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arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea

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Reducing the Safety Tax in LLM Safety Alignment with On-Policy Self-Distillation

DGX agent

arXiv:2605.15239v1 Announce Type: new Abstract: Safety alignment often improves robustness to harmful queries at the cost of reasoning ability, a tradeoff known as the safety tax. A common cause is di

model-releasesarxiv-cs-lg

18 May 2026

SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation

arXiv:2605.16117v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities across diverse NLP applications, such as translation, text generation, and question a

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference

arXiv:2605.15488v1 Announce Type: new Abstract: Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appr

Unlocking Dense Metric Depth Estimation in VLMs

arXiv:2605.15876v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text

Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment

arXiv:2605.14311v1 Announce Type: cross Abstract: Test-Time Scaling (TTS), which samples multiple candidate actions and ranks them via a Critic Model, has emerged as a promising paradigm for generalis

Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions

arXiv:2605.14752v1 Announce Type: cross Abstract: Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distr

FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR

arXiv:2605.14920v1 Announce Type: new Abstract: Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe nav

researcharxiv-cs-ro

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

arXiv:2601.21349v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small subset of experts, where the router plays a central

ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World

arXiv:2605.15081v1 Announce Type: cross Abstract: The development of high-quality text embeddings is increasingly drifting toward an exclusionary future, defined by three critical barriers: prohibitiv

Agents

Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition

arXiv:2605.14111v1 Announce Type: new Abstract: Hospital pharmacists make high-stakes decisions to mitigate drug shortages under uncertainty, time pressure, and patient risk. Interviews revealed that

agentsarxiv-cs-ai

Safety

One Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries

arXiv:2605.14605v1 Announce Type: cross Abstract: Model providers increasingly release open weights or allow users to fine-tune foundation models through APIs. Although these models are safety-aligned

safetyarxiv-cs-ai

PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts

arXiv:2605.14002v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long context question answering into op

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model

arXiv:2605.14567v1 Announce Type: cross Abstract: We propose a simple mechanism by which scaling laws emerge from feature learning in multi-layer networks. We study a high-dimensional hierarchical tar

researcharxiv-cs-lg

Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture

arXiv:2605.14448v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have emerged as a powerful backbone for multimodal embeddings. Recent methods introduce chain-of-thought (CoT

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

arXiv:2605.12882v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have significantly advanced document understanding, yet current Doc-VQA evaluations score only the final answ

Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study

arXiv:2605.13138v1 Announce Type: cross Abstract: Automated detection of vulnerability-fixing commits (VFCs) is critical for timely security patch deployment, as advisory databases lag patch releases

From Pixels to BFS: High Maze Accuracy Does Not Imply Visual Planning

arXiv:2603.26839v2 Announce Type: replace-cross Abstract: How do multimodal models solve visual spatial tasks -- through genuine planning, or through brute-force search in token space? We introduce ex

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?

arXiv:2605.12827v1 Announce Type: cross Abstract: Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses t

Local Ai

PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution

arXiv:2605.13027v1 Announce Type: new Abstract: Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character ident

local-aiarxiv-cs-cv

StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos

arXiv:2512.01707v3 Announce Type: replace-cross Abstract: Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realis

Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs

arXiv:2505.11556v4 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are expected to enhance decision-making by pooling distributed information, yet syst

Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents

arXiv:2602.16246v3 Announce Type: replace Abstract: Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Ben

Tutorials

VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

tutorialsarxiv-cs-ai

When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction

arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions

A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length

arXiv:2605.11406v1 Announce Type: new Abstract: Existing granular-ball classification methods are often driven by handcrafted quality measures, neighborhood rules, or heuristic splitting and stopping

Adversarial Causal Tuning for Realistic Time-series Generation

arXiv:2506.02084v2 Announce Type: replace Abstract: We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional d

researcharxiv-cs-lg

AlphaEarth Satellite Embeddings for Modelling Climate Sensitive Diseases Towards Global Health Resilience

arXiv:2605.10949v1 Announce Type: cross Abstract: Malaria, childhood acute respiratory infection, and child undernutrition together account for over two million deaths annually in children under five,

researcharxiv-cs-cv

Drop the Act: Probe-Filtered RL for Faithful Chain-of-Thought Reasoning

arXiv:2605.11467v1 Announce Type: new Abstract: Reasoning models post-hoc rationalize answers they have already committed to internally, producing chains of *reasoning theater*: deliberative-looking s

fg-expo: Frontier-guided exploration-prioritized policy optimization via adaptive kl and gaussian curriculum

arXiv:2605.11403v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, with Group Relative Policy Opti

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training

arXiv:2605.11416v1 Announce Type: new Abstract: Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze o

Safety

Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling

arXiv:2605.11299v1 Announce Type: cross Abstract: Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, of

safetyarxiv-cs-cl

Rethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack

arXiv:2605.11232v1 Announce Type: cross Abstract: Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements dif

RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction

arXiv:2605.11622v1 Announce Type: new Abstract: Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular archit

researcharxiv-cs-cv

Routers Learn the Geometry of Their Experts: Geometric Coupling in Sparse Mixture-of-Experts

arXiv:2605.12476v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (SMoE) models enable scaling language models efficiently, but training them remains challenging, as routing can collapse ont

SAGE: Scalable Automated Robustness Augmentation for LLM Knowledge Evaluation

arXiv:2605.12022v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on standard knowledge evaluation benchmarks, yet recent work shows that their knowledge capabili

SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification

arXiv:2605.11022v1 Announce Type: cross Abstract: Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

arXiv:2605.12015v1 Announce Type: cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools,

Targeted Neuron Modulation via Contrastive Pair Search

arXiv:2605.12290v1 Announce Type: new Abstract: Language models are instruction-tuned to refuse harmful requests, but the mechanisms underlying this behavior remain poorly understood. Popular steering

Safety

Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures

arXiv:2605.10991v1 Announce Type: new Abstract: Existing approaches to LLM personalization focus on constructing better personalized models or inputs, while treating inference as a single-shot process

safetyarxiv-cs-lg

UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration

arXiv:2511.18152v3 Announce Type: replace Abstract: Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited fo

WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting

arXiv:2605.11696v1 Announce Type: new Abstract: Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, t

YFPO: A Preliminary Study of Yoked Feature Preference Optimization with Neuron-Guided Rewards for Mathematical Reasoning

arXiv:2605.11906v1 Announce Type: new Abstract: Preference optimization has become an important post-training paradigm for improving the reasoning abilities of large language models. Existing methods

A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability

arXiv:2605.09121v1 Announce Type: cross Abstract: Agents built on large language models (LLMs) rely on a range of reliability techniques, including retry, majority voting, and self-consistency, that h

BaseCal: Unsupervised Confidence Calibration via Base Model Signals

arXiv:2601.03042v4 Announce Type: replace Abstract: Reliable confidence is essential for trusting the outputs of LLMs, yet widely deployed post-trained LLMs (PoLLMs) typically compromise this trust wi

researcharxiv-cs-cl

BenchCAD: A Comprehensive, Industry-Standard Benchmark for Programmatic CAD

arXiv:2605.10865v1 Announce Type: new Abstract: Industrial Computer-Aided Design (CAD) code generation requires models to produce executable parametric programs from visual or textual inputs. Beyond r

Benchmarking Sensor-Fault Robustness in Forecasting

arXiv:2605.10822v1 Announce Type: new Abstract: Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standard for

Beyond Red-Teaming: Formal Guarantees of LLM Guardrail Classifiers

arXiv:2605.10901v1 Announce Type: new Abstract: Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no formal