Search: “models”

Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds

arXiv:2609.02302v1 Announce Type: new Abstract: A core obstacle to alignment evaluation is evaluation awareness: capable models can tell when they are being tested rather than deployed, weakening the

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

DGX agent

arXiv:2609.02194v1 Announce Type: new Abstract: Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated ba

researcharxiv-cs-lg

3 Sept 2026

LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates

model-releasesarxiv-cs-lg

arXiv:2609.02734v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geo

PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

arXiv:2508.00259v2 Announce Type: replace Abstract: While 3D Gaussian Splatting (3DGS) has established new standards for high-fidelity 3D scene modeling, interpreting massive, unstructured Gaussian pr

Local Ai

Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation

arXiv:2609.01945v1 Announce Type: cross Abstract: Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated. However, the exchanged model updates

local-aiarxiv-cs-lg

Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills

arXiv:2609.02749v1 Announce Type: new Abstract: Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for plannin

Applications

Robust Streaming PCA

arXiv:1902.03223v4 Announce Type: replace-cross Abstract: We consider streaming principal component analysis when the stochastic data generating model is subject to perturbations. While existing model

applicationsarxiv-cs-lg

Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue

arXiv:2609.02390v1 Announce Type: cross Abstract: Sampling error yields exclusively reactive, non-lesional brain parenchyma in a significant proportion of intracranial biopsies, leaving the underlying

Research

Stepwise Think-Critique: Interleaved Reasoning and Self-Critique in a Single LLM

arXiv:2512.15662v4 Announce Type: replace Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions.

researcharxiv-cs-ai

ToolGate: An Executable Acceptance Pipeline for Tool-Dependent Scientific Benchmark Construction

arXiv:2609.02067v1 Announce Type: new Abstract: Scientific benchmarks are commonly built by domain experts who write tasks and cross-check one another's work, or who adapt existing material from textb

VakyArth: Evaluating Pragmatic Competence in LLMs across Indic Languages

arXiv:2609.01788v1 Announce Type: cross Abstract: Real-world communication often requires pragmatic reasoning: interpreting meanings implied through context and cultural convention rather than stated

When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic

arXiv:2609.01741v1 Announce Type: new Abstract: Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extrac

XMerge: Cross-Axis Selection and Reconstructive Layer Merging for LLM Depth Compression

arXiv:2609.02083v1 Announce Type: cross Abstract: Removing complete transformer layers preserves a standard serving architecture, but existing depth-compression methods can lose substantial quality, a

ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.02546v1 Announce Type: new Abstract: Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) models as robot hardware evolves and task-spe

Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients

arXiv:2606.18216v2 Announce Type: replace Abstract: Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitat

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607.18358v2 Announce Type: replace Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise. The blocker is rarely model architecture; it is

safetyarxiv-cs-cl

A hybrid quantum-classical neural network for learning to route

model-releasesarxiv-cs-lg

arXiv:2609.00489v1 Announce Type: new Abstract: This work studies hybrid quantum-classical neural networks for learning routing heuristics. Specifically, this paper asks whether small quantum neural n

A Stable Aggregation Method for Quantum Federated Learning

arXiv:2609.00356v1 Announce Type: new Abstract: Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in

Automated Tree Knowledge Graph Construction using Ontology Expansion and Retrieval from Vietnamese History Textbooks

arXiv:2609.00763v1 Announce Type: new Abstract: Hierarchical Knowledge graph (KG)-based retrieval augmented generation (RAG) has emerged as a powerful approach for supporting large language models wit

Research

BS: Take the Hint - Interactive Multitracer PET/CT Lesion Segmentation with a Scribble-Conditioned ResEnc U-Net

arXiv:2609.01554v1 Announce Type: cross Abstract: Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appearan

researcharxiv-cs-ai

Applications

CADKnitter: Compositional CAD Generation from Text and Geometry Guidance

arXiv:2512.11199v2 Announce Type: replace Abstract: Computer-aided design (CAD) defines 3D models as compact, precise, and editable representations, making it directly useful for several fields. Recen

applicationsarxiv-cs-cv

Can LLMs Discover Scientific Laws in Real and Parallel Worlds?

arXiv:2609.01552v1 Announce Type: new Abstract: Scientific equation discovery has long been central to scientific progress, proceeding through iterative cycles of hypothesis generation, observational

Agents

CoBRA: Learning Tool-Use Boundaries via Counterfactual Margins

arXiv:2609.00967v1 Announce Type: new Abstract: As large language models increasingly act through external tools, deciding when to call a tool has become a central problem alongside deciding how to us

agentsarxiv-cs-ai

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges

arXiv:2607.19011v2 Announce Type: replace-cross Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, s

ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents

arXiv:2609.00749v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents require context assembly: the runtime must decide what to include in each prompt, in what order, and when

Counterfactual Closing-Acceleration Risk: An Anticipatory Surrogate Safety Measure for the Blind Region of Car-Following

model-releasesarxiv-cs-ro

arXiv:2609.00370v1 Announce Type: cross Abstract: Surrogate safety measures allow road-safety assessment from trajectory data in which crashes are absent, yet the dominant proximity measures - time-to

CRAD: Class-wise Reliability-Aware Distillation for Decentralized Heterogeneous Federated Learning

arXiv:2609.00446v1 Announce Type: cross Abstract: Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architectur

Agents

Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning

arXiv:2609.00351v1 Announce Type: cross Abstract: Large language models can hide hidden behaviors that activate only under narrow conditions, such as backdoor triggers, sleeper-agent deployment cues,

agentsarxiv-cs-ai

Agents

EDGE: Error Dependency Graph-Guided Multi-Error Attribution in Multi-Agent LLM Systems

arXiv:2609.01360v1 Announce Type: new Abstract: Large language model (LLM) agent failures often contain multiple related errors rather than a single mistake. Existing attribution methods usually ident

agentsarxiv-cs-ai

Evaluating Second-Order Bias of LLMs Through Epistemic Entitlement

arXiv:2606.17506v2 Announce Type: replace Abstract: Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judge

safetyarxiv-cs-cl

Evaluating Style-Personalized Text Generation: Challenges and Directions

arXiv:2508.06374v4 Announce Type: replace Abstract: With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--'write like me'--

Explicit Interaction Architectures for Dynamical Learning: A Controlled Study of Structural Inductive Bias

arXiv:2606.19101v2 Announce Type: replace-cross Abstract: We investigate a structure-first approach to dynamical learning in which the organization of stateful interactions is prescribed explicitly ra

safetyarxiv-cs-lg

Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection

arXiv:2504.10214v2 Announce Type: replace Abstract: Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories inc

Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents

arXiv:2609.01245v1 Announce Type: cross Abstract: Reinforcement learning is a natural way to post-train LLM agents for long-horizon interactive tasks judged only by end-of-task verification, yet a sha

FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data

arXiv:2508.04698v2 Announce Type: replace Abstract: LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Rece

Tutorials

FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge

arXiv:2402.16934v2 Announce Type: replace-cross Abstract: Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effec

tutorialsarxiv-cs-ai

GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing

arXiv:2609.00525v1 Announce Type: new Abstract: Modern image generation and editing systems can produce photorealistic, prompt-aligned images, but still often render familiar objects at implausible re

GeoGR^2:Zero-Shot Geospatial Inference via Geostatistically-Guided Iterative Refinement with LLMs

arXiv:2508.04080v2 Announce Type: replace Abstract: Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial depen

GuidedBench: Measuring and Mitigating the Evaluation Discrepancies of In-the-wild LLM Jailbreak Methods

arXiv:2502.16903v3 Announce Type: replace Abstract: Despite the growing interest in jailbreaks as an effective red-teaming tool for building safe and responsible large language models (LLMs), flawed e

HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation

arXiv:2603.10359v2 Announce Type: replace Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sa

Intelligent Edge Computing

arXiv:2609.00181v1 Announce Type: cross Abstract: The number of edge devices in large-scale edge systems is rapidly increasing. Edge devices have limited processing power, memory, and network bandwidt

Research

One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification

arXiv:2510.13302v4 Announce Type: replace-cross Abstract: Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, id

researcharxiv-cs-ai

Tutorials

Pre-carved Niches: The Formation Dynamics of Modular Task Partitions in Early LLM Training

arXiv:2609.01170v1 Announce Type: new Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizatio

tutorialsarxiv-cs-lg

Real-Time Neuromorphic Spectrum Intelligence Simulator

model-releasesarxiv-cs-lg

arXiv:2609.00585v1 Announce Type: cross Abstract: We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memris

RestoreBench: Can AI Agents Restore Power Flow Convergence?

arXiv:2609.00384v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and i

Retrieval, Scoring, and Decoding Shape Performance and Stability in LLM-based Conversational Recommendation

arXiv:2609.00086v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in conversational recommender systems, yet measured gains depend strongly on the retri

Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs

arXiv:2609.01573v1 Announce Type: cross Abstract: How to divide a fixed annotation budget between supervised fine-tuning (SFT) and reinforcement learning (RL) during LLM post-training remains an open

Applications

Separating Syntax from Language: A Mechanistic Account of Translation in Multilingual LLMs

arXiv:2609.01356v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they tran

applicationsarxiv-cs-cl