uncovering un

agentsarxiv-cs-ai

Applications

Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors

arXiv:2605.27967v1 Announce Type: cross Abstract: Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), inclu

applicationsarxiv-cs-ai

Natural Functional Gradients for Smooth Trajectory Optimization

arXiv:2605.28202v1 Announce Type: new Abstract: Generating collision-free and smooth motions remains a central challenge in robotic manipulation, particularly in cluttered environments and narrow pass

researcharxiv-cs-ro

No Safe Dose: How Training Data Drives Unsafe Image Generation

arXiv:2605.28137v1 Announce Type: new Abstract: Text-to-image models trained on large-scale data often inevitably ingest unsafe content. While some people observe input-output amplifications, it remai

safetyarxiv-cs-cv

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation

arXiv:2605.27616v1 Announce Type: cross Abstract: Real-time anomaly segmentation demands both high recall and efficient low-precision inference. We study the three-way interaction of model architectur

Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking

arXiv:2605.27429v1 Announce Type: cross Abstract: Industrial video-on-demand (VOD) recommenders need richer content understanding, but LLM-as-reranker designs repeat prompt construction, token generat

On Compositional Learning Behaviours in Formal Mathematics

arXiv:2605.28512v1 Announce Type: new Abstract: Self-evolving scientific agents capable of conquering the hard tail of formal mathematics require Compositional Learning Behaviours (CLBs) -- the capaci

On the Origin of Synthetic Information by Means of Steganographic Inheritance

arXiv:2605.27551v1 Announce Type: new Abstract: The origin of species has been the mystery of mysteries in natural science. By analogy, the origin of synthetic information, we suggest, is the mystery

Optimal ridge regularization revisited

model-releasesarxiv-cs-lg

arXiv:2605.28679v1 Announce Type: new Abstract: We consider L^2-regularized linear (ridge) regression over a finite data sample X with bounded covariance and linear prediction targets y with additive

OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis

arXiv:2605.27378v1 Announce Type: new Abstract: Dental image analysis plays a pivotal role in supporting accurate diagnosis and treatment planning in oral healthcare. Although recent advances have pro

Paraphrase Brittleness in Production Retrieval-Augmented Commercial Recommendation: Reproducibility Below the Rerun-Stability Baseline

arXiv:2605.27440v1 Announce Type: cross Abstract: Small changes to how a buyer phrases a question -- 'best CRM' vs 'top CRM' vs 'best CRM for a SaaS startup' -- produce substantially different brand r

Patched-DeltaNet: Token-Level Event-Driven Memory for Linear-Time Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2605.27992v1 Announce Type: new Abstract: Time series anomaly detection is critical for maintaining the reliability of mission-critical systems. While Transformer-based models like PatchTST have

Pattern Recognition Tasks with Personalized Federated Learning

arXiv:2605.27816v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing p

researcharxiv-cs-cv

Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis

arXiv:2605.28037v1 Announce Type: new Abstract: Prompt-based personality control is a key technique for designing large language model (LLM) dialogue agents that behave consistently across social cont

agentsarxiv-cs-cl

PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion

arXiv:2605.28226v1 Announce Type: new Abstract: Small-molecule drug discovery requires simultaneous optimization of numerous properties of candidate molecules. These properties can be investigated thr

researcharxiv-cs-lg

PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management

arXiv:2605.28232v1 Announce Type: new Abstract: Occupant comfort and grid-aware energy efficiency are competing objectives whose joint optimization depends critically on how reward functions are speci

agentsarxiv-cs-ai

Plant, Persist, Trigger: Sleeper Attack on Large Language Model Agents

arXiv:2605.28201v1 Announce Type: new Abstract: Large Language Model (LLM) agents remain vulnerable to safety threats from the external environment, where attackers inject adversarial content into ext

Playing with Words, Improving with Rewards: Training Language Models for Creative Association

arXiv:2605.27832v1 Announce Type: new Abstract: Large Language Models (LLMs) are being applied to increasingly difficult problems and use cases. To navigate their vast solution spaces effectively, LLM

researcharxiv-cs-cl

Preference-Shaped Expected Hypervolume and R2 Improvement: Exact Computation and Monotonicity

arXiv:2605.28746v1 Announce Type: cross Abstract: This paper studies preference-shaped expected improvement criteria for Bayesian multiobjective optimization. We consider two indicator families which

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations

arXiv:2605.27958v1 Announce Type: cross Abstract: Linear probes trained on LLM activations are increasingly proposed as deception-detection metrics, yet report AUROC exceeding 0.96 on clean benchmarks