Search: “models”

Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark

arXiv:2606.30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This

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Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction

DGX agent

arXiv:2606.29445v1 Announce Type: cross Abstract: Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarka

model-releasesarxiv-cs-ai

30 Jun 2026

Counterfactual Residual Data Augmentation for Regression

arXiv:2606.28460v1 Announce Type: cross Abstract: Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspir

EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures

arXiv:2606.30219v1 Announce Type: new Abstract: LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while th

Hardware

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks

arXiv:2606.29082v1 Announce Type: new Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrate

hardwarearxiv-cs-cl

Factorizable Normalizing Flows for parameter-dependent density morphing

arXiv:2606.30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeli

Internal-State Probes Read the Situation, Not the Action: Three Negative Results for Pre-Action Misalignment Monitoring

arXiv:2606.30449v1 Announce Type: new Abstract: Probes on model internals could help monitor agentic systems if they identify harmful text or tool actions before those actions are generated. We ask wh

Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion

arXiv:2603.05693v2 Announce Type: replace-cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep gen

Local Ai

Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents

arXiv:2606.16364v2 Announce Type: replace Abstract: LLM agents mis-call tools, and the natural guess is that the model failed to see the right tool in a crowded harness. We show the opposite through a

local-aiarxiv-cs-ai

Research

Majority Vote Silences Minority Values: Annotator Disagreement at the Hate/Offensive Boundary in HateXplain

arXiv:2606.28772v1 Announce Type: cross Abstract: Hate speech annotation pipelines routinely collapse annotator disagreement into majority vote labels before training. We show that this aggregation is

researcharxiv-cs-ai

Memory-Managed Long-Context Attention: A Preliminary Study of Editable Request-Local Memory

arXiv:2606.28876v1 Announce Type: new Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.

MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

arXiv:2602.21608v2 Announce Type: replace Abstract: Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain s

Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop

arXiv:2606.29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science. A decade of work has pr

Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement

arXiv:2406.05830v2 Announce Type: replace-cross Abstract: This paper presents a fully probabilistic approach for solving optimal experimental design problems under budget constraints. The experimental

Reliability-Prioritized Fine-Grained Generation in Multimodal Large

arXiv:2606.29573v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to generate fine-grained descriptions of visual content. However, we observe and theo

RIPA: Sensory-Vector Prompt Injection Attacks on LLM-Controlled ROS 2 Robots

arXiv:2606.28649v1 Announce Type: cross Abstract: We present RIPA, the first systematic multi-channel empirical study of prompt injection attacks delivered through the sensory pipeline of a ROS 2-base

SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation

arXiv:2606.30201v1 Announce Type: cross Abstract: Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical

Singular Learning and Occam's Razor in Deep Monomial Networks

arXiv:2606.28464v1 Announce Type: new Abstract: In the optimization of neural networks, gradient dynamics are influenced by critical points that arise from the model's architecture. These critical poi

Local Ai

SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis

arXiv:2606.29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recent

local-aiarxiv-cs-ai

SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows

arXiv:2606.29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks

StrucTab: A Structured Optimization Framework for Table Parsing

arXiv:2606.29905v1 Announce Type: new Abstract: Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial l

Safety

Test-Time Detoxification without Training or Learning Anything

arXiv:2602.02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale. Detoxification is

safetyarxiv-cs-ai

Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding

arXiv:2601.04693v2 Announce Type: replace Abstract: Although negation is known to challenge large language models (LLMs), benchmarks for evaluating negation understanding-especially in Korean-are scar

Research

Towards Engineering Scaling Laws with Pretraining Data Composition

arXiv:2606.19781v2 Announce Type: replace-cross Abstract: Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established fo

researcharxiv-cs-ai

Translating Natural Language to Strategic Temporal Specifications via LLMs

arXiv:2606.30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS). However, writing corr

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs

arXiv:2606.28438v1 Announce Type: cross Abstract: Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We st

When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

arXiv:2606.28661v1 Announce Type: cross Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sam

Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression

arXiv:2606.29712v1 Announce Type: new Abstract: Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning, but require long output sequences and

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

arXiv:2602.20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground the

Applications

Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving

arXiv:2606.27457v1 Announce Type: cross Abstract: Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single m

applicationsarxiv-cs-cl

From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection

arXiv:2606.27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models rema

Safety

HunyuanImage 3.0 Technical Report

arXiv:2509.23951v3 Announce Type: replace Abstract: We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with

safetyarxiv-cs-cv

LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation

model-releasesarxiv-cs-ro

arXiv:2606.27871v1 Announce Type: new Abstract: Vision Language Models (VLMs) have emerged in the robotic domain as a powerful tool that enables environmental perception with language context, serving

Multimodal Evaluator Preference Collapse: Cross-Modal Coupling in Self-Evolving Agents

arXiv:2606.16682v3 Announce Type: replace-cross Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge. We show that Evaluator Preferen

NormAct: A Benchmark for Hidden Social Norm Compliance in Embodied Planning

arXiv:2606.27826v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly deployed as embodied planners in egocentric environments, where task success requires not only

Applications

Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems

arXiv:2606.27797v1 Announce Type: cross Abstract: Knowledge Distillation (KD) enables training smaller student models under the guidance of larger teacher models, and the widely adopted TRL library im

applicationsarxiv-cs-ai

Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks

arXiv:2606.27474v1 Announce Type: cross Abstract: How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding? We study this question through Speculative Refineme

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

arXiv:2606.26104v1 Announce Type: cross Abstract: Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about ani

Autoregressive Boltzmann Generators

arXiv:2606.27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the dev

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT

arXiv:2606.26861v1 Announce Type: new Abstract: Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning

Context Recycling for Long-Horizon LLM Inference

arXiv:2606.26105v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong capabilities in short-context reasoning but degrade in performance over long conversational horizons due t

CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs

arXiv:2606.27264v1 Announce Type: new Abstract: Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text jud

DiCoBench: Benchmarking Multi-Image Fine-Grained Perception via Differential and Commonality Visual Cues

arXiv:2606.26602v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive fine-grained perception capabilities. However, existing ben

Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform

arXiv:2606.26590v1 Announce Type: new Abstract: Security misconfigurations in Terraform Infrastructure-as-Code are a growing risk in cloud deployments, and large language models are increasingly used

From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion

arXiv:2604.01849v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion, they typically adhere to a Hard Completion (HC) par

Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training

arXiv:2606.26102v1 Announce Type: cross Abstract: Standard post-training pipelines apply supervised fine-tuning (SFT) and reinforcement learning (RL) to make language models helpful, but these process

How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?

arXiv:2606.26346v1 Announce Type: new Abstract: Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-doma

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

arXiv:2606.26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run-