Search: “models”

TabReX : Tabular Referenceless eXplainable Evaluation

arXiv:2512.15907v2 Announce Type: replace Abstract: Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into te

Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations

DGX agent

arXiv:2601.09953v2 Announce Type: replace Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value o

model-releasesarxiv-cs-cl

22 Apr 2026

Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs

model-releasesarxiv-cs-ai

arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv

The new Gemini Enterprise: one platform for agent development, orchestration, and governance

model-releasesgoogle-cloud-ai

The first wave of AI changed how we find information; the next wave is changing how we get work done. Today, we’re enhancing our most powerful AI tools and bringing them together under one roof. Gemin

TrEEStealer: Stealing Decision Trees via Enclave Side Channels

arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c

researcharxiv-cs-lg

Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length

arXiv:2603.22608v2 Announce Type: replace Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, a

researcharxiv-cs-ai

Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks

arXiv:2604.19697v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains chall

VDPP: Video Depth Post-Processing for Speed and Scalability

arXiv:2604.06665v2 Announce Type: replace Abstract: Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-t

Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling

model-releasesarxiv-cs-ai

arXiv:2509.16343v2 Announce Type: replace-cross Abstract: Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inferen

What’s new in the Agentic Data Cloud: Powering the System of Action

model-releasesgoogle-cloud-ai

Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structur

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification

arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r

Agents

Access GPT Image 2.0 natively in Hermes Agent Update now to get access - just run `hermes update` and select your image generation tool mode…

Access GPT Image 2.0 natively in Hermes Agent Update now to get access - just run `hermes update` and select your image generation tool model with `hermes tools` Introducing ChatGPT Images 2.0 A state

agentsnous-research--x

Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations

arXiv:2604.02846v2 Announce Type: replace Abstract: Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, c

AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning

arXiv:2604.17889v1 Announce Type: new Abstract: Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such sc

AeroScene: Progressive Scene Synthesis for Aerial Robotics

model-releasesarxiv-cs-ro

arXiv:2603.23224v2 Announce Type: replace Abstract: Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This

ArgBench: Benchmarking LLMs on Computational Argumentation Tasks

arXiv:2604.17366v1 Announce Type: new Abstract: Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection

Local Ai

Benchmarking programs?

The Reddit post 'Benchmarking programs?' in r/ollama likely discusses tools and methods for measuring the performance of local language models running on Ollama. Available benchmarking tools for Ollam

local-air-ollama

Local Ai

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling

arXiv:2508.16745v2 Announce Type: replace Abstract: Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this questio

local-aiarxiv-cs-lg

Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games

arXiv:2604.16785v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In co

Applications

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA

arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer

applicationsarxiv-cs-cl

Applications

Can we generate portable representations for clinical time series data using LLMs?

arXiv:2603.23987v2 Announce Type: replace Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we st

applicationsarxiv-cs-lg

CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval

arXiv:2601.17230v2 Announce Type: replace Abstract: Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where tr

CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams

arXiv:2604.16665v1 Announce Type: new Abstract: Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional

Coevolving Representations in Joint Image-Feature Diffusion

arXiv:2604.17492v1 Announce Type: new Abstract: Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents

Agents

CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving

arXiv:2509.00789v2 Announce Type: replace Abstract: The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs):

agentsarxiv-cs-cv

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding

arXiv:2511.08480v3 Announce Type: replace Abstract: Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enh

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching

arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio

DMax: Aggressive Parallel Decoding for dLLMs

arXiv:2604.08302v2 Announce Type: replace Abstract: We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggr

safetyarxiv-cs-lg

Do LLM-derived graph priors improve multi-agent coordination?

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

Document-as-Image Representations Fall Short for Scientific Retrieval

arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning

arXiv:2510.00761v5 Announce Type: replace Abstract: Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving

safetyarxiv-cs-lg

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher

Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

arXiv:2604.18336v1 Announce Type: cross Abstract: Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth A

EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations

arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr

safetyarxiv-cs-cv

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning

arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their

FireScope: Wildfire Risk Prediction with a Chain-of-Thought Oracle

arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer

Flexible Aspect Ratios ChatGPT Images 2.0 supports aspect ratios as wide as 3:1 and as tall as 1:3. It can generate outputs that are ready t…

Flexible Aspect Ratios ChatGPT Images 2.0 supports aspect ratios as wide as 3:1 and as tall as 1:3. It can generate outputs that are ready to fit the formats you need, from wide banners and presentati

model-releasesopenai--x

Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens

arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo

researcharxiv-cs-cl

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i

From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization

arXiv:2601.16397v2 Announce Type: replace Abstract: Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where e

researcharxiv-cs-cl

From log pi to pi: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight

arXiv:2603.14389v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics re

Geometric Stability: The Missing Axis of Representations

arXiv:2601.09173v4 Announce Type: replace-cross Abstract: Representational similarity analysis and related methods have become standard tools for comparing the internal geometries of neural networks a

safetyarxiv-cs-cl

GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization

arXiv:2506.07160v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is

safetyarxiv-cs-cl

GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning

arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua

Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)

arXiv:2604.17025v1 Announce Type: cross Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a

HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents

arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou

iDocV2: Leveraging Self-Supervision and Open-Set Detection for Improving Pattern Spotting in Historical Documents

arXiv:2604.16726v1 Announce Type: new Abstract: Considering the imminent massification of digital books, it has become critical to facilitate searching collections through graphical patterns. Current

iPhoneme: Brain-to-Text Communication for ALS Using ConformerXL Decoding

arXiv:2604.16441v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) for speech restoration hold transformative potential for the approximately 173,000--232,500 individuals worldwide wit

researcharxiv-cs-cl