Search: “models”

Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance

arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and ex

23 Sept 2026

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Model Releases

VLM-in-Sandbox: Visual Workspaces for Agentic Visual Reasoning

DGX agent

arXiv:2609.24362v1 Announce Type: new Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language mo

model-releasesarxiv-cs-ai

23 Sept 2026

Adapting Tree-Structured Speculative Decoding to DeepSeek-V4 for Efficient Inference

arXiv:2609.24698v1 Announce Type: new Abstract: Repeated execution of the target model during autoregressive decoding is a major source of LLM inference latency. Unlike linear speculation, which follo

AirGC-CD: Gaussian-Circulant Precoding for Exactly Debiasable PAPR Reduction in Over-the-Air Federated Learning

arXiv:2609.23084v1 Announce Type: new Abstract: Over-the-air federated learning lets edge devices transmit their local updates simultaneously, reducing the communication overhead. The resulting wavefo

Authority-Preserving Evaluation of Medical Vision-Language Assistants

arXiv:2609.22302v1 Announce Type: new Abstract: Medical vision-language models can propose how urgently a skin lesion should be reviewed, but the local service retains authority to accept or replace t

Beyond Accuracy and Surface Fluency: Risk-Sensitive Evaluation of LLMs for Legal Clause Generation

arXiv:2609.22127v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to draft contractual language, yet conventional accuracy or preference-based evaluations are poorly m

Beyond Point Prediction: Artificial Representative Trees with Uncertainty

arXiv:2609.24528v1 Announce Type: cross Abstract: Random forests (RFs) predict well but are opaque, whereas single decision trees are interpretable but unstable. Artificial representative trees (ARTs)

Can Coding Agents Reproduce Official Statistics? Metadata, Retry Budget and the Limits of Execution Feedback in a Controlled Eurostat Benchmark

arXiv:2609.22222v1 Announce Type: cross Abstract: Large language models can generate executable data-analysis code, but successful execution is not equivalent to a valid official-statistics result. Th

ChartJudgeBench: Evaluating LMM Judges for Chart-to-Code Generation

arXiv:2609.24210v1 Announce Type: new Abstract: Building strong chart-to-code systems increasingly relies on reinforcement learning, whose effectiveness depends critically on the quality of the reward

Compound-QA: A Benchmark for Evaluating LLMs on Compound Questions

arXiv:2411.10163v3 Announce Type: replace Abstract: Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks

Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver

arXiv:2609.24021v1 Announce Type: cross Abstract: Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying co

COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning

arXiv:2609.22697v1 Announce Type: new Abstract: Recently, text-to-speech systems have made significant progress in speech expressiveness and controllability. However, the speaking style of generated s

ETH-TraceBench: A Large-Scale Event-Stream Benchmark for Ethereum DeFi under Temporal, Protocol, and Contract Shift

arXiv:2609.23659v1 Announce Type: new Abstract: Ethereum decentralized finance (DeFi) provides a public, time-stamped record of transaction-level event streams, but the same public symbols can create

Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards

arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conve

From Documented Strengths to Force Limits: Material-Informed Robotic Insertion for Construction Assembly

model-releasesarxiv-cs-ro

arXiv:2609.22609v1 Announce Type: new Abstract: Insertion is a fundamental operation in robotic construction assembly, where variations in material properties and assembly conditions make it difficult

Research

H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache

arXiv:2609.24197v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates large language model inference by having a lightweight draft model predict future tokens for verification by

researcharxiv-cs-lg

Agents

Harness-Zero: Harness Distillation via Agent-as-Harness

arXiv:2609.24974v1 Announce Type: cross Abstract: Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain

agentsarxiv-cs-cl

Improving Parameter Utilization by Sharing Neural Experts Across Layers in Transformers

arXiv:2609.22199v1 Announce Type: new Abstract: Transformer-based large language models often suffer from inter-layer parameter redundancy, where functional transformations are redundantly learned acr

ITSY: Causal Discovery From Irregular Time-Series Data

arXiv:2609.23516v1 Announce Type: new Abstract: Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become mi

Research

LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

arXiv:2609.22904v1 Announce Type: new Abstract: Triage in the emergency department (ED) is a sequential decision process that unfolds turn by turn. Existing evaluations of large language models (LLMs)

researcharxiv-cs-cl

Local Ai

Look Where It Counts: A Free, Label-Free Visual Evidence Signal for Fine-Grained Vision-Language Reasoning

arXiv:2609.24244v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) fail at fine-grained visual questions less because they cannot reason than because they never see the evidence:

local-aiarxiv-cs-cv

Safety

Machine Learning-Based Prediction of Childhood Stunting in Bangladesh: Fairness and Temporal Robustness Assessment

arXiv:2609.24386v1 Announce Type: new Abstract: Childhood stunting remains a major public health concern in Bangladesh and reflects long-term growth failure influenced by child, maternal, household, s

safetyarxiv-cs-lg

MinCU: A Fine-Grained Benchmark for Grounded Minimal-Change Understanding in Image Pairs

arXiv:2609.23336v1 Announce Type: new Abstract: Localizing and describing fine-grained differences between near-identical images is a critical yet underexplored capability for multimodal large languag

MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs

arXiv:2609.23073v1 Announce Type: new Abstract: Recent advances in natural language processing have led to molecular Large Language Models (LLMs) with strong performance across diverse chemistry tasks

Privacy Personalization Trade offs in LLMs: The Impact of Stylometric Signal Reduction on User-Specific Text Generation

arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that

Research

Relightable 3D Avatar Reconstruction with Semantic-Adaptive Motion-Illumination Responses

arXiv:2609.24158v1 Announce Type: new Abstract: Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modeling

researcharxiv-cs-cv

Safety

Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds

arXiv:2603.18532v3 Announce Type: replace-cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for fi

safetyarxiv-cs-lg

Scoped Verification for Reliable Long-Horizon Agentic Context Evolution under Distribution Shift

arXiv:2607.09175v2 Announce Type: replace-cross Abstract: Deployed LLM agents rely on agentic context, the model-external textual control content assembled by an operational harness. In this work, the

ShearFuse-UNet: Hadamard, DCT, and Shearlet Transform Fusion for Next-Day Wildfire Spread Prediction

arXiv:2606.14071v2 Announce Type: replace Abstract: We propose ShearFuse-UNet, a lightweight and computationally efficient deep learning model for next-day wildfire spread prediction from multi-modal

SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm

arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by

Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time

arXiv:2609.22226v1 Announce Type: new Abstract: Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We a

Local Ai

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

arXiv:2407.16139v2 Announce Type: replace Abstract: Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions.

local-aiarxiv-cs-lg

Taramandal-GPT: Enhancing Astrodynamics Problem-Solving with Knowledge Retrieval and Structured Thinking

arXiv:2609.24246v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable progress in natural language understanding, yet their effectiveness in specialized fields like astron

TWIG: A Time-Causal Wavelet Operator for Autoregressive Forecasting on Irregular Graphs

arXiv:2609.22585v1 Announce Type: new Abstract: We introduce TWIG (Time-Causal Wavelet Operator for Irregular Graphs), a graph-native neural operator for autoregressive surrogate modeling on static ir

URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER

arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limit

Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents

model-releasesarxiv-cs-ai

arXiv:2609.21997v1 Announce Type: cross Abstract: LLM agents in social simulation revise their opinions implicitly, in context: how open an agent is to persuasion can neither be specified nor verified

Chinese Competitive Debating Dataset and Benchmark

model-releasesarxiv-cs-ai

arXiv:2609.21637v1 Announce Type: cross Abstract: Debate adjudication requires tracking how arguments develop through interaction, yet existing datasets rarely combine fine-grained debate transcripts

Generative inversion for early ranking of competing geologic interpretations

arXiv:2609.20978v1 Announce Type: new Abstract: High-consequence subsurface decisions are often made under severe data scarcity. Experts may arrive at competing interpretations of the same subsurface

Hybrid GPU-CPU Retrieval for Personalized Search at Ultra-Large Scale

arXiv:2609.21281v1 Announce Type: cross Abstract: Embedding-based retrieval on user-generated content at the trillion-document scale exposes a sharp conflict between two production demands: deep, expr

MAPLE-RF: Efficient Probabilistic RF Source Localization in Partially Explored Environments

model-releasesarxiv-cs-ro

arXiv:2609.21026v1 Announce Type: new Abstract: Localizing a radio-frequency (RF) transmitter from received signals often requires a model of the environment to predict how obstacles block and reflect

Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data

arXiv:2603.29715v2 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns an

Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty

arXiv:2609.21822v1 Announce Type: new Abstract: While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limi

PolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design

model-releasesarxiv-cs-ai

arXiv:2609.21493v1 Announce Type: new Abstract: Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whet

Applications

RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer

arXiv:2609.21791v1 Announce Type: new Abstract: As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced in

applicationsarxiv-cs-lg

Scaling Vision-Language Reward Learning for Robot Manipulation in Parallel Simulation

model-releasesarxiv-cs-ro

arXiv:2609.21767v1 Announce Type: new Abstract: Vision-language models (VLMs) can replace human annotators in preference-based reward learning, but sequential API requests and single-environment data

Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA

arXiv:2602.00279v2 Announce Type: replace Abstract: Reliable uncertainty quantification (UQ) is essential for safe deployment of large language models (LLMs) in scientific question answering, where lo

Local Ai

SpecQuant: Speculative Decoding with Multi-Parent Quantization for Adaptive LLM Inference

arXiv:2609.21704v1 Announce Type: new Abstract: Running large language models (LLMs) locally continues to be limited by restrictions of compute and memory on consumer hardware. The popular acceleratio

local-aiarxiv-cs-lg

Understanding LLM Quantization through Activation-Guided Compensation and Orthogonal Residuals

arXiv:2609.21450v1 Announce Type: new Abstract: Post-training weight-activation quantization reduces the memory and inference costs of large language models, but aggressive W4A4 quantization remains d