Search: “models”

Answer-Level Trust Selection for Physical Vision-Language Reasoning

arXiv:2608.19807v1 Announce Type: new Abstract: Vision-language models (VLMs) can estimate physical quantities such as duration, speed, and acceleration from visual observations, but existing benchmar

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Model Releases

InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries

DGX agent

arXiv:2608.20220v1 Announce Type: new Abstract: Legal AI systems are increasingly used to answer legal questions, yet existing benchmarks assume queries arrive fully specified. In practice, users omit

model-releasesarxiv-cs-ai

21 Aug 2026

Tutorials

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

arXiv:2608.20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However, optimizing

tutorialsarxiv-cs-ai

The impact of feature engineering and an optimisation framework for ocean colour machine learning

arXiv:2608.19899v1 Announce Type: cross Abstract: Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparam

Where Grounding Accuracy Lives on the IoU Curve: Label-Free Inference-Time Boundary Refinement

arXiv:2608.19553v1 Announce Type: new Abstract: Vision--language models can identify the correct referent while returning an imprecise bounding box. We study whether a frozen direct-answer model can u

Abliteration Mitigation via Refusal Aliases

arXiv:2608.18093v1 Announce Type: cross Abstract: Abliteration, the removal of refusal capabilities from large language models by projecting weight matrices orthogonal to an extracted refusal directio

Cacheable by Design? Training Mixture-of-Experts Routers for Locality Against the Edge Memory-Bandwidth Wall: A Pre-Registered Negative Result with a Systems Measurement Study

arXiv:2608.18261v1 Announce Type: new Abstract: Serving a 235B-parameter Mixture-of-Experts (MoE) model on a single 8 GB GPU is bottlenecked not by compute but by memory bandwidth: decode must stream

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

arXiv:2608.18578v1 Announce Type: new Abstract: Proactive interference (PI) is a documented failure mode in large language models in which retrieval of a repeatedly overwritten value degrades as prior

FlashAttention for Scalable Vector Architectures

arXiv:2608.18656v1 Announce Type: new Abstract: Inference with transformer models on CPUs is increasingly important, especially for Small Language Models (SLMs), where vector architectures are emergin

LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum

arXiv:2601.01684v2 Announce Type: replace-cross Abstract: While dense retrieval models have been the standard for state-of-the-art information retrieval, their deployment is often constrained by high

Measuring the Partial-Credit Gap: A Strict Benchmark on Vietnam's 2025 Convex Marking Scheme

arXiv:2608.18336v1 Announce Type: new Abstract: When evaluating language models on human exams, benchmarks typically score each response as right or wrong and report the overall accuracy. This approac

OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios

arXiv:2608.18586v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used as OCR systems in document and knowledge-processing pipelines, but their ability to fai

Reconstruction of continuum robots by marker-free shape registration of image data using a kinematic model

arXiv:2405.15336v2 Announce Type: replace Abstract: Continuum robots are slender, flexible manipulators that navigate confined, curved workspaces and are gaining traction in aerospace, inspection, aut

researcharxiv-cs-ro

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

arXiv:2608.18115v1 Announce Type: cross Abstract: Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is confidently wro

Hardware

Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering

arXiv:2608.19125v1 Announce Type: new Abstract: When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operatio

hardwarearxiv-cs-ai

A Residual Learning Approach for Unsteady Aerodynamic Load Prediction

arXiv:2608.17894v1 Announce Type: cross Abstract: This paper investigates the feasibility of using residual learning to improve unsteady aerodynamic load prediction for aeroelastic applications. The m

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

arXiv:2606.15315v2 Announce Type: replace Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user pr

Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

arXiv:2512.22287v4 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research

Do LLMs Know a Good Hypothesis When They See One? Logit-Based Energy Scoring Outperforms Prompted LLM-as-Judge for Scientific Hypothesis Ranking

arXiv:2608.17270v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a challenge fo

Neural Operator-Based Nonlinear Nudging for Chaotic Dynamical Systems

arXiv:2508.05778v2 Announce Type: replace Abstract: Nudging is an empirical data assimilation technique that incorporates an observation-driven control term into the model dynamics. The trajectory of

TileMix: Tile-Centric Mixed-Precision Attention for LLM Inference Acceleration

arXiv:2608.17336v1 Announce Type: new Abstract: Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention computes quadratic q

What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrations

arXiv:2608.17719v1 Announce Type: cross Abstract: Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate older models.

Safety

Audio-Visual Segmentation via Depth-Guided Collaborative Modeling

arXiv:2608.16285v1 Announce Type: cross Abstract: Audio-Visual Segmentation (AVS) is a fundamental task in multimodal perception that performs pixel-level segmentation of sounding objects in videos by

safetyarxiv-cs-ai

Applications

Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving

arXiv:2608.16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ra

applicationsarxiv-cs-lg

Hardware

BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces

arXiv:2608.15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer'

hardwarearxiv-cs-lg

Evaluating Agentic Code Repair Capabilities in Distributed Systems

arXiv:2608.14863v1 Announce Type: cross Abstract: LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified.

Agents

ForceU-VLA: A Force-Aware Vision-Language-Action Model for Embodied Ultrasound Scanning

arXiv:2608.15009v1 Announce Type: cross Abstract: Embodied intelligent ultrasound scanning enables the automation and standardization of the ultrasound examination process by integrating perception, d

agentsarxiv-cs-cv

Applications

HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents

arXiv:2608.16447v1 Announce Type: new Abstract: Long-horizon embodied tasks require LLM agents to iteratively decompose high-level goals, revise plans in response to environmental feedback, and ground

applicationsarxiv-cs-ai

Historical Backtesting for Scientific Question Discovery: A Protocol and Astronomy Pilot

arXiv:2608.16795v1 Announce Type: cross Abstract: Systems that generate scientific research questions are evaluated today by expert scores, LLM-as-judge ratings, or curated case studies -- all subject

Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data

arXiv:2410.13341v4 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid

MathGen: Revealing the Illusion of Mathematical Competence through Text-to-Image Generation

arXiv:2603.27959v3 Announce Type: replace Abstract: Modern generative models have demonstrated the ability to solve challenging mathematical problems. In many real-world settings, however, mathematica

Local Ai

Rotate Disks to Reach Farther: Design and Modeling of a Novel Reconfigurable Tendon Driven Manipulator

arXiv:2608.15946v1 Announce Type: new Abstract: Rerouting the tendon path in tendon driven continuum manipulators (TDCMs) enables a broad range of deformation modes. This work presents a Reconfigurabl

local-aiarxiv-cs-ro

Agents

The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines

arXiv:2608.14588v1 Announce Type: new Abstract: Sequential multi-agent LLM pipelines chain specialized agents without verification at handoffs, creating a structural flaw with measurable and severe co

agentsarxiv-cs-ai

Applications

Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects

arXiv:2502.04899v3 Announce Type: replace-cross Abstract: The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the gen

applicationsarxiv-cs-ai

Whose Gold? Annotator-Pool Disagreement Is Large at the Item Level, and Hidden by Small Leaderboards

arXiv:2608.15980v1 Announce Type: new Abstract: Preference benchmarks are built by hiring annotators, and the identity of those annotators is treated as an implementation detail. We measure what that

Safety

Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty

arXiv:2608.13651v1 Announce Type: cross Abstract: We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system x_{t+1} = ax_t + u_t + w_t, x_

safetyarxiv-cs-lg

Cross-Disciplinary Taxonomy and Modeling of Misunderstanding Generation, Amplification, and Detection, from Pragmatics to AI Agents

arXiv:2608.13604v1 Announce Type: new Abstract: Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasing

Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning

arXiv:2608.14290v1 Announce Type: new Abstract: We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) t

Multiphase-Diff: Diffusion-Based Generative Modeling for High-Contrast Multiphase Physical Systems with Sharp Interfaces

arXiv:2608.13669v1 Announce Type: new Abstract: Physics-constrained diffusion for high-contrast, sharp-interface multiphase fields faces three coupled difficulties. At coefficient jumps, expanded poin

researcharxiv-cs-cv

PRM-as-a-Judge 1.5: A Toolkit for Robot Process Assessment

arXiv:2608.14284v1 Announce Type: cross Abstract: Fine-grained robotic evaluation matters for understanding embodied models, going beyond binary success rates and rule-based process scores. We present

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

arXiv:2608.13947v1 Announce Type: new Abstract: High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the stru

researcharxiv-cs-cl

Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs

arXiv:2608.12781v1 Announce Type: new Abstract: Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-think

Do LLMs Beat Nash? Testing Decentralized Coordination in Self-Play Multi-Agent Games

model-releasesarxiv-cs-ro

arXiv:2608.12547v1 Announce Type: cross Abstract: Large language model agents deployed without a central controller are often assumed to require communication to coordinate their actions. We ask what

Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ

arXiv:2608.13441v1 Announce Type: new Abstract: Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientif

Agents

Foam-Agent: A Large Language Model-Based Multi-Agent Framework for Automating Computational Fluid Dynamics Workflows

arXiv:2505.04997v3 Announce Type: replace Abstract: Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage wo

agentsarxiv-cs-ai

From Atomic Evidence to Logical Composition: Structured Compositional Reasoning over Compound Answer Options

arXiv:2608.12836v1 Announce Type: cross Abstract: Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the ind

How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures

arXiv:2608.13267v1 Announce Type: cross Abstract: Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in

LoRA-Diffusion: Parameter-Efficient Fine-Tuning via Low-Rank Trajectory Decomposition

arXiv:2608.12328v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA have transformed the adaptation of large autoregressive language models, enabling task-specific cus