Search: “models”

STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images

model-releasesarxiv-cs-cv

arXiv:2609.05956v1 Announce Type: new Abstract: Spatial transcriptomics (ST) provides unprecedented insights into tumor heterogeneity by capturing spatially resolved gene expression, yet its high expe

9 Sept 2026

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WM-Craftnet: World Synesthesia Model for Generalizable and Robust Dexterous In-Hand Manipulation

DGX agent

arXiv:2609.07002v1 Announce Type: new Abstract: Generalizable and robust dexterous in-hand manipulation requires a policy to infer object pose, geometry, contact, and potential slip from partial and n

safetyarxiv-cs-ro

9 Sept 2026

A Removal Based Approach to Improve LLM Faithfulness at Test-Time

arXiv:2609.04343v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for consequential decisions, making their explanations an important tool for auditing model behavior.

safetyarxiv-cs-ai

7 Sept 2026

Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving

arXiv:2509.22480v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely used for problem-solving tasks. Most recent work improves their performance through supervised f

researcharxiv-cs-ai

7 Sept 2026

MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision

arXiv:2609.04958v1 Announce Type: new Abstract: Recovering camera and hand motion in world coordinates from egocentric video is a key capability for activity understanding, robot learning, and augment

researcharxiv-cs-cv

7 Sept 2026

CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning

arXiv:2609.03526v1 Announce Type: new Abstract: Multimodal language models achieve near-ceiling scores on food recognition benchmarks, yet it remains unclear whether this success reflects genuine cult

EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering

arXiv:2509.25175v3 Announce Type: replace-cross Abstract: Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted mani

Neural-Network Maxent: a general extension with learned nonlinearity, applied to time-series for Desert Locust distribution modelling

arXiv:2609.03603v1 Announce Type: new Abstract: Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodiversity, particularly for destructive pests

researcharxiv-cs-lg

PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

arXiv:2609.02896v1 Announce Type: new Abstract: Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted conside

researcharxiv-cs-cl

The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

arXiv:2609.02959v1 Announce Type: cross Abstract: What does a language model predict when it has few clues? The answer lurks in its unembedding geometry: a single direction of the unembedding matrix e

Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

arXiv:2512.10534v4 Announce Type: replace Abstract: Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO)

agentsarxiv-cs-ai

AGI Maze Prediction Datasets: A Compact Benchmark for Learning World Dynamics with Transformers

arXiv:2609.02339v1 Announce Type: cross Abstract: World modeling requires a predictive model to maintain and update an internal state adequate for reasoning about the consequences of actions. We intro

CivBench: A Long-Horizon Benchmark for Tool-Mediated Agents in Civilization VI

arXiv:2609.02459v1 Announce Type: new Abstract: We present CivBench, an open-source benchmark for evaluating language model agents in long-horizon, tool-mediated environments through the Model Context

Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing

arXiv:2609.02790v1 Announce Type: cross Abstract: Generative models have been studied experimentally and theoretically as priors for inverse problems such as compressed sensing. Recent work by Gunn et

safetyarxiv-cs-lg

Selective Knowledge Edit Reversal via Gated Singular Vector Shrinkage

arXiv:2609.02091v1 Announce Type: new Abstract: Knowledge editing provides an efficient way to update factual knowledge in large language models. However, malicious edits may introduce safety risks, m

Towards One-for-All Robustness Across a Continuum of Threat Levels

arXiv:2609.02440v1 Announce Type: cross Abstract: Adversarially robust models often overfit to a specific attack budget, necessitating multiple specialized models for diverse and dynamic adversarial e

AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes

arXiv:2609.00052v1 Announce Type: cross Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to

CHARM: Character Hallucination for Multicultural Role Play Benchmark

arXiv:2609.01352v1 Announce Type: cross Abstract: Role-playing large language models (LLMs) are expected to adopt a character's style while also respecting that character's knowledge boundaries. Prior

Compressing AI Traffic: Standardized Neural Network Coding of Visual-Token Representations in Split Vision-Language Inference

model-releasesarxiv-cs-cv

arXiv:2609.01200v1 Announce Type: new Abstract: When the visual encoder and the language decoder of a vision-language model (VLM) run on different compute nodes, the intermediate visual-token embeddin

Local Ai

Life Operators: a self-evolving framework for multiscale life modelling

arXiv:2609.00068v1 Announce Type: cross Abstract: Medical AI is moving beyond recognition towards clinical dialogue and longitudinal prediction. Yet a central question remains: how would a patient's s

local-aiarxiv-cs-ai

Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs

arXiv:2609.00575v1 Announce Type: new Abstract: Mixture-of-experts (MoE) architectures scale large language models efficiently, but they demand massive GPU memory. To cope with such demand, models are

RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks

arXiv:2609.00078v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large foundation models. Adopting fine-tuning to dis

When Tokenization is Secretly Output Supervision

arXiv:2609.01386v1 Announce Type: new Abstract: Tokenization in language models is treated by default as an input preprocessing decision. We argue that this framing is incomplete: in autoregressive mo

researcharxiv-cs-cl

A Controlled Evaluation of Model Rankings and Input Reliance in Surface Water Segmentation

arXiv:2608.30895v1 Announce Type: new Abstract: Performance evaluation for surface-water segmentation commonly uses an aggregate metric such as global intersection-over-union (IoU) to rank model confi

researcharxiv-cs-cv

A Mental Model Based Framework of Trust

arXiv:2301.12569v2 Announce Type: replace Abstract: Handling trust is a core requirement of effective interaction between people and AI agents. Thus, any decision-making framework designed to work wit

agentsarxiv-cs-ai

Frequency Selective Neural Networks as a Foundation Architecture for Time Series Learning

arXiv:2608.29012v1 Announce Type: new Abstract: Time-series data across physical and biological domains are fundamentally driven by complex, non-stationary oscillatory modes. While deep learning model

GreenBench: Benchmarking Energy Efficiency and Carbon Footprint of Open-Source LLM Inference on Apple Silicon

arXiv:2608.28667v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Models (LLMs) has raised concerns about their environmental impact during inference. While Green AI research

HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving

arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syste

Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling

arXiv:2608.30821v1 Announce Type: cross Abstract: Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and em

safetyarxiv-cs-ai

One Policy Is Enough: Single-Agent Reinforcement Learning Outperforms Tree Search for Chemistry Tool Learning

arXiv:2608.30952v1 Announce Type: cross Abstract: Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must reach for

Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling

arXiv:2608.30839v1 Announce Type: new Abstract: Thermal Infrared detection is widely used in autonomous driving, medical AI, etc., but its security has only attracted attention recently. We propose in

agentsarxiv-cs-cv

Sequential Trajectories and Simultaneous Blending: Multi-Emotion Modeling for Instruction-Following TTS

arXiv:2608.30325v1 Announce Type: new Abstract: Natural-language instructions enable flexible control of synthesized speech, yet emotional TTS systems primarily model a single utterance-level affect,

safetyarxiv-cs-cl

Test-Time Training with Next-Token Prediction

arXiv:2606.21803v2 Announce Type: replace Abstract: Next-token prediction is the self-supervised signal that trains language models, and every observed prompt token provides the same signal at test ti

The Halt Vector: Internalizing a Causal Steering Intervention for Efficient Reasoning

arXiv:2608.28859v1 Announce Type: cross Abstract: Reasoning models do not stop when they know the answer. On DeepSeek-R1-Distill-Qwen-7B the chain of thought runs about twice as long as the model's ow

TrainSDC: Characterizing and Mitigating Silent Data Corruption in Large Language Model Training

model-releasesarxiv-cs-lg

arXiv:2608.30769v1 Announce Type: new Abstract: LLM training is increasingly vulnerable to silent data corruption (SDC), yet existing protection methods largely treat Transformer computations uniforml

Understanding Reward Hacking in Text-to-Image Reinforcement Learning

arXiv:2601.03468v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a standard approach for post-training large language models and, more recently, for improving image generatio

safetyarxiv-cs-cv

Verification-Aware Training for Speculative Decoding

arXiv:2608.30135v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target mo

Advancing Interaction-Sensitive Feature Selection: Novel Relief-Based Algorithms, Expanded Comparisons, and Recommendations for Biomedical Data Mining

model-releasesarxiv-cs-lg

arXiv:2608.28552v1 Announce Type: new Abstract: As a precursor to high-dimensional biomedical data modeling, reliable feature selection can reduce computational expense, improve modeling performance,

31 Aug 2026

Diverging Transformer Predictions for Human Sentence Processing: A Comprehensive Analysis of Agreement Attraction Effects

arXiv:2603.16574v2 Announce Type: replace Abstract: Transformers underlie almost all state-of-the-art language models in computational linguistics, yet their cognitive adequacy as models of human sent

researcharxiv-cs-cl

31 Aug 2026

Applications

Focus Where It Counts: A Salience-Driven Vision-Language Model for Low Vision Assistance

arXiv:2608.28218v1 Announce Type: new Abstract: Vision-language models (VLMs) are rapidly progressing and offer promising capabilities for assistive technologies supporting persons with blindness or l

applicationsarxiv-cs-cv

31 Aug 2026

AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design

arXiv:2608.26747v1 Announce Type: new Abstract: Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomousl

safetyarxiv-cs-ai

Benchmarking AI Agents for Hardware Design Automation via MCP Tool Calling

arXiv:2608.26199v1 Announce Type: new Abstract: We ask whether AI agents powered by locally deployed large language models can reliably automate expert-defined hardware design workflows in an industry

Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners

arXiv:2608.27424v1 Announce Type: cross Abstract: Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation m

Double Trouble: Bilingual Pretraining Leaves Language-Conditioned Effects in Shared-Language Representations

arXiv:2608.26576v1 Announce Type: new Abstract: When researchers compare multilingual models for probing, interpretability, or cross-lingual transfer, they often align embedding spaces and assume that

Applications

Graph-Based Modeling of Financial Volatility Dynamics

arXiv:2608.26127v1 Announce Type: cross Abstract: Accurate forecasting of realized volatility (RV) is crucial for risk management and derivatives pricing. Although the implied volatility (IV) surface

applicationsarxiv-cs-cl

Prompt Sensitivity of Generative Agents: Evidence from an Epidemic Model

arXiv:2608.26221v1 Announce Type: cross Abstract: As generative AI gains traction, researchers are investigating its potential to serve as proxies for humans. From undergoing cognitive psychology expe

agentsarxiv-cs-ai

ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction

model-releasesarxiv-cs-cv

arXiv:2608.27154v1 Announce Type: new Abstract: Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this

SimCast-S2S: An Efficient Generative Model for Subseasonal Precipitation Forecasting via Transfer Learning from Climate Simulations

arXiv:2608.26594v1 Announce Type: new Abstract: Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictiv

safetyarxiv-cs-lg