Search: “models”

RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement

arXiv:2607.25886v1 Announce Type: cross Abstract: Recursive self-improvement requires turning evidence of model failures into better models. Data-centric post-training research entails diagnosing capa

29 Jul 2026

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Simulation-based parameter estimation via a combination of embedded normalizing flows and implied empirical probabilities under moment restrictions

DGX agent

arXiv:2607.25026v1 Announce Type: cross Abstract: In this work, we present a simulation-based parameter estimation framework for a model defined by a computational simulation of a physical system. We

model-releasesarxiv-cs-lg

29 Jul 2026

The Disruptive Impact of Large Language Models on Capture the Flag Competitions and the Path Toward Fair Play

arXiv:2607.25425v1 Announce Type: new Abstract: Capture the Flag (CTF) competitions are among cybersecurity's most effective training grounds, developing practical skill across cryptography, web explo

29 Jul 2026

Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design

arXiv:2607.25798v1 Announce Type: new Abstract: An often overlooked factor of robot manipulation performance is the embodiment of the robot itself. Motivated by this problem, we study motion-condition

researcharxiv-cs-ro

29 Jul 2026

A Coulomb Particle Model for Learning Kernel Attention in Transformers

arXiv:2607.23869v1 Announce Type: cross Abstract: Randomized features provide a scalable approximation to kernel machines, but their performance depends strongly on the choice of feature distribution.

A Few Words Go a Long Way: Language Guided Robot Policy Synthesis

arXiv:2607.23784v1 Announce Type: cross Abstract: While vision-language-action models have demonstrated impressive zero-shot manipulation capabilities, they remain fundamentally black box policies tha

Agents

Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction

arXiv:2602.00575v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) provides a promising pathway for continuously advancing GUI agents, yet existing reward modeli

agentsarxiv-cs-ro

CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation

arXiv:2607.23647v1 Announce Type: cross Abstract: Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring prefe

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs

arXiv:2607.22577v1 Announce Type: new Abstract: Scaling large language models (LLMs) has driven their success, yet dense Transformers couple capacity and computation: every parameter is activated for

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations

model-releasesarxiv-cs-cv

arXiv:2607.23835v1 Announce Type: new Abstract: Attribution methods are widely used to characterize the evidence underlying model predictions, yet their potential to improve model behavior remains und

Local Ai

DRC-Aid: Design-Rule Correction via Agentic Framework utilizing Inference-Time Large Language Models

arXiv:2607.22761v1 Announce Type: cross Abstract: Resolving Design Rule Violations (DRVs) in layouts entails an iterative loop of geometric edits and verification. We present DRC-Aid, a closed-loop ag

local-aiarxiv-cs-lg

Hallucination Rates in Language Generation

arXiv:2607.23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algor

Local Ai

Intuitionistic j-Do-Calculus in Topos Causal Models

arXiv:2510.17944v2 Announce Type: replace-cross Abstract: In this paper, we generalize Pearl's do-calculus to an Intuitionistic setting called j-stable causal inference inside a topos of sheaves. Our

local-aiarxiv-cs-ai

Kimi K3: Open Frontier Intelligence

arXiv:2607.24653v1 Announce Type: new Abstract: We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token

LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings

arXiv:2607.24435v1 Announce Type: cross Abstract: Large language models may easily assign personality labels from text, but model interpretability remains an open problem. To address this gap, we intr

MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection

arXiv:2607.15166v2 Announce Type: replace Abstract: Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? W

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design

model-releasesarxiv-cs-cv

arXiv:2607.24665v1 Announce Type: new Abstract: Modern large language models scale successfully by pairing capacity growth with efficiency, keeping per-token and deployment costs under control as capa

Model Predictive Planner for UAV Navigation in Non-Convex Air Corridors

arXiv:2607.24369v1 Announce Type: new Abstract: This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking mo

researcharxiv-cs-ro

Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction

arXiv:2607.22729v1 Announce Type: cross Abstract: Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches r

safetyarxiv-cs-cl

Pose-Aware Modeling to Mitigate Pose-Related Artifacts in Tactile Gloves

arXiv:2607.22964v1 Announce Type: new Abstract: Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and

researcharxiv-cs-ro

Similarity All The Way Up: Multilingual Generalization in LLMs Relies on Language-Level Similarity Structures

arXiv:2607.22699v1 Announce Type: new Abstract: As Large Language Models (LLMs) grow more capable across diverse tasks, their (in)ability to generalize remains difficult to quantify and poorly underst

Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks

arXiv:2607.22758v1 Announce Type: cross Abstract: The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication t

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

arXiv:2607.23492v1 Announce Type: new Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while prese

researcharxiv-cs-cv

Tokengeist: Multi-Turn Attribution Tracing in Agentic Conversations

arXiv:2607.22610v1 Announce Type: new Abstract: When a language model produces a response in a multi-turn conversation, which tokens from prior turns shaped that answer, and how did those dependencies

Towards simultaneous decoding of kinetic and kinematic movement parameters during grasp and lift task by noninvasive brain imaging

arXiv:2607.24081v1 Announce Type: cross Abstract: Brain-machine interfaces (BMIs) can assist individuals with limited mobility, such as stroke survivors or amputees. One of the key challenges in devel

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

model-releasesarxiv-cs-cv

arXiv:2607.22727v1 Announce Type: new Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can

Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness

model-releasesarxiv-cs-lg

arXiv:2607.21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and crimi

Local Ai

DCS: A Unified Conditional Sensitivity Framework for Cross-Modal Copyright Infringement Detection

arXiv:2607.22035v1 Announce Type: new Abstract: Currently, most foundation models can reproduce or strongly depend on copyrighted training content, but output similarity alone is insufficient for infr

local-aiarxiv-cs-lg

LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks

arXiv:2607.21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and

researcharxiv-cs-lg

Layer-wise LoRA fine-tuning: a similarity metric approach

model-releasesarxiv-cs-lg

arXiv:2602.05988v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) on web-scale datasets becomes fundamental for advancing general-purpose AI. In contrast, enhancing their p

Agents

MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph

arXiv:2508.12393v3 Announce Type: replace Abstract: The rapid expansion of medical literature challenges the scalable structuring of domain knowledge. Knowledge Graphs (KGs) offer a solution, yet curr

agentsarxiv-cs-cl

Applications

A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

arXiv:2607.21403v1 Announce Type: new Abstract: Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior

applicationsarxiv-cs-lg

Capital Markets LLM Reliability Score (CM-LRS): From Plausible to Bankable

arXiv:2607.21340v1 Announce Type: new Abstract: In capital-markets workflows the question is rarely whether a large language model can produce a fluent draft, but whether the draft is bankable: defens

Evaluating Risks in Weak-to-Strong Alignment: A Bias-Variance Perspective

arXiv:2604.25077v2 Announce Type: replace Abstract: Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on examples

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

arXiv:2607.20583v1 Announce Type: cross Abstract: The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess

GuardianAgentBench: Where Agents Fail and How to Guard Them

arXiv:2607.20982v1 Announce Type: new Abstract: As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and reliable behavi

How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming

arXiv:2607.21208v1 Announce Type: new Abstract: Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conc

Tutorials

Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling

arXiv:2607.20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data scarcity. Bi

tutorialsarxiv-cs-ai

Masked Topology Modeling for Self-Supervised Learning on Parametric CAD

arXiv:2607.20642v1 Announce Type: new Abstract: Computer aided design (CAD) is ubiquitous: virtually any modern object was designed using editable CAD tools. However, with the shortage of available CA

researcharxiv-cs-cv

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development

arXiv:2607.20532v1 Announce Type: cross Abstract: Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system mainten

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs

arXiv:2607.21063v1 Announce Type: new Abstract: Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is as

Scaling Interpretable Transformers with Parity Bottleneck Layers

arXiv:2607.20652v1 Announce Type: cross Abstract: Language models are thought to exhibit the phenomenon of superposition, representing many more features than dimensions in their residual streams. Spa

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

arXiv:2602.19313v2 Announce Type: replace-cross Abstract: General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled, fa

Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation

arXiv:2607.21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing authenticat

AlphaRoute: Large Language Models as Semantic Optimizers for Multi-Objective Routing

arXiv:2607.19768v1 Announce Type: new Abstract: Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-cons

safetyarxiv-cs-lg

Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation

arXiv:2607.15434v3 Announce Type: replace-cross Abstract: Multi-agent systems routinely place one AI agent in authority over another. When a subordinate refuses a task, the manager chooses the outcome

Context-structured Video Anomaly Detection with Large Vision-Language Models

arXiv:2607.19077v1 Announce Type: new Abstract: Training video anomaly detectors is challenging due to the difficulty and cost of annotating diverse and rare abnormal events. Although recent large vis

researcharxiv-cs-cv

Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

arXiv:2607.19894v1 Announce Type: cross Abstract: Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into