Search: “models”

Information-Aware KV Cache Compression for Long Reasoning

arXiv:2606.26875v1 Announce Type: cross Abstract: Reasoning capability has advanced rapidly in large language models (LLMs), leading to an increasing size of key-value (KV) cache in both prefilling an

Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation

DGX agent

arXiv:2606.27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data. We show that

model-releasesarxiv-cs-ai

26 Jun 2026

Learning from Equivalence Queries, Revisited

arXiv:2604.04535v2 Announce Type: replace Abstract: Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction,

Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

arXiv:2508.08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and s

Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds

arXiv:2606.26964v1 Announce Type: new Abstract: As embodied AI and world models increasingly operate in dynamic 3D environments, visual perception must move beyond passively interpreting given observa

Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation

arXiv:2606.26711v1 Announce Type: new Abstract: Transforming foundation segmentation models from human-prompted tools into auto-promptable annotators is critical for scalable medical data annotation.

MKG-RAG-Bench: Benchmarking Retrieval in Multimodal Knowledge Graph-Augmented Generation

arXiv:2606.26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchma

OpenAI says GPT-5.6 Sol and Terra were capable of identifying vulnerabilities but were unable to execute autonomous, end-to-end attacks against hardened targets (OpenAI)

OpenAI: OpenAI says GPT-5.6 Sol and Terra were capable of identifying vulnerabilities but were unable to execute autonomous, end-to-end attacks against hardened targets — GPT-5.6 is a new family of th

model-releasestechmeme

OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents

arXiv:2606.26350v1 Announce Type: new Abstract: Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tas

Parametric Open Source Games

arXiv:2606.27068v1 Announce Type: cross Abstract: Open-source game theory studies agents whose behavior may depend on one another's decision procedures, but most existing models use discrete or symbol

PersistentKV: Page-Aware Decode Scheduling for Long-Context LLM Serving on Commodity GPUs

model-releasesarxiv-cs-lg

arXiv:2606.26666v1 Announce Type: new Abstract: Autoregressive large language model (LLM) serving is increasingly limited by key-value (KV) cache movement rather than dense matrix multiplication. Mode

Applications

PhysiFormer: Learning to Simulate Mechanics in World Space

arXiv:2606.27364v1 Announce Type: new Abstract: We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion. Unlike video world models that operate in view-dependent pixe

applicationsarxiv-cs-cv

ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP

arXiv:2606.26794v1 Announce Type: cross Abstract: CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text ali

ReportLogic: Evaluating Logical Quality in Deep Research Reports

arXiv:2602.18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports th

SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages

arXiv:2606.26901v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diver

See & Sniff: Learning Visuo-Olfactory Representations

arXiv:2606.27307v1 Announce Type: new Abstract: While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olf

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

arXiv:2606.27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become fo

Safety

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De

safetyarxiv-cs-cl

Tutorials

Structure Before Collapse: Transient semantic geometry in next-token prediction

arXiv:2606.26749v1 Announce Type: cross Abstract: Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuratio

tutorialsarxiv-cs-cl

Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

arXiv:2603.24991v2 Announce Type: replace Abstract: Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, ei

Which one is Claude is pretty obvious. GLM-5.2 is a beast in some ways, but doesn't have the self-reflective persona of Claude, and isn't re…

model-releasesethan-mollick--x

Ethan Mollick compares Claude and GLM-5.2 AI models, noting that while GLM-5.2 excels in certain capabilities, Claude distinguishes itself through its self-reflective persona and other characteristics

Zero-Shot Size Transfer for Neural ODEs on Sparse Random Graphs: Graphon Limits and Adjoint Convergence

arXiv:2606.26662v1 Announce Type: cross Abstract: Graph Neural Differential Equations (GNDEs) model continuous-time graph dynamics by parameterizing Neural ODE velocity fields with Graph Neural Networ

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606.24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution. Graph Neural Networks (GNNs) effectively model spatiotemporal depen

Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability

arXiv:2606.25819v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents that solve tasks by interacting with external tool environments. Although recent tool-use benc

Blasto-Net: An Explainable Multi-Task Learning for Blastocyst Segmentation, Grading, and Implantation Prediction

arXiv:2606.25463v1 Announce Type: cross Abstract: This study introduces Blasto-Net, a multi-task deep learning model for comprehensive blastocyst analysis. The proposed model performs three tasks simu

Constraint Tax in Open-Weight LLMs: An Empirical Study of Tool Calling Suppression Under Structured Output Constraints

arXiv:2606.25605v1 Announce Type: new Abstract: Tool Calling and Structured Output are two core capabilities of modern Agent systems, yet their interaction under joint deployment conditions remains in

DFMU: Data-Frugal Machine Unlearning

arXiv:2606.25410v1 Announce Type: new Abstract: Machine unlearning is an emerging domain that ensures the safe removal of elements (includes concepts, attributes, entity and class) from the trained mo

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns

arXiv:2606.25010v1 Announce Type: cross Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabil

researcharxiv-cs-cl

Enhancing Pathological VLMs with Cross-scale Reasoning

arXiv:2606.17412v3 Announce Type: replace Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to

Evaluating LLMs on Real-World Software Performance Optimization

arXiv:2606.25530v1 Announce Type: cross Abstract: Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinemen

HG-Bench: A Benchmark for Multi-Page Handwritten Answer-Region Grounding in Automated Homework Assessment

arXiv:2606.25491v1 Announce Type: new Abstract: Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate r

Safety

Homogeneity Bias in Open-Weight LLMs Is Robust to Decoding Hyperparameters

arXiv:2501.02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominan

safetyarxiv-cs-cl

Invoice Haystack: Benchmarking Document Retrieval and Visual Question Answering Under Strong Visual Homogeneity

arXiv:2606.25343v1 Announce Type: new Abstract: Vision Language Models have achieved near-human performance on single-document Visual Question Answering, yet their effectiveness degrades significantly

Hardware

JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting

arXiv:2606.18394v2 Announce Type: replace Abstract: Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it

hardwarearxiv-cs-cl

Applications

LLM Evolution as an Industry-Scale Ecosystem: A Lifecycle Perspective on Continual Learning

arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environ

applicationsarxiv-cs-lg

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios

model-releasesarxiv-cs-lg

arXiv:2606.24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamental

Multilingual Hematology Visual Question Answering Dataset

arXiv:2606.25246v1 Announce Type: cross Abstract: Vision Language Models (VLMs) have shown promising capabilities in medical image analysis by jointly understanding visual and textual information for

OpenAI will delay GPT-5.6 after Trump administration request

model-releasesthe-verge-ai

The Trump administration, apprehensive of potential security issues, has reportedly asked OpenAI to stagger the release of its next big-ticket model, GPT-5.6. The Information reported that OpenAI CEO

@OpenRouter https://openrouter.ai/sakana/fugu-ultra

OpenRouter is a platform that provides API access to multiple large language models and AI services through a unified interface. Sakana AI's Fugu-Ultra appears to be an AI model available through Open

researchdavid-ha--x

Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need

arXiv:2606.25956v1 Announce Type: new Abstract: Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records

Applications

Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy

arXiv:2606.24974v1 Announce Type: new Abstract: Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be

applicationsarxiv-cs-lg

RoboAtlas: Contextual Active SLAM

arXiv:2606.26046v1 Announce Type: cross Abstract: We present RoboAtlas, a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D sem

RoboRouter: Training-Free Policy Routing for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2603.07892v4 Announce Type: replace Abstract: Research on robotic manipulation has developed a diverse set of policy paradigms, including vision-language-action (VLA) models, vision-action (VA)

Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis

arXiv:2606.25369v1 Announce Type: cross Abstract: While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on Englis

Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation

arXiv:2509.22193v2 Announce Type: replace Abstract: Distilling reasoning traces from strong teacher models has become the standard recipe for building capable small language models. Yet reasoning trac

researcharxiv-cs-cl

SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding

arXiv:2606.25552v1 Announce Type: new Abstract: Prompt-based spoken language understanding (SLU) with large language models (LLMs) often suffers from inconsistent intent--slot structures due to decodi

ShutterMuse: Capture-Time Photography Guidance with MLLMs

arXiv:2606.25763v1 Announce Type: new Abstract: Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evalua