Search: “models”

Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text

arXiv:2609.40359v1 Announce Type: new Abstract: We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the in

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Revisiting scaling laws for reward optimization

DGX agent

arXiv:2609.38526v1 Announce Type: cross Abstract: Scaling laws for optimization against reward models in AI alignment have pinned down how performance depends on optimization effort---measured by a KL

safetyarxiv-cs-ai

1 Oct 2026

Scaling Laws for Looped Mixture of Experts

arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed pa

SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs

arXiv:2609.38899v1 Announce Type: cross Abstract: Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit polic

Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts

arXiv:2609.38600v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in clinical settings, it is critical to evaluate their reliability under realistic variation in cl

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

arXiv:2602.14049v2 Announce Type: replace-cross Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as sig

When Scientific Contradictions Are Lost in Translation

arXiv:2609.38621v1 Announce Type: new Abstract: Two scientific findings can disagree without contradicting each other. Determining whether they conflict requires knowing whether they describe comparab

WorkGenesis: Building the Worlds That Teach Agents to Work

arXiv:2609.39325v1 Announce Type: new Abstract: The ability of Large Language Model (LLM) agents to complete daily and professional work is receiving increasing attention. Training such agents require

AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation

arXiv:2609.38142v1 Announce Type: new Abstract: A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advis

BITEM at the NTCIR-19 R2C2 Task: Predicting Confidence from Agentic RAG Pipeline Signals

arXiv:2609.37993v1 Announce Type: cross Abstract: The BITEM team entered both subtasks of the NTCIR-19 R2C2 task with a single agentic pipeline, in which a model searches, reads and records evidence o

Counterfactual Probing for Parallel Unmasking with Hidden Forest Structure

arXiv:2609.37841v1 Announce Type: new Abstract: Masked generative models offer parallel token prediction, but accurate parallel sampling must account for dependencies among tokens. When dependencies a

CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering

arXiv:2609.36570v1 Announce Type: cross Abstract: Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that s

Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data

arXiv:2609.38165v1 Announce Type: new Abstract: The landscape of satellite imagery time series datasets and boundary-pushing architectures for cropland segmentation has never been richer. However, in

researcharxiv-cs-cv

CruxBench: A Benchmark of Information Discovery

arXiv:2609.35879v1 Announce Type: cross Abstract: Benchmarks for large language models (LLMs) typically evaluate the accuracy of answers against fixed reference labels. But a central step in many comp

Do LLM Agents Execute the Plans They Declare? From Planning-Mode Declaration to Pattern-Specific Execution

arXiv:2609.38108v1 Announce Type: new Abstract: Large language models (LLMs) enable agents to solve long-horizon tasks by generating a plan and then executing it in an environment. However, successful

Does the Unsafe Gradient Survive a Conversation? On the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue

arXiv:2609.36849v1 Announce Type: cross Abstract: Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns i

Evaluation Choices Shape Biomedical ML Claims: A Pediatric Pneumonia Benchmark Case Study

arXiv:2609.37848v1 Announce Type: new Abstract: Biomedical machine learning papers often compress model performance into one headline number. That number can look like a property of the model even whe

FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning

arXiv:2609.37033v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but stat

ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction

arXiv:2609.36996v1 Announce Type: new Abstract: Knowledge Graph Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missin

InsightMap: Structured Spatial Modeling for Embodied Multimodal Reasoning

arXiv:2609.37187v1 Announce Type: new Abstract: Language-guided navigation requires connecting partial observations to a persistent spatial reference and learning how actions change that representatio

researcharxiv-cs-cv

Knowing Is Not Choosing: What Explicit Verification Adds Beyond Generative Preference

arXiv:2609.33142v2 Announce Type: replace Abstract: Generating a correct answer does not mean that a language model will select it. We separate factual recall into three steps: generating a correct ca

LazySloth: Bounded LLM-based Lazy Tree Search for Fast Long Video Comprehension

arXiv:2609.37426v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture. H

Applications

Learning Continuous Patient Trajectories from Electronic Health Records

arXiv:2609.36144v1 Announce Type: new Abstract: Electronic health records provide irregular observations of latent patient states that evolve continuously over time. Recent autoregressive models condi

applicationsarxiv-cs-lg

Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing st

OTT3R: Multi-View 3D Reconstruction and Fast Dataset Generation at 1% Compute

arXiv:2609.36374v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research grou

Point2Part: Unified 3D Partitioning from Point Prompts

arXiv:2609.38180v1 Announce Type: new Abstract: Existing 3D part decomposition methods do not necessarily partition the original shape into non-overlapping parts that collectively cover the entire sha

Reducing the Adaptation Gap Through Reachable Fisher Geometry

arXiv:2609.36329v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) determines not only how many parameters are trained, but also which local directions a model can move in, so simi

Relevance Is Not Sufficient Evidence: Detecting Evidence Gaps Before Generation in RAG

arXiv:2609.37469v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds large language models in external sources, but retrieved passages often name the right entities without pro

Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing

arXiv:2609.37402v1 Announce Type: new Abstract: Large language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such

researcharxiv-cs-ai

SafeLLM4SE: Statistical Evaluation and Reporting for LLM-based Software Engineering Systems

arXiv:2609.37294v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for software engineering tasks, yet their stochastic behavior challenges the validity, reproducibil

Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning

arXiv:2609.36862v1 Announce Type: cross Abstract: Fine-tuning-as-a-service lets users adapt a safety-aligned language model to their own data, but it also creates a harmful fine-tuning attack surface:

Video2Skill: From Streaming Experience to Reusable Embodied Skills

arXiv:2609.36691v1 Announce Type: new Abstract: Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embodi

Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoom

arXiv:2609.37002v1 Announce Type: cross Abstract: High-resolution visual question answering often fails because a multimodal model does not acquire the small, spatially localized evidence needed to an

Visual sensitivity is not claim retractability: persistence-aware credit assignment for multimodal reinforcement learning

arXiv:2609.36572v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been extended to Large Vision-Language Models (LVLMs), and perception-aware methods further en

vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains

arXiv:2609.37062v1 Announce Type: cross Abstract: Dynamic layer skipping reduces LLM computation by allowing each token to execute only a subset of the model's layers. However, existing skippers rely

Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space

arXiv:2609.37731v1 Announce Type: new Abstract: Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform,

Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts

arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would l

You Only Reprogram Once: Rethinking Prolonged Training for Visual Reprogramming

arXiv:2609.36661v1 Announce Type: new Abstract: Visual reprogramming is a parameter-efficient method for adapting pretrained models, yet its training can remain computationally expensive: even with a

Applications

Activation Flow: Manufacturing Activations for Steering

arXiv:2609.32530v1 Announce Type: new Abstract: Difference-in-means steering requires activations recorded while a model shows the desired behavior, which a sandbagging model withholds by deliberately

applicationsarxiv-cs-lg

CoDrive: Cross-Vehicle World-Consistent Video Generation with Precise Trajectory Control for Cooperative Driving

arXiv:2609.34749v1 Announce Type: new Abstract: Real-world driving is inherently multi-agent, yet most existing driving world models generate observations from a single ego vehicle. Independently exte

Composable Decoding on the Probability Simplex: Theory and Implementation

arXiv:2609.34992v1 Announce Type: new Abstract: Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the b

ControlTrace: Recovering Control Fields for Hidden-Content Recognition

arXiv:2609.34807v1 Announce Type: new Abstract: Spatially conditioned diffusion models can embed words and contours in natural-looking images, but vision-language models (VLMs) may fail to recognize t

Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features

arXiv:2609.35022v1 Announce Type: new Abstract: The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for ad

DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution

arXiv:2609.32705v1 Announce Type: new Abstract: While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity

researcharxiv-cs-cv

Fine Until Fine-Tuned: Repeated Solutions Make Reasoning Fragile

arXiv:2609.33559v1 Announce Type: new Abstract: Recipes such as s1 and LIMO teach a model to reason with little data by showing it the same thousand or fewer worked solutions many times over. Judged w

researcharxiv-cs-lg

How Reusable Are Benchmarks with Richer Feedback?

arXiv:2609.32109v1 Announce Type: new Abstract: We study whether benchmarks reliably guide model selection as developers adapt to evaluation feedback across multiple criteria. We find that the worst-c

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Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors

arXiv:2609.33407v1 Announce Type: cross Abstract: De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue t

safetyarxiv-cs-lg

LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking

arXiv:2609.33306v1 Announce Type: new Abstract: Current Transformer-based tracking methods typically stack multiple Transformer blocks with separate parameters to model interactions between the target