Search: “models”

Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks

arXiv:2601.16312v2 Announce Type: replace-cross Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this

Unlocking Complex Visual Generation via Closed-Loop Verified Reasoning

DGX agent

arXiv:2605.14876v1 Announce Type: cross Abstract: Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex s

model-releasesarxiv-cs-ai

15 May 2026

ViMU: Benchmarking Video Metaphorical Understanding

arXiv:2605.14607v1 Announce Type: new Abstract: Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two lev

Vision-LLMs for Spatiotemporal Traffic Forecasting

arXiv:2510.11282v2 Announce Type: replace Abstract: Accurate spatiotemporal traffic forecasting is a critical prerequisite for proactive resource management in dense urban mobile networks. While large

safetyarxiv-cs-lg

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

arXiv:2605.14504v1 Announce Type: new Abstract: Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied A

A3 : an Analytical Low-Rank Approximation Framework for Attention

arXiv:2505.12942v4 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable performance; however, their massive parameter counts make deployment highly expensive. Low-

[AINews] Codex Rises, Claude Meters Programmatic Usage

model-releaseslatent-space

This AINews roundup from Latent Space likely covers updates on OpenAI's Codex model gaining prominence in code generation applications, alongside news about Anthropic's Claude implementing usage meter

Anthropic announces ‘programmatic credit pool’ as agentic tool use rises

model-releasessiliconangle

Anthropic PBC, the developer and provider of the Claude artificial intelligence model family, said it’s offering a special credit pool for users who want to use agentic tools with its large language m

Building Interactive Real-Time Agents with Asynchronous I/O and Speculative Tool Calling

arXiv:2605.13360v1 Announce Type: new Abstract: There is a growing demand for agentic AI technologies for a range of downstream applications like customer service and personal assistants. For applicat

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

arXiv:2605.13709v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories ar

Collaborative Parameter Learning: Mitigating Forgetting via Parameter-Level Gradient Analysis

arXiv:2601.21577v2 Announce Type: replace Abstract: Catastrophic forgetting during knowledge injection impairs the ability of large language models to acquire new knowledge without overwriting previou

Dense vs Sparse Pretraining at Tiny Scale: Active-Parameter vs Total-Parameter Matching

arXiv:2605.13769v1 Announce Type: cross Abstract: We study dense and mixture-of-experts (MoE) transformers in a tiny-scale pretraining regime under a shared LLaMA-style decoder training recipe. The sp

Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features

arXiv:2605.12874v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are now standard tools for decomposing language model activations into interpretable features, and automated interpretability

Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack

arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hackin

Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion

arXiv:2605.11679v2 Announce Type: replace Abstract: In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. S

Exploring Multimodal LMMs for Online Episodic Memory Question Answering on the Edge

arXiv:2602.22455v2 Announce Type: replace Abstract: We investigate the feasibility of using Multimodal Large Language Models (MLLMs) for real-time online episodic memory question answering. While clou

FOAM: Blocked State Folding for Memory-Efficient LLM Training

arXiv:2512.07112v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However

HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation

model-releasesarxiv-cs-ro

arXiv:2605.13321v1 Announce Type: new Abstract: VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoo

LIFT: Last-Mile Fine-Tuning for Table Explicitation

arXiv:2605.13424v1 Announce Type: new Abstract: We propose last-mile fine-tuning, or Lift, a pipeline in which a pre-trained large language model extracts an initial table from unstructured clipboard

MedCore: Boundary-Preserving Medical Core Pruning for MedSAM

arXiv:2605.13688v1 Announce Type: new Abstract: Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large fo

MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence

arXiv:2605.12703v1 Announce Type: cross Abstract: We introduce MMCL-Bench, a benchmark for multimodal context learning: learning task-local rules, procedures, and empirical patterns from visual or mix

Pareto-Guided Optimal Transport for Multi-Reward Alignment

arXiv:2605.13155v1 Announce Type: new Abstract: Text-to-image generation models have achieved remarkable progress in preference optimization, yet achieving robust alignment across diverse reward model

safetyarxiv-cs-cv

Phasor Memory Networks: Stable Backpropagation Through Time for Scalable Explicit Memory

arXiv:2605.13370v1 Announce Type: new Abstract: For over a decade, explicit memory architectures like the Neural Turing Machine have remained theoretically appealing yet practically intractable for la

Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles

arXiv:2605.12803v1 Announce Type: new Abstract: Detecting concept drift in high-speed data streams remains challenging, particularly when models must operate on unlabeled data and avoid false alarms c

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

arXiv:2605.12869v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that ci

safetyarxiv-cs-ai

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

arXiv:2510.13999v3 Announce Type: replace-cross Abstract: Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create signi

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency

arXiv:2605.13047v1 Announce Type: cross Abstract: Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Tr

safetyarxiv-cs-ai

SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning

arXiv:2605.12872v1 Announce Type: new Abstract: Despite the recent success of Multimodal Foundation Models (FMs), their reliance on massive paired datasets limits their applicability in low-data and r

SpatialReward: Bridging the Perception Gap in Online RL for Image Editing via Explicit Spatial Reasoning

arXiv:2602.07458v4 Announce Type: replace Abstract: Online Reinforcement Learning (RL) offers a promising avenue for complex image editing but is currently constrained by the scarcity of reliable and

State-Space NTK Collapse Near Bifurcations

arXiv:2605.12763v1 Announce Type: new Abstract: Rich feature learning in tasks that unfold over time often requires the model to pass through bifurcations, constituting qualitative changes in the unde

Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalism

arXiv:2605.12524v1 Announce Type: cross Abstract: We introduce ProofGrid, a benchmark suite for evaluating LLM reasoning through machine-checkable proofs rather than final answers alone. ProofGrid con

SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking

arXiv:2601.19667v2 Announce Type: replace-cross Abstract: We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in

The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm

arXiv:2507.18553v4 Announce Type: replace Abstract: Quantizing the weights of large language models (LLMs) from 16-bit to lower bitwidth is the de facto approach to deploy massive transformers onto mo

The table in HTML format for easier (and non-truncated) viewing: https://sebastianraschka.com/llm-architecture-gallery/active-parameter-rati…

model-releasessebastian-raschka--x

Sebastian Raschka shared an HTML-formatted table comparing active parameter ratios across different large language model architectures for improved readability and to avoid text truncation. The resour

TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma

arXiv:2605.11033v1 Announce Type: cross Abstract: TokaMind is a multi-modal transformer (MMT) foundation model pre-trained on tokamak plasma diagnostics data from MAST, where it was shown to outperfor

Agents

TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints

arXiv:2605.13414v1 Announce Type: new Abstract: Deploying language models as autonomous agents requires more than per-task accuracy: when an agent faces a queue of problems under a finite token budget

agentsarxiv-cs-ai

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning

arXiv:2605.13835v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkab

Research

WARDEN: Endangered Indigenous Language Transcription and Translation with 6 Hours of Training Data

arXiv:2605.13846v1 Announce Type: cross Abstract: This paper introduces WARDEN, an early language model system capable of transcribing and translating Wardaman, an endangered Australian indigenous lan

researcharxiv-cs-ai

Research

Weakly Supervised Segmentation as Semantic-Based Regularization

arXiv:2605.13674v1 Announce Type: cross Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes,

researcharxiv-cs-ai

Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference

model-releasesarxiv-cs-cl

arXiv:2605.11581v1 Announce Type: new Abstract: When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System

arXiv:2605.12025v1 Announce Type: new Abstract: Neural operators provide a framework for learning solution operators of partial differential equations (PDEs), enabling efficient surrogate modeling for

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

model-releasesarxiv-cs-cl

arXiv:2412.05225v3 Announce Type: replace Abstract: Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and proce

Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images

arXiv:2605.12413v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We study

Research

BLOCK-EM: Preventing Emergent Misalignment via Latent Blocking

arXiv:2602.00767v2 Announce Type: replace Abstract: Emergent misalignment can arise when a language model is fine-tuned on a narrowly scoped supervised objective: the model learns the target behavior,

researcharxiv-cs-lg

BSO: Safety Alignment Is Density Ratio Matching

arXiv:2605.12339v1 Announce Type: new Abstract: Aligning language models for both helpfulness and safety typically requires complex pipelines-separate reward and cost models, online reinforcement lear

safetyarxiv-cs-lg

CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography

arXiv:2605.11304v1 Announce Type: new Abstract: Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-

safetyarxiv-cs-cv

Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verification

model-releasesarxiv-cs-cl

arXiv:2603.28488v2 Announce Type: replace Abstract: Large language models (LLMs) remain unreliable for high-stakes claim verification due to hallucinations and shallow reasoning. While retrieval-augme

Covering Human Action Space for Computer Use: Data Synthesis and Benchmark

arXiv:2605.12501v1 Announce Type: new Abstract: Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions i