Search: “model-releases”

Can LLMs Catch a Rigged Backtest? A Clean-Control Calibration Benchmark

arXiv:2609.28090v1 Announce Type: cross Abstract: Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item p

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Model Releases

Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark

DGX agent

arXiv:2609.28449v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing reposit

model-releasesarxiv-cs-ai

24 Sept 2026

Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs

arXiv:2609.27262v1 Announce Type: new Abstract: Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or

Can Vision-Language Models Analyze Human-Centered Video? Mapping Model Capabilities and Human-AI Collaborative Workflows

model-releasesarxiv-cs-cv

arXiv:2609.27327v1 Announce Type: new Abstract: Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting

Causal Tracing of Audio-Text Fusion in Large Audio Language Models

arXiv:2603.13768v2 Announce Type: replace-cross Abstract: Despite the strong performance of large audio language models (LALMs) in various tasks, exactly how and where they integrate acoustic features

CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments

arXiv:2609.27273v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act on behalf of users online. What happens when the environments they operate in have incentives that do not al

ChipMEM: Verification-Grounded Memory for EDA Agents

arXiv:2609.27067v1 Announce Type: cross Abstract: Large language model (LLM)-based agents use Electronic Design Automation (EDA) tools to generate and revise register-transfer-level (RTL) designs unde

ChronosAttack: Adversarial Tool Scheduling Attacks on LLM Agents

arXiv:2609.27857v1 Announce Type: cross Abstract: Large language model (LLM) agents often process external tool responses as they arrive, making response timing part of the decision process. We introd

ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Cross-Modal Alignment Dataset

arXiv:2505.10083v2 Announce Type: replace Abstract: Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and under

Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

arXiv:2609.26945v1 Announce Type: new Abstract: Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the

COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference

arXiv:2609.26913v1 Announce Type: cross Abstract: No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or

Comparative Evaluation of Static Embedding Models for HTTP Request Anomaly Detection

arXiv:2609.26860v1 Announce Type: cross Abstract: Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewa

Computation Over Geometry: Meaning Identity Is Computed, Not Shipped in the Embeddings

arXiv:2609.28290v1 Announce Type: new Abstract: Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independentl

Conformal Bayes under Continuous Label Shift: Sensitivity Analysis and the Limits of Exact Validity

arXiv:2609.27976v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictive scores with conformal calibration, but under continuous label shift both the score and calibrat

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

model-releasesarxiv-cs-cv

arXiv:2412.02575v3 Announce Type: replace Abstract: Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question A

CORE-STACK+: Meta-Learning for Deep Stacked Generalization

model-releasesarxiv-cs-cv

arXiv:2609.26905v1 Announce Type: cross Abstract: Stacking heterogeneous vision backbones (CNNs, ViTs, and hybrids) is the de facto recipe for accuracy, calibration, and robustness, yet two coupled pa

CoRe-WAM: Correspondence-Aligned Temporal Residuals for World Action Models

arXiv:2609.27314v1 Announce Type: new Abstract: Comparing current and past observations helps robots understand scene changes and select subsequent actions during manipulation. However, comparing visu

Count Evidence, Not Sentences: Tempered Evidence Fusion of LLM Judgments for Long-Text Value Measurement

arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgro

Counterfactual Constraint-Conditioned On-Policy Distillation for Multi-Constraint Instruction Following

arXiv:2609.27421v1 Announce Type: new Abstract: Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tune

CVaR anchor regression protects against rare shifts

arXiv:2609.27034v1 Announce Type: cross Abstract: We study prediction in new environments when training data contain rare, large shifts. Anchor regression penalizes the average of the squared mean res

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

arXiv:2609.27900v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate

DeltaS: Reading the Gated Linear Attention State for KV Cache Eviction in Streaming Video

arXiv:2609.27470v1 Announce Type: cross Abstract: Recent video-language models increasingly adopt hybrid architectures that interleave linear and full attention layers for efficient long-context proce

Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate

arXiv:2609.27930v1 Announce Type: cross Abstract: We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selecti

Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models

arXiv:2609.27234v1 Announce Type: new Abstract: AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the s

Discovery of fully efficient fault indicators along a data-based diagnosis process

arXiv:2609.28087v1 Announce Type: new Abstract: The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytic

Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

arXiv:2609.28231v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PF

Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring

arXiv:2609.27069v1 Announce Type: cross Abstract: Graph neural networks dominate recent work on microservice root-cause analysis, yet recent results question whether the graph contributes. Those resul

DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation

arXiv:2609.27338v1 Announce Type: new Abstract: Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may

EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields

arXiv:2605.06192v2 Announce Type: replace-cross Abstract: Pretrained video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models

EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics

arXiv:2609.27308v1 Announce Type: new Abstract: We study coding agents for long-horizon, dexterous robotics and ask whether their solutions can provide scalable supervision for learning general robot

Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolution

arXiv:2609.26952v1 Announce Type: new Abstract: Dependency conflicts in Python ecosystems arise from incompatible version constraints, missing packages, and undocumented compatibility relationships, c

Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing

arXiv:2609.28026v1 Announce Type: cross Abstract: We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in t

Evaluating Open-Weight LLMs for Turkish Domain Documents Under Retrieval and Hardware Constraints

arXiv:2609.28007v1 Announce Type: new Abstract: Most Turkish-capable large language models (LLMs) are evaluated using general-purpose benchmarks rather than long, structurally complex domain documents

Evaluation Choices Decide the Forecasting Leaderboard: Evidence from a Production Marketplace Panel

arXiv:2609.27867v1 Announce Type: new Abstract: A forecasting benchmark reports which method won. We show that the answer is set by the evaluator's choices before any model is fitted. We benchmark 24

EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection

arXiv:2609.28149v1 Announce Type: cross Abstract: Studies of machine-learning-based power system protection are difficult to compare because task definitions, measurement access, data partitions, metr

Even Sharper Bounds for Transductive Learning and Its Applications

arXiv:2609.28459v1 Announce Type: new Abstract: We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replac

EviStreams: Human-in-the-Loop AI Data Extraction for Systematic Reviews in Medicine

arXiv:2609.27418v1 Announce Type: new Abstract: Systematic reviews underpin clinical guidelines, yet their data-extraction step is a major expert-labor bottleneck bound by a protocolized workflow: two

Exact Quantile Balancing and Load-Error Injection for Mixture-of-Experts

arXiv:2609.28053v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) training requires global load balance to prevent expert under-utilization and local balance for efficient expert-parallel exe

FDE-Bench: Evaluating LLM Agents for Deployment Environment Configuration

arXiv:2609.27571v1 Announce Type: cross Abstract: Deployment requires an agent to turn application code into a running system whose services connect, become ready, and remain observable. FDE-Bench eva

Fed-ReMasker: Federated Tabular Imputation under Feature-Level Missingness

arXiv:2609.28105v1 Announce Type: cross Abstract: Multi-center clinical studies and biomedical research collaborations increasingly seek to utilize data across centers to build models that generalize

FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints

arXiv:2609.27654v1 Announce Type: cross Abstract: Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending,

Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following

arXiv:2609.28395v1 Announce Type: new Abstract: Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally

FLEET: From Logits Entropy to Enhanced Trajectories in Text Generation

arXiv:2609.27657v1 Announce Type: cross Abstract: Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples f

FLINT: Fast Lightweight Inference for Traversability

arXiv:2609.26857v1 Announce Type: cross Abstract: Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversabilit

From LiDAR Maps to Visual Localization: Unified Visual Association for Robust Point-Line-Plane Pose Estimation

arXiv:2609.27363v1 Announce Type: new Abstract: Camera localization in a prior LiDAR map provides a persistent geometric reference for long-term robotic navigation, yet remains challenging because of

Geometry-Conditioned Visual Place Recognition in Natural Environments

arXiv:2609.27370v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial

GLASS: Architecture-Tuned, Composable, Device-Side Linear Algebra for Edge Robotics and Beyond

arXiv:2609.28179v1 Announce Type: new Abstract: GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeate

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

arXiv:2601.05353v3 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk. However, cur