Search: “model-releases”

VST: Verifiable Structured Transport for Auditable Agent-to-Agent Alpha Discovery

arXiv:2609.07065v1 Announce Type: new Abstract: Agent-to-agent (A2A) alpha discovery is slowed by repeated feedback cycles between mining and evaluation agents, whose hand-offs, in contemporary LLM mu

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Model Releases

We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation

DGX agent

arXiv:2609.07568v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains unders

model-releasesarxiv-cs-ai

10 Sept 2026

What Does an LLM-Agent Leaderboard Rank Actually Compare?

arXiv:2609.07785v1 Announce Type: new Abstract: An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that co

What Does MMLU Actually Measure? A Psychometric Audit of Difficulty Structure in Aggregate Benchmark Scores

arXiv:2609.09372v1 Announce Type: cross Abstract: Although MMLU is widely adopted as a benchmark for calibrating general AI capabilities, we psychometrically demonstrate that its aggregate score prima

What Eviction Destroys: A Restore-Counterfactual Audit of Forgetting in Agent Memory

arXiv:2609.08279v1 Announce Type: cross Abstract: Agent memory systems must discard stored information when their history exceeds a fixed token budget. Existing budget-accuracy frontiers quantify the

What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets

arXiv:2609.05663v1 Announce Type: new Abstract: We present a continuous, population-scale measurement record of autonomous language-model trading agents operating in production across two systems with

What Symmetry Buys a Learned Motion Planner

arXiv:2609.10033v1 Announce Type: new Abstract: Learning-based motion planners pay at training what classical planners pay per query. Trained in world coordinates, they relearn the same motion at ever

When Agent Governance Helps

arXiv:2609.05531v1 Announce Type: cross Abstract: No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed a

When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference

arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundation

When Auditors Fabricate: Batch-Size Degradation and Confident Hallucination in LLM Detection of Planted Document Contamination

arXiv:2609.09696v1 Announce Type: new Abstract: Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poorl

When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors

arXiv:2609.09887v1 Announce Type: new Abstract: LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We stud

When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents

arXiv:2609.05441v1 Announce Type: new Abstract: Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dial

When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems

arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

arXiv:2609.10355v1 Announce Type: cross Abstract: Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained larg

XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks

arXiv:2609.05701v1 Announce Type: cross Abstract: One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller

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

XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?

arXiv:2609.06842v1 Announce Type: cross Abstract: When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., 'How do I parse XML with regex?'). In such cases, of

YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models

arXiv:2609.10153v1 Announce Type: new Abstract: Arabic morphology remains challenging for large language models, since fluent generation does not guarantee accurate morphosyntactic control. Existing A

Zero-Shot Temporal Localisation of Audio Deepfakes in Multi-Speaker Conversations

arXiv:2609.10051v1 Announce Type: cross Abstract: Voice-cloning fraud increasingly relies on surgical injection: a genuine conversation in which only one or two sentences are replaced by synthetic spe

3DHarnessBench: Probing Agentic 3D-to-Code Capabilities of Frontier Vision-Language Models

arXiv:2609.06535v1 Announce Type: new Abstract: We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender

3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints

arXiv:2609.08224v1 Announce Type: new Abstract: Intermediate representations are key to bridging the modality gap between generalizable manipulation policies and large-scale pretrained vision-language

A dictionary learning framework for graphs via filters and optimal transport

arXiv:2609.05919v1 Announce Type: new Abstract: We propose a graph dictionary learning (GDL) framework where each graph is represented as a zero-mean Gaussian distribution derived from its filtered La

A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management

arXiv:2609.08078v1 Announce Type: new Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an explor

A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware

arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balan

A Multimodal Label Forecasting Method for Aperiodic Visuo-Motor Time Series

arXiv:2609.07930v1 Announce Type: new Abstract: Deep learning models have been increasingly applied to Time Series Forecasting (TSF) in recent years. Transformer-based and MLP-based models have both b

A Statistical and Machine Learning Framework for Quantifying Offensive Impact in Professional Box Lacrosse

arXiv:2609.06610v1 Announce Type: new Abstract: Professional box-lacrosse statistics summarize outcomes but provide limited information about shot quality or the roles behind scoring opportunities. Th

AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation

arXiv:2609.05762v1 Announce Type: new Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such spl

Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

arXiv:2609.07664v1 Announce Type: new Abstract: Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluati

ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs

arXiv:2609.06072v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise

Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs

arXiv:2606.19993v2 Announce Type: replace Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximati

Adapting Neural Robot Dynamics on the Fly for Predictive Control

arXiv:2604.04039v2 Announce Type: replace Abstract: Accurate dynamics models are critical for the design of predictive controllers for autonomous mobile robots. Physics-based models are often too simp

AgentDrift: A Step-Labeled Benchmark of Injection-Hijacked LLM Agent Trajectories

arXiv:2609.06972v1 Announce Type: cross Abstract: LLM agents complete tasks by issuing sequences of tool calls, and every observation they read is a channel through which an indirect prompt injection

AhaBench: Do Agents Learn from Prior Experience? A Benchmark for Long-Horizon Continual Learning

arXiv:2609.05435v1 Announce Type: new Abstract: Modern language agents are expected to operate over long horizons: they ask follow-up questions, reuse worked examples, handle tool feedback, and adapt

All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs

arXiv:2609.06161v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment

alpha-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling

arXiv:2609.07961v1 Announce Type: cross Abstract: Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. Howe

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

arXiv:2609.07154v1 Announce Type: new Abstract: We describe our entry to the EgoLongQA track of the Wearable-AI Challenge in ECCV 2026, which placed first in the <=2B parameter division with 0.8279 on

Ambient @ EgoProactive 2026 : Proactive Egocentric Assistance with Visually Grounded Supervision

arXiv:2609.07099v1 Announce Type: new Abstract: We present our submission to the EgoProactive track of the ECCV 2026 Wearable AI Challenge, which ranked first in the large-model division and second in

Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning

arXiv:2604.02007v3 Announce Type: replace Abstract: Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopted

ARC-Bench: Closed-Loop Replanning Masks Broken Action Ranking in Frozen JEPA World Models

arXiv:2609.05461v1 Announce Type: cross Abstract: Reward-free latent world models plan by scoring candidate actions with distances in a frozen latent space: an action is preferred if its predicted fut

Are Image Generators Zero-Shot Perceivers? A Rigorous Evaluation

arXiv:2609.07884v1 Announce Type: new Abstract: Recent work, such as Vision Banana, shows that lightweight instruction tuning can enable an image generator to achieve state-of-the-art performance acro

ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement

arXiv:2609.07013v1 Announce Type: new Abstract: Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiogra

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

arXiv:2609.06656v1 Announce Type: new Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasti

Asymptotically-informed neural networks for Black-Scholes implied volatility computation

arXiv:2609.05491v1 Announce Type: cross Abstract: The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration an

Automated Chest CT Protocol Selection via Large Language Model Derived Text Embeddings from Imaging Request Text

arXiv:2609.07986v1 Announce Type: new Abstract: Purpose: Accurate CT protocol selection is critical for diagnostic quality and patient safety, yet the current process is manual, time-consuming, and pr

AV-SafetyBench: A Safety Benchmark for Text-to-Audio-Video Generation

arXiv:2609.06991v1 Announce Type: new Abstract: Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses ne

AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions

arXiv:2609.07917v1 Announce Type: new Abstract: Counterfactual explanations formalize 'what-if' scenarios by identifying modifications to an input instance that obtain a desired alternative prediction

Back to the Feature: Zero-Shot 6DoF Pose Estimation via Dense Local Features

arXiv:2609.06726v1 Announce Type: new Abstract: We present B2TFPose, a training-free zero-shot method for 6DoF pose estimation of unseen objects from RGB images. Using a single frozen DINOv3 vision tr

BanglaMemeX: Advancing Cultural Metaphoric Image Interpretation in Bangla with a Multimodal Explainable Dataset

arXiv:2609.08029v1 Announce Type: cross Abstract: Vision Language Models have achieved strong performance on multimodal benchmarks, yet their ability to reason about culturally grounded and metaphor-r