Search: “models”

SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks

arXiv:2608.12127v1 Announce Type: new Abstract: Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is

13 Aug 2026

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The Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance

DGX agent

arXiv:2608.11694v1 Announce Type: cross Abstract: A benchmark score comes from a single phrasing of each problem. That single phrasing is treated as if it stood for the whole space of ways the same pr

model-releasesarxiv-cs-ai

13 Aug 2026

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608.11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informa

13 Aug 2026

Agents

Continuous Interaction Diffusion: A Diffusion-Native Runtime for Asynchronous Tool-Augmented Reasoning

arXiv:2608.10438v1 Announce Type: new Abstract: Large language models increasingly rely on external tools to access up-to-date information, perform computation, and interact with the outside world. Fo

agentsarxiv-cs-ai

DriveVLA-M0: Failure-Aware Memory Augmentation for Autonomous Driving

arXiv:2608.10413v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for end-to-end autonomous driving by enabling unified reasoning across

FADE: From Passive Verification to Active Discovery in Counterfactual Video Understanding

arXiv:2608.10764v1 Announce Type: new Abstract: Counterfactual video understanding evaluates whether models grasp physical and commonsense regularities. However, existing multiple-choice question (MCQ

Logit-Boundary Geometric Belief Interfaces and Sparse Sheaf-Enclave Protocols: A Self-Contained Substrate for Secure Network Electronic Health Record (EHR) Interoperability

arXiv:2608.10300v1 Announce Type: new Abstract: Electronic health-record interoperability is a boundary problem: legacy systems, generative models, terminology services, identity systems, and human re

Rescene: band-limited stochastic forcing turns a frozen neural weather operator into a climate emulator

arXiv:2608.09971v1 Announce Type: cross Abstract: Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-

Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement

arXiv:2608.10725v1 Announce Type: new Abstract: Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models

TAF-MED: Multi-Turn Safety Refusal Collapse in LLMs Under Declared Self-Treatment Intent

arXiv:2608.10258v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly provide conversational health information that may influence treatment decisions, yet existing benchmarks do

The Evaluation Protocol Determines the Result: An Independent Reproduction of LeWorldModel on TwoRoom

arXiv:2608.10145v1 Announce Type: new Abstract: LeWorldModel trains a latent world model with a prediction loss and a single anti-collapse regulariser, and reports approximately 87% of goals reached o

CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

arXiv:2608.09164v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms.

FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients

arXiv:2608.09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per

Staying True to the Origin: Continuous Image Stylization with Smooth Transitions

arXiv:2608.08125v1 Announce Type: new Abstract: Recent advances in generative models have achieved remarkable performance in text- and image-conditioned editing. However, preserving the content of a g

Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers

arXiv:2608.08809v1 Announce Type: new Abstract: A single model scale challenges the flexibility of a production retrieval system: some settings need it faster, others need a smaller index, and the rig

The Knowing-Saying Gap: When Probes See Errors that Confidence Misses

arXiv:2608.07528v1 Announce Type: new Abstract: Linear probes detect corrupted context in language models with near-perfect accuracy, yet this does not translate into reliable failure prediction. The

When Counterbalancing Hides the Bias: Access-Conditioned Position Lock in Forced-Choice LLM Evaluation

arXiv:2607.10202v2 Announce Type: replace Abstract: Forced-choice probes with counterbalanced orientations are a standard tool for measuring language-model 'value dispositions,' and a concentration/ex

When Skills Meet Safety: Benchmarking and Characterizing the Adaptive Jailbreak Robustness of Skill-Merged LLMs

arXiv:2608.08542v1 Announce Type: new Abstract: Model merging has become the default way to give an aligned language model new skills without retraining: a practitioner folds task vectors from math, c

Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark

arXiv:2608.07062v1 Announce Type: new Abstract: Test-time adaptation (TTA) has become a practical way to adapt deployed models to unlabeled target data, a setting that is especially relevant in comput

10 Aug 2026

Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?

arXiv:2608.06972v1 Announce Type: new Abstract: Embeddings have emerged as a standard representational interface linking foundation models with downstream systems. Most embedding benchmarks assess rep

10 Aug 2026

The Bitter Lesson of Tool Calling

arXiv:2608.06370v1 Announce Type: new Abstract: Tool use transforms LLMs into agents that act beyond their training data, and for code-capable models, programmatic tool calling extends this further by

7 Aug 2026

Item Response Theory for AI Safety

arXiv:2608.05086v1 Announce Type: new Abstract: Language models differ in how safely they behave and these differences are measured by safety benchmarks. But aggregated benchmark scores are hard to tr

6 Aug 2026

Intertemporal Preference Steering in Qwen3 via Contrastive Activation Addition

arXiv:2608.03892v1 Announce Type: new Abstract: We study linear representations of temporal horizon in the large language model Qwen3-32B and use them to change the model's time-related preferences, r

5 Aug 2026

SocietyBench: Forecasting Counterfactual Social-World Evolution

arXiv:2608.04009v1 Announce Type: new Abstract: Large language models (LLMs), and the agents built on top of them, are now benchmarked heavily on whether they can finish a task -- fix a bug, drive a b

5 Aug 2026

Accuracy Does Not Guarantee Human-Likeness: Cross-Domain Human-Centered Benchmark in Monocular Depth Estimation

arXiv:2512.08163v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are increasingly used as functional models of human vision, yet standard monocular depth estimation (MDE) benchmarks lar

Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks

arXiv:2608.01238v1 Announce Type: new Abstract: Vision Language Models (VLMs) have demonstrated exceptional performance across various tasks. However, they have not yet been thoroughly evaluated on mo

Image-Space Rule Discovery

arXiv:2608.00490v1 Announce Type: new Abstract: Can image-editing models discover visual rules in image space and complete problem-solving end-to-end? We tackle this question in the spirit of a human

Research

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics

arXiv:2608.01522v1 Announce Type: cross Abstract: Teaching a language model a skill it has not mastered is obstructed by three recurring difficulties: training data is scarce, ground-truth reasoning t

researcharxiv-cs-cl

AMTFV: Agentic Mathematical Tool-Flow Verification for LLM Self-Correction

arXiv:2607.29549v1 Announce Type: new Abstract: Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challen

3 Aug 2026

Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation

arXiv:2607.28658v1 Announce Type: cross Abstract: Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying datasets. However, e

3 Aug 2026

Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

arXiv:2607.28079v1 Announce Type: new Abstract: Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development. However,

31 Jul 2026

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory

arXiv:2607.27919v1 Announce Type: new Abstract: Decoder-only language models entangle long-term memory and reasoning in a single parameter set, making it difficult to scale memory capacity independent

31 Jul 2026

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

arXiv:2607.27378v1 Announce Type: new Abstract: Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-g

31 Jul 2026

APEX-Accounting

arXiv:2607.27189v1 Announce Type: new Abstract: We introduce APEX-Accounting, a benchmark built by Mercor in partnership with Ramp, to assess whether frontier models can do the real work of accountant

GPT-Red: Automated Red Teaming via Self-Play at Scale

arXiv:2607.26115v1 Announce Type: cross Abstract: We introduce extbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs. The goal

Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities

arXiv:2607.26062v1 Announce Type: cross Abstract: Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intell

ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation

arXiv:2509.22768v3 Announce Type: replace Abstract: We introduce ML2B, the first benchmark for evaluating cross-lingual task comprehension in end-to-end ML pipeline generation by large language models

Understanding Knowledge Transfer Mechanism in Heterogeneous MLLM Fusion: A Simple Linear Approach

arXiv:2607.26608v1 Announce Type: new Abstract: Training-free fusion of heterogeneous multimodal large language models (MLLMs) provides a direct route for cross-scale capability transfer, yet improvem

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization

arXiv:2607.25451v1 Announce Type: new Abstract: Language models are almost always quantized before they are deployed, and a growing line of work asks whether quantization also lowers their privacy ris

29 Jul 2026

Emergent Latent-State Computation under Stochastic Volatility

arXiv:2607.25459v1 Announce Type: cross Abstract: Mechanistic interpretability has largely focused on language models and deterministic toy tasks. Much less is known about how sequence models internal

29 Jul 2026

HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising

arXiv:2607.24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.g., PID controllers, model predictive control, offline RL

29 Jul 2026

Auditing Alignment Controllability in LLMs via Political Axes

arXiv:2607.23519v1 Announce Type: cross Abstract: Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in dep

CachedSearch: Training-Free Cached Exploration for Test-Time Search in Video Diffusion

arXiv:2607.23159v1 Announce Type: new Abstract: Test-time search lets small video diffusion models rival larger ones, but costs 2-10x more. All candidates are fully denoised, although most are discard

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation

arXiv:2607.23386v1 Announce Type: new Abstract: We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional

From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement

arXiv:2607.23802v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-sc

Local Ai

Generative Video Compression with Adaptive Score Distillation

arXiv:2607.22772v1 Announce Type: cross Abstract: Diffusion models provide strong generative capabilities for video compression at ultra-low bitrates. Existing diffusion-based video codecs adapt base

local-aiarxiv-cs-cv

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning

arXiv:2607.22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations. However, because they primarily model contextual dependencies along amino-acid se

MegaSlide-DiT: Memory-Centric Adaptation and Deformable Local Attention for Efficient Video Diffusion

arXiv:2607.22696v1 Announce Type: cross Abstract: High-resolution video diffusion models built on Diffusion Transformers (DiTs) deliver strong fidelity but quickly exhaust the memory budget of a singl