Search: “models”

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

arXiv:2609.07798v1 Announce Type: cross Abstract: Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, policy

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Do New Attention Mechanisms Actually Fix Attention Sinks at Million-Token Context?

DGX agent

arXiv:2609.08574v1 Announce Type: cross Abstract: Long context language models now advertise windows of one million tokens, but two habits limit how much of that window is used. Attention heads with n

model-releasesarxiv-cs-ai

10 Sept 2026

IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier

arXiv:2609.10494v1 Announce Type: new Abstract: Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet a

applicationsarxiv-cs-cl

In Medical Claims Data, Enhancing Predictive Performance for Major Adverse Cardiovascular Events Using Cross Attention

arXiv:2609.09824v1 Announce Type: new Abstract: Medical claims data comprise the financial details, including the expenses and billing information, as well as the clinical information, such as the dia

Proof-Carrying Cognition: Closing the Verification Gap with Reality-Settled Reward

arXiv:2609.09776v1 Announce Type: cross Abstract: Frontier gains in language-model reasoning come from reinforcement learning on reasoning traces and are concentrated in domains with a cheap, sound ve

Solving versus Verifying: Catching Contradictions in Tax Reasoning Systems

arXiv:2609.05928v1 Announce Type: cross Abstract: Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for th

applicationsarxiv-cs-ai

Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts

arXiv:2609.08236v1 Announce Type: new Abstract: Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the numbers

The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Audits

arXiv:2609.09048v1 Announce Type: cross Abstract: Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same

The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?

arXiv:2609.08589v1 Announce Type: cross Abstract: Recent large language models can emit task-progress signals that agent frameworks use to decide whether a task should continue or stop, yet whether a

Topology Obstructs Pure Foundation Neural Quantum States

arXiv:2609.07591v1 Announce Type: cross Abstract: Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase di

TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs

arXiv:2609.08226v1 Announce Type: new Abstract: Temporal graph learning models the evolution of dynamic systems, where both structural interactions and semantic states change over time. However, exist

UnitBoost: Managing Compound LLM Systems with a Merge Operator, Not a Model

arXiv:2609.09815v1 Announce Type: cross Abstract: Compound LLM systems often solve a coordination problem by adding a higher-level LLM. The resulting meta-agent reads workers' outputs, writes the fina

agentsarxiv-cs-cl

xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems

arXiv:2609.07784v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally

Safety

A Closed-Form Estimator and Diagnostic Battery for Anchor-Judge Error Correlation, Under a Single-Common-Factor Model

arXiv:2609.08826v1 Announce Type: cross Abstract: When an external reference set (an anchor) is used to decompose an LLM-judge panel's error into a quality signal and a shared common-mode error, stand

safetyarxiv-cs-lg

Local Ai

CGSM: Concept-Guided Segmentation Model for Precise Pulmonary Lesion Delineation

arXiv:2609.07004v1 Announce Type: new Abstract: Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches ofte

local-aiarxiv-cs-cv

Continuous Token-Level Spatio-Temporal Context Modeling for Visual Object Tracking

arXiv:2609.07070v1 Announce Type: new Abstract: Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete

researcharxiv-cs-cv

CUSP: Decomposable Collective Uncertainty for Multi-Agent Multimodal Reasoning

arXiv:2609.05708v1 Announce Type: cross Abstract: Aggregating heterogeneous vision-language models (VLMs) can improve multimodal reasoning, but neither an individual model's confidence nor that of the

agentsarxiv-cs-lg

DDPM Score Matching and Distribution Learning

arXiv:2504.05161v2 Announce Type: replace-cross Abstract: Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key r

Geometry-Aware Test-Time Learning for Quantitative Spatial Reasoning

arXiv:2609.06004v1 Announce Type: new Abstract: Quantitative spatial reasoning in visual-language models (VLMs) aims to infer spatial distances and directional relationships among objects in 3D space

researcharxiv-cs-cv

Hierarchical Prompt Injector for Domain Generalization Segmentation

arXiv:2609.05864v1 Announce Type: new Abstract: Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domai

Image-Scale Robustness and Visual Recognition Performance: A Cross-Architecture Analysis

arXiv:2609.06051v1 Announce Type: new Abstract: The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogene

Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation

arXiv:2508.06990v2 Announce Type: replace Abstract: Semantic navigation requires an agent to navigate toward a specified target in an unseen environment. Employing an imaginative navigation strategy t

agentsarxiv-cs-ro

JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery

arXiv:2609.07915v1 Announce Type: new Abstract: Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field

Safety

MetaRSI / RSI2: A Meta-Recursive Self-Improving System for Recursive Self-Improving Systems Themselves

arXiv:2609.06396v1 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet R

safetyarxiv-cs-lg

PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell Us

arXiv:2609.06080v1 Announce Type: new Abstract: Deeply phenotyped cohorts combine clinical, imaging, molecular, and wearable observations across timescales from seconds to years. This breadth can reve

Local Ai

PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout

arXiv:2609.07328v1 Announce Type: new Abstract: Local pedestrian-vehicle forecasting spans heterogeneous physical scales: pedestrians combine root locomotion with articulated motion, whereas vehicles

local-aiarxiv-cs-ro

Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations

arXiv:2609.05658v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correc

Local Ai

SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model

arXiv:2609.08281v1 Announce Type: new Abstract: Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillance, and autonomous driving. However, they a

local-aiarxiv-cs-cv

Social Intuition vs. Machine Reasoning: Anticipating Human-Robot Interaction from multiple modalities

arXiv:2609.07394v1 Announce Type: new Abstract: Anticipating whether a person will interact from one's own perspective is a highly intuitive task for humans, that relies on a combination of cues. We i

Time-Aware Assistive Navigation

arXiv:2609.05596v1 Announce Type: new Abstract: Can interactive vision-and-language agents learn not just what to say but also extbf{extit{when}} to say it? Current language models rarely plan over wh

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are s

VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification

arXiv:2609.06245v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often strugg

When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning

arXiv:2609.07525v1 Announce Type: new Abstract: Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitatio

researcharxiv-cs-cv

A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification

arXiv:2609.04772v1 Announce Type: new Abstract: The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and un

applicationsarxiv-cs-lg

Adaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detection

arXiv:2609.05066v1 Announce Type: cross Abstract: As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly detection (WSVAD) has emerged as a crit

applicationsarxiv-cs-ai

AlcaTRAz - Anchored Tree-Rule Defense Against Jailbreaks

arXiv:2609.03693v1 Announce Type: cross Abstract: Large language models (LLMs) are vulnerable to jailbreak attacks that bypass safety alignment through carefully crafted prompts. Many existing defense

Optimal Rates for Agentic Networked Information Aggregation

arXiv:2609.05318v1 Announce Type: new Abstract: Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a networked learning model. The model captures

The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behavior

arXiv:2609.05190v1 Announce Type: new Abstract: In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent M

agentsarxiv-cs-ai

When Genomic Masking Priors Fail to Transfer: Strong Variant Prediction, Weak Functional Generation

arXiv:2609.04861v1 Announce Type: new Abstract: Bidirectional discrete diffusion model appears naturally suited to genomic modeling because it can reconstruct missing sequence from both flanks. We dev

BASP: Communication-Efficient Batch-Aware Sequence Parallelism for LLM Training

arXiv:2609.03151v1 Announce Type: cross Abstract: Long-context reasoning for large language models (LLMs) is becoming increasingly important, but training over long sequences remains challenging due t

CASCADE: A Component Ablation and Corpus Audit of a Layered Local Defense for MCP-Based Systems

arXiv:2604.17125v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) widens the prompt injection attack surface of large language model applications to tool descriptions, paramet

Continuous Actions from Discrete Minds: Latent-Aligned Planning for End-to-End Autonomous Driving

arXiv:2609.04070v1 Announce Type: new Abstract: Bridging the gap between the discrete reasoning of Vision-Language Models and the continuous, physics-constrained nature of autonomous driving remains a

CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation

arXiv:2609.04083v1 Announce Type: cross Abstract: MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes containing the same concepts but different

Evolving Excellence: Automated Optimization of LLM-based Agents

arXiv:2512.09108v2 Announce Type: replace-cross Abstract: Agentic AI systems built on large language models (LLMs) offer significant potential for automating complex workflows, from software developme

Interface-Induced Trajectory Censoring

arXiv:2609.03966v1 Announce Type: new Abstract: Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model is emitting well-formed calls: the interfa

It's the Problem, Not the Path: Budget and Difficulty Confounds in LLM Reasoning Trajectories

arXiv:2609.03436v1 Announce Type: cross Abstract: Reasoning traces of large language models are widely read as containing 'breakthrough' moments and early-legible fates. Both readings rest on measurem

Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning

arXiv:2609.04194v1 Announce Type: new Abstract: Reasoning traces from chain-of-thought models appear to offer a legible window into how a model arrives at its answer. A growing body of work treats the

researcharxiv-cs-cl

Tutorials

LLMs Learn Better In-Context from Rules than from Examples

arXiv:2609.03213v1 Announce Type: new Abstract: Large language models (LLMs) exhibit in-context learning capabilities, where they can learn new tasks from prompt contexts without weight updates. We co

tutorialsarxiv-cs-cl