Search: “model-releases”

Rigorous Evaluation of Large Language Models for Malaria Drug Discovery: Trade-offs in Performance, Scale, and Resource Utility

arXiv:2608.20418v1 Announce Type: cross Abstract: We introduce Malaria-Instruct, a curated instruction-following dataset derived from the ChEMBL Legacy Malaria corpus for Malaria virtual screening, an

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Model Releases

RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

DGX agent

arXiv:2608.20656v1 Announce Type: cross Abstract: Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw m

model-releasesarxiv-cs-ai

24 Aug 2026

Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning

model-releasesarxiv-cs-ro

arXiv:2608.21032v1 Announce Type: new Abstract: Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X

RODE: A Radial-Orthogonal Decoupled Engine for Optimization

arXiv:2608.21024v1 Announce Type: new Abstract: Modern neural network training increasingly uses matrix-aware optimizers, yet their conditioned matrix step is typically added directly to the weight, j

Self-Speculation for Faster Reasoning Models

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance

SlidesGen-Bench: Evaluating Slides Generation via Computational and Quantitative Metrics

arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts

Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models

arXiv:2504.05978v4 Announce Type: replace Abstract: Reinforcement learning (RL) is a powerful framework for decision-making in uncertain environments, but it often requires large amounts of data to le

Socialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts

arXiv:2608.21044v1 Announce Type: new Abstract: Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sess

SPARK-SAM: Self-Prompt Adaptation with Response Knowledge for SAM in Infrared Small Target Segmentation

arXiv:2608.20754v1 Announce Type: new Abstract: Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a misma

Sparse Token Routing in Efficient Transformers

arXiv:2608.20632v1 Announce Type: new Abstract: Efficient-transformer research often motivates token pruning and adaptive computation with the claim that not all tokens require equal computational eff

Specification Portability Across LLM Development Agents: Cross-Agent Compatibility in Specification-Driven Software Migration

arXiv:2608.21208v1 Announce Type: cross Abstract: This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The s

SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control

arXiv:2608.21175v1 Announce Type: cross Abstract: Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneou

StateSight: Benchmarking Latent Spatial-State Reconstruction in Vision-Language Models

arXiv:2608.20414v1 Announce Type: new Abstract: Vision-language models are increasingly used for multimodal question answering, yet their ability to reconstruct latent spatial structure from a single

STCO: Conditional Neural Operators for Time-Dependent PDEs

arXiv:2608.20477v1 Announce Type: new Abstract: Neural operators have emerged as efficient surrogates for time-dependent physical systems governed by partial differential equations (PDEs), but their f

Stored in Optimizer State, Valued by Later Training: A Causal Account of Subliminal Trait Transfer

arXiv:2608.20442v1 Announce Type: new Abstract: Subliminal trait transfer allows a student model to acquire behavioral dispositions from teacher-generated data in which the trait is not semantically e

Structure is information: structural identifiability mappings for machine learning with partially observed dynamical systems

arXiv:2502.04131v2 Announce Type: replace Abstract: The successful application of modern machine learning for time series classification is often hampered by limitations in quality and quantity of ava

TaPeR: Probabilistic Recovery of Sparse Task Precedence Graphs from a Handful of Demonstrations

model-releasesarxiv-cs-ro

arXiv:2608.21035v1 Announce Type: new Abstract: Long-horizon manipulation tasks are often only partially ordered. For example, when assembling an electronic device, the battery and circuit board may b

Terminal Agents: A Survey of AI Agents in Command-Line Environments

arXiv:2608.20485v1 Announce Type: new Abstract: Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, to

TH-GNN: Heterogeneous Temporal Graph Neural Networks for LLM-Agent Shilling Attack Detection

arXiv:2608.20376v1 Announce Type: new Abstract: LLM agents can now generate realistic shilling profiles, fluent reviews, and coherent ratings at scale, systematically defeating recommender-system defe

The Exceedance Design Effect: Effective Sample Size for Thresholds under Clustering

arXiv:2608.21262v1 Announce Type: cross Abstract: Many machine-learning systems set a threshold at a quantile of a calibration set: conformal predictors that promise 90% coverage by drawing their cuto

The Intrinsic Dimension of Prompts in Internal Representations of Large Language Models

arXiv:2501.10573v2 Announce Type: replace Abstract: We study the geometry of token representations at the prompt level in large language models through the lens of intrinsic dimension. Viewing transfo

Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning

arXiv:2608.21180v1 Announce Type: cross Abstract: LGE cardiac MRI is widely used for left atrial fibrosis assessment and ablation planning in atrial fibrillation patients as knowledge of fibrotic tiss

Towards Traffic Modelling of Multi-Agent Systems: The Role of Coordination Topology

arXiv:2608.20494v1 Announce Type: cross Abstract: Multi-agent LLM systems are an emerging networked workload whose rapid deployment raises questions about the traffic patterns they generate. Compared

TRACE: Training-time Report-guided and Clinically Ordered Concept Editing

arXiv:2608.20809v1 Announce Type: cross Abstract: Breast ultrasound diagnosis relies on clinically meaningful semantic concepts, yet most deep learning methods adopt end-to-end image-to-label paradigm

Training, learning and inference: unified dynamics of neural systems

arXiv:2608.20965v1 Announce Type: new Abstract: We define an atomic generation fact f=(u,tau,omega,z;rho), recording the origin, realized transformation, concrete occurrence, generated result and rela

Tree-of-Concerns: Hierarchical Multi-Agent Debate for Unstated-Limitation Extraction in Scientific Critique

arXiv:2608.20777v1 Announce Type: new Abstract: As scientific literature grows and papers increasingly under-report limitations, multi-agent LLMs offer a promising approach to systematically uncover t

Triangulation-Free Bundle Adjustment with Graduated Non-Convexity for Camera Pose Refinement from Coarse Priors

arXiv:2608.21008v1 Announce Type: new Abstract: Mobile AR frameworks attach a metric pose prior to every casual phone capture, and turning it into reconstruction-grade poses cheaply on CPU is the step

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

arXiv:2608.21095v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they ret

Truth Lies Deep: Countering Semantic Camouflage via Latent Intent Verification

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

arXiv:2504.09772v3 Announce Type: replace Abstract: Test-Time Scaling has emerged as a powerful method to extend the reasoning capabilities of Large Language Models. However, single-agent TTS faces si

Uncertainty propagation in auto-regressive random neural network models

arXiv:2608.20483v1 Announce Type: cross Abstract: We develop analytical and particle-based methods for uncertainty propagation in random neural network models, where both the inputs and network parame

Unified Branch-and-Bound Search for the Steiner Traveling Salesman Problem on Graphs of Convex Sets

arXiv:2608.21319v1 Announce Type: new Abstract: We formalize the Steiner Traveling Salesman Problem (Steiner-TSP) on Graphs of Convex Sets (GCS), which seeks a minimum-cost closed trajectory through r

UpgradeBench: A Decision-Centric Benchmark for Upgrading Fine-Tuned LLM Specialists

arXiv:2608.20918v1 Announce Type: new Abstract: Organizations maintain task-specific adapters for open-weight language models, and each new base-model release forces a migration decision: retain exist

VA-DPO: Valence-Arousal Direct Preference Optimization for Controllable Emotion Generation in Language Models

arXiv:2608.20374v1 Announce Type: cross Abstract: How precisely can we tell a language model how to feel? Most work on emotional generation answers with a discrete label - happy, angry, sad - which ca

VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences

arXiv:2608.21357v1 Announce Type: new Abstract: In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plo

VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Clouds

arXiv:2608.20740v1 Announce Type: new Abstract: State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the small

VortexChat: An agentic framework for autonomous multi-objective integrated photonic design

arXiv:2608.20688v1 Announce Type: new Abstract: The advancement of modern integrated photonics is frequently bottlenecked by device design workflows that rely heavily on manual simulation and expert i

VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation

arXiv:2608.21290v1 Announce Type: cross Abstract: We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode v

WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving

arXiv:2608.20974v1 Announce Type: cross Abstract: Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent featur

Weighted Memory Tree: Remembering What Matters for Long-Horizon LLM Agents

arXiv:2608.20631v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated the ability to solve multi-step tasks requiring planning, tool use, and external information access,

What Color Is the Text? A Benchmark for Hallucination Induced by Image-Embedded Prompt

arXiv:2511.13400v3 Announce Type: replace Abstract: We introduce Embedded Stroop, a controlled diagnostic paradigm for measuring image-embedded prompt interference in Multimodal Large Language Models

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot set

When Better Teachers Don't Make Better Students: Revisiting Knowledge Distillation for CLIP Models in VQA

arXiv:2511.17886v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have achieved remarkable success across multimodal tasks, yet their substantial computational demands hinder eff

When Do LLMs Replace Fine-Tuned NLU? A Decision Framework for Intent Detection in Production Conversational Systems

arXiv:2608.20371v1 Announce Type: cross Abstract: A common claim is that zero-shot large language models (LLMs) can replace fine-tuned NLU classifiers for intent detection. We test this claim head-to-

When does fusing hand-crafted knowledge with learned representations pay? A cost-normalized benchmark of stacking, substitution, and interference

arXiv:2608.21098v1 Announce Type: new Abstract: Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or harm

When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation

arXiv:2608.20627v1 Announce Type: cross Abstract: Agentic retrieval-augmented generation (RAG) interleaves retrieval, reasoning, and answer generation across multiple hops. A retrieval error at hop 1

When Generated Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception

arXiv:2608.20810v1 Announce Type: cross Abstract: Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, s

When Retrieval Fails Before It Begins: Structurally Indirect Prerequisite Eviction as a Retention Failure in Agentic Memory

arXiv:2608.20400v1 Announce Type: new Abstract: Agentic memory under a fixed budget involves two stages: retention and retrieval. Existing retrieval-centered paradigms implicitly assume necessary evid