Search: “models”

Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression

arXiv:2606.01608v1 Announce Type: new Abstract: Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidel

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GeistBERT: Breathing Life into German NLP

DGX agent

arXiv:2506.11903v5 Announce Type: replace Abstract: Advances in transformer-based language models have highlighted the benefits of language-specific pre-training on high-quality corpora. In this conte

researcharxiv-cs-cl

2 Jun 2026

Applications

GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping

arXiv:2606.00998v1 Announce Type: new Abstract: We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to no

applicationsarxiv-cs-ro

HalleluBERT: Let Every Token That Has Meaning Bear Its Weight

arXiv:2510.21372v2 Announce Type: replace Abstract: Transformer-based models have advanced NLP, yet Hebrew still lacks a RoBERTa encoder that is trained at scale and released in both base and large va

Implicit Geographic Inference in LLM Medical Triage: Language-Driven Disparities in Emergency Recommendations

arXiv:2606.01204v1 Announce Type: cross Abstract: We investigate whether large language models produce different medical triage recommendations for identical symptoms based solely on the language of t

IndoBias: A Dual Track Culturally Grounded Benchmark for LLMs Bias Evaluation in Indonesian Languages

arXiv:2606.01260v1 Announce Type: cross Abstract: Despite being home to more than 1300 ethnic groups and 700 indigenous languages, bias in Large Language Models has not been fully studied in Indonesia

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606.02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets (arepsilon_i) according to insti

Latent Reasoning in TRMs is Secretly a Policy Improvement Operator

arXiv:2511.16886v5 Announce Type: replace-cross Abstract: Recently, small models with latent recursion have obtained promising results on complex reasoning tasks. These results are typically explained

Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks

arXiv:2606.01432v1 Announce Type: new Abstract: Accurate modeling of leaf spectral reflectance from physiological and biochemical traits is essential for advancing remote sensing applications in plant

researcharxiv-cs-lg

Linguistics-Aware Non-Distortionary LLM Watermarking

arXiv:2606.00613v1 Announce Type: cross Abstract: Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment m

researcharxiv-cs-ai

Low-Resource Safety Failures Are Action Failures, Not Representation Failures

arXiv:2606.01196v1 Announce Type: cross Abstract: Safety alignment learned in high-resource languages transfers poorly to low-resource languages. Models refuse harmful prompts in English but fail to r

Make Your VLA More Robust Without More Data By Interleaving Motion Planning

model-releasesarxiv-cs-ro

arXiv:2606.00985v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. Th

MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution

arXiv:2606.00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events t

Navigating the Reality Gap: On-Device Continual Adaptation of ASR for Clinical Telephony

arXiv:2512.16401v5 Announce Type: replace Abstract: Automatic Speech Recognition (ASR) can significantly reduce documentation burden in clinical workflows, but standard models degrade sharply in real-

On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching

arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as cha

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606.01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint tr

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506.22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces. Usually, the output layer is linear, which can create a 'ran

OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery

arXiv:2603.27645v2 Announce Type: replace Abstract: Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined

Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization

arXiv:2606.02178v1 Announce Type: cross Abstract: Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery lo

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts

arXiv:2606.01509v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale by activating only a small subset of experts per token. However, training such models remains challenging becaus

researcharxiv-cs-ai

RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting

arXiv:2606.00147v1 Announce Type: cross Abstract: Domain-specific supervised fine-tuning (SFT) often improves in-domain performance at the cost of degrading a model's general capabilities. We view thi

Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning

arXiv:2606.02044v1 Announce Type: new Abstract: Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In superv

Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data

arXiv:2606.00404v1 Announce Type: new Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function. For terrain elevation data, this supports analytic d

Rethinking the Role of Positional Encoding: Sliding-Window Transformers without PE Remain Turing Complete

arXiv:2606.01532v1 Announce Type: new Abstract: Positional encoding (PE) is widely viewed as necessary for transformers to process ordered sequences: without them, the next-token map appears permutati

Revisiting Ripple Effects in Knowledge Editing through Pressure-Aware Joint Neighborhood Optimization

arXiv:2606.01610v1 Announce Type: new Abstract: Single-edit updates in large language models can trigger ripple effects across local knowledge neighborhoods: desirable propagation to related facts and

RoboBenchMart: Benchmarking Robots in Retail Environment

arXiv:2511.10276v2 Announce Type: replace-cross Abstract: Most existing robotic manipulation benchmarks focus on tabletop or household scenarios. While these setups have driven impressive progress, it

S-SPPO: Semantic-Calibrated Self-Play Preference Optimization

arXiv:2606.01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley

Sandboxed Coding Agents are Competitive Omni-modal Task Solvers

arXiv:2606.00579v1 Announce Type: new Abstract: As multimodal LLMs increasingly target video and audio, it is often assumed that such tasks require native omnimodal models. We show that this is not al

Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

arXiv:2503.06520v3 Announce Type: replace Abstract: Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-d

Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems

arXiv:2606.00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential a

StreamingVLM: Real-Time Understanding for Infinite Video Streams

arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i

Symbolic Neural Generation with Applications to Lead Discovery in Drug Design

arXiv:2510.23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to cons

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition

arXiv:2606.00545v1 Announce Type: new Abstract: Post-trained language models can recognize their own outputs from a sentence or two out of context. In a companion paper itep{jack2026twomodes} we showe

TLG: Temporal-Logic Grounding for Video Question Answering via Source-Annotation Reconstruction and Category-Targeted Reasoning

arXiv:2606.01591v1 Announce Type: new Abstract: The TimeLogic Challenge evaluates formal temporal-logic reasoning over video - 16 operators (before, after, until, since, always, co-occur, ordering, ..

Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners

arXiv:2606.01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. Thi

Applications

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale

arXiv:2606.00422v1 Announce Type: cross Abstract: Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers enc

applicationsarxiv-cs-lg

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention

arXiv:2606.01243v1 Announce Type: new Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over ex

v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound

arXiv:2509.25773v3 Announce Type: replace-cross Abstract: AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions. To gauge

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight

arXiv:2606.00424v1 Announce Type: new Abstract: As large language models become stronger, weak supervisors may fail to provide reliable labels, preferences, or final judgments for complex outputs, lim

What Cosine Similarity of Label Representations Can and Cannot Tell us

arXiv:2603.29488v2 Announce Type: replace Abstract: Cosine similarity is often used to measure the similarity of vector representations of neural network models. However, the cosine similarity of repr

researcharxiv-cs-lg

What to Format and How: A Benchmark and Workflow Approach for Document Formatting

arXiv:2606.01936v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have opened up new possibilities for automated document formatting. However, real-world formatting often

When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs

arXiv:2602.03554v2 Announce Type: replace-cross Abstract: Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evalu

Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination

arXiv:2606.01276v1 Announce Type: new Abstract: Large language model (LLM)-based machine translation has advanced cross-cultural communication, yet it still struggles with culture-loaded words (CLWs)

XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT

arXiv:2606.00134v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data

Auditing LLM Benchmarks with Item Response Theory

arXiv:2605.30504v1 Announce Type: new Abstract: LLM benchmark labels are frozen at release and silently propagated into downstream benchmarks, errors and all. We introduce an Item Response Theory-base

Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks

arXiv:2605.30892v1 Announce Type: new Abstract: We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless ch

Beyond Static Dialogues: Benchmarking Realistic, Heterogeneous, and Evolving Long-Term Memory

arXiv:2605.31086v1 Announce Type: new Abstract: In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the under

BilliardPhys-Bench: Benchmarking Physical Reasoning and Visual Dynamics of Multimodal LLMs

arXiv:2605.30900v1 Announce Type: new Abstract: Current multimodal models handle static image recognition well, but intuitive physical reasoning remains a weakness. Predicting how objects will move an