Search: “models”

The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing

arXiv:2606.02184v1 Announce Type: cross Abstract: These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academ

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The Image Reconstruction Game: Drawing Common Ground Through Iterative Multimodal Dialogue

DGX agent

arXiv:2606.01901v1 Announce Type: cross Abstract: We introduce the Image Reconstruction Game, a fully automated benchmark in which a vision-language model issues corrective instructions to an image ge

model-releasesarxiv-cs-ai

2 Jun 2026

Agents

Unsupervised Collaborative Domain Adaptation for Driving Scene Parsing

arXiv:2606.01818v1 Announce Type: new Abstract: Reliable driving scene parsing is a fundamental capability for autonomous vehicles operating in open and dynamic driving environments. However, adapting

agentsarxiv-cs-cv

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

arXiv:2602.16763v2 Announce Type: replace Abstract: Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions. However, benchmarks qui

WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis

arXiv:2606.01869v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly asked not only to write static interfaces, but to construct executable interactive worlds from natural lan

Agents

Before Parc Ferme: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving

arXiv:2605.31256v1 Announce Type: new Abstract: Embodied Large Language Models (LLMs) are increasingly used as reasoning modules in robotic control pipelines to improve human-robot interaction, but th

agentsarxiv-cs-ro

BlueFin: Benchmarking LLM Agents on Financial Spreadsheets

arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet wo

CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference

arXiv:2605.31591v1 Announce Type: new Abstract: Models for AI-based skin cancer screening suffer a severe performance drop when shifting from expert dermoscopic (source) images to consumer-grade clini

Consolidating Rewarded Perturbations for LLM Post-Training

arXiv:2605.31494v1 Announce Type: new Abstract: Post-training of language models is commonly framed as a sample-score-update loop implemented by gradient descent. A recent line of work, exemplified by

CSULoRA: Closest Safe Update Low-Rank Adaptation

arXiv:2605.30640v1 Announce Type: cross Abstract: Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adv

Effective Reasoning Chains Reduce Intrinsic Dimensionality

arXiv:2602.09276v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning and its variants have substantially improved the performance of language models on complex reasoning tasks, y

Tutorials

Eigenvectors of Experts are Training-free Non-collapsing Routers

arXiv:2605.30992v1 Announce Type: new Abstract: Sparse Mixture of Experts (SMoE) architectures improve the training efficiency of Large Language Models (LLMs) by routing input tokens to a selected sub

tutorialsarxiv-cs-lg

EUDAIMONIA: Evaluating Undesirable Dynamics in AI

arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but t

Evaluating using Mock Tool Calls to Quarantine Untrusted Prompt Inputs

arXiv:2605.30521v1 Announce Type: new Abstract: Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classif

FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection

arXiv:2605.31349v1 Announce Type: cross Abstract: Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding

HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs

arXiv:2511.18760v2 Announce Type: replace Abstract: Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments.

LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification

arXiv:2605.30829v1 Announce Type: new Abstract: Lower extremity computed tomography (CT) contains clinically relevant information for body composition analysis, sarcopenia assessment, and musculoskele

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

arXiv:2605.30931v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to susta

Nemotron 3 Ultra: Frontier smart. 5X faster. 30% cheaper. 💚💚💚

model-releasesjeremy-howard--x

Nemotron 3 Ultra is NVIDIA's latest language model featuring significant improvements in speed (5X faster) and cost efficiency (30% cheaper) compared to previous versions, positioning it as a frontier

PhyDrawGen: Physically Grounded Diagram Generation from Natural Language

arXiv:2605.30512v1 Announce Type: new Abstract: Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, th

Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach

arXiv:2511.04393v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as 'agents' for decision-making (DM) in interactive and dynamic environments. Yet, since they

researcharxiv-cs-ai

PRISM: Progressive Reasoning through Iterative Slot Memory for Vision

arXiv:2605.30942v1 Announce Type: new Abstract: Modern vision models process images in a single feed-forward pass, which limits their ability to recover missing evidence or refine uncertain representa

Re-examining Low Rank adaptation for private LLM fine-tuning

arXiv:2510.01137v3 Announce Type: replace Abstract: Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent

Safe Equilibrium Policy Optimization for Strategic Agent Policies

arXiv:2605.30854v1 Announce Type: cross Abstract: Language models fine-tuned with reinforcement learning typically optimize for task reward, ignoring multi-agent strategic structure. Because these age

SERA: Soft-Verified Efficient Repository Agents

arXiv:2601.20789v3 Announce Type: replace Abstract: Open-weight coding agents should hold a fundamental advantage over closed-source systems because they can specialize to private codebases, encoding

Smaller and Faster 3DGS via Post-Training Dictionary Learning

arXiv:2605.30396v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is a promising neural scene representation for real-time rendering, but trained models often suffer from large memory foo

Speculative Decoding Across Languages

arXiv:2605.30580v1 Announce Type: new Abstract: Speculative decoding has become a crucial component of large language model (LLM) inference, enabling faster generation by drafting multiple tokens and

Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning

arXiv:2505.17607v3 Announce Type: replace Abstract: Large Language Models (LLMs) display reasoning capabilities over linguistic and symbolic objects but have limited capabilities to directly interpret

Triaging Threats to Specialized Guardrails

arXiv:2605.30693v1 Announce Type: cross Abstract: Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains

Safety

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems

arXiv:2506.00175v5 Announce Type: replace-cross Abstract: Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, whe

safetyarxiv-cs-ai

Apertus LLM Family Expansion via Distillation and Quantization

arXiv:2605.29128v1 Announce Type: new Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating

CLUBench: A Clustering Benchmark

arXiv:2605.29933v1 Announce Type: new Abstract: Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progres

ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression

arXiv:2605.29350v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intens

DiffSpot: Can VLMs Spot Fine-Grained Visual Differences in Web Interfaces?

arXiv:2605.29615v1 Announce Type: cross Abstract: Vision-language models (VLMs) have made strong progress on high-level image-text alignment, yet their ability to perceive subtle visual differences re

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

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

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

Horizon Activation Mapping for Neural Networks in Time Series Forecasting

arXiv:2601.02094v4 Announce Type: replace Abstract: Neural networks for time series forecasting have relied on error metrics and architecture-specific interpretability approaches for model selection t

researcharxiv-cs-lg

Knowledge Offloading: Decomposing LLMs into Sparse Backbones and Memory Modules

arXiv:2605.29075v1 Announce Type: new Abstract: LLMs encode both general capabilities and domain-specific knowledge in a single set of parameters. We ask whether this capacity can be reorganized: keep

LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs

arXiv:2605.29756v1 Announce Type: new Abstract: As large language models continue to scale, low-bit weight-only post-training quantization (PTQ) offers a practical solution to their memory-efficient d

researcharxiv-cs-ai

Safety

LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback

arXiv:2605.30273v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empathy, and s

safetyarxiv-cs-ai

LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol

arXiv:2603.14644v3 Announce Type: replace-cross Abstract: Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hinderin

Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels

arXiv:2605.29800v1 Announce Type: new Abstract: LLM-as-a-judge panels aggregate votes from multiple models, with the expectation that diverse models yield more reliable evaluations. We develop a frame

On-Policy Replay for Continual Supervised Fine-Tuning

arXiv:2605.29495v1 Announce Type: new Abstract: Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers

OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation

arXiv:2605.29829v1 Announce Type: new Abstract: Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient par

Personalized Turn-Level User Conversation Satisfaction Benchmark

arXiv:2605.29711v1 Announce Type: cross Abstract: User satisfaction with AI assistants is highly personalized: the same response may satisfy one user but disappoint another depending on what each user

PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing

arXiv:2605.29815v1 Announce Type: new Abstract: The growing number of submitted papers has motivated the exploration of Large Language Models (LLMs) as a means to support and augment the peer review p