Search: “reasoning”

You Do Not Fully Utilize Transformer's Representation Capacity

arXiv:2502.09245v3 Announce Type: replace-cross Abstract: In contrast to RNNs, which compress their history into a single hidden state, Transformers can attend to all past tokens directly. However, st

1 Sept 2026

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Model Releases

A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection

DGX agent

arXiv:2608.27997v1 Announce Type: new Abstract: Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intellig

model-releasesarxiv-cs-cv

31 Aug 2026

Are These Modules Worth Their Cost? A Paradigm-Level Accuracy-Cost Analysis of In-context Learning Text-to-SQL

arXiv:2608.28432v1 Announce Type: new Abstract: Recent advances in in-context learning (ICL) text-to-SQL have substantially improved execution accuracy on public benchmarks by assembling increasingly

Hardware

Benchmarking large language model agent societies against human behavioural distributions

arXiv:2608.28182v1 Announce Type: cross Abstract: Populations of large language model agents are increasingly used as experimental societies. Three doubts shadow every such result: whether the agents

hardwarearxiv-cs-cl

Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge

arXiv:2608.28478v1 Announce Type: new Abstract: Factual question answering (QA) typically assumes a single canonical answer, obscuring whether large language models (LLMs) retain divergent accounts of

Local Ai

CAVE-NAV: VLM-Based Autonomous 3D Navigation in Underwater Cave Environments

arXiv:2608.27793v1 Announce Type: new Abstract: Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Tradi

local-aiarxiv-cs-ro

CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs

arXiv:2608.28137v1 Announce Type: cross Abstract: We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our met

researcharxiv-cs-cv

ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL

arXiv:2608.28476v1 Announce Type: new Abstract: Long-horizon agentic tasks require large language models (LLMs) to iteratively retrieve, integrate, and maintain dispersed information across multi-turn

agentsarxiv-cs-cl

DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging

model-releasesarxiv-cs-lg

arXiv:2608.28547v1 Announce Type: new Abstract: Model merging combines multiple task-specific fine-tuned LLMs into a single multi-task model without additional training. However, merged models are kno

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

model-releasesarxiv-cs-cv

arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.

Iron: Intent-Aligned and Retrospective Dual Learning Framework for Enhancing Generalist Virtual Agents

arXiv:2608.27866v1 Announce Type: new Abstract: Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal La

safetyarxiv-cs-cv

Long Story Short: Story-level Video Understanding from 20K Short Films

arXiv:2406.10221v3 Announce Type: replace-cross Abstract: Recent developments in vision-language models have significantly advanced video understanding. Existing datasets and tasks, however, have nota

Tutorials

Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints

arXiv:2512.05079v2 Announce Type: replace Abstract: Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial

tutorialsarxiv-cs-cv

OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion

arXiv:2512.00234v3 Announce Type: replace Abstract: There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. How

Post-Training VLMs for Video Mistake Detection

model-releasesarxiv-cs-cv

arXiv:2608.28406v1 Announce Type: new Abstract: Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in de

Sliding-window beats linear attention

arXiv:2608.28444v1 Announce Type: new Abstract: Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new token costs more than the previous

Steering Multimodal Large Language Models Decoding for Context-Aware Safety

arXiv:2509.19212v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisi

safetyarxiv-cs-cl

Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs

arXiv:2608.27948v1 Announce Type: new Abstract: Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which invo

safetyarxiv-cs-lg

The Discrete-Log Clock: How a Transformer Learns Modular Multiplication

arXiv:2606.17399v2 Announce Type: replace Abstract: When small transformers grok modular multiplication, prior work reports that the learned embedding has a 'dense' Fourier spectrum requiring all freq

researcharxiv-cs-lg

Trajectory-Level Speculative Decoding for Diffusion Language Models

arXiv:2608.27514v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to singl

Under-Mattress Temporal Sensing for Next-Day Agitation Risk Scoring in Dementia Wards

model-releasesarxiv-cs-lg

arXiv:2608.28152v1 Announce Type: cross Abstract: Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk

6.5% of the Neuro-Symbolic Literature Can Be Reproduced from Its Published Artifacts, a Six-Stage Audit Framework and First Instantiation

arXiv:2608.26236v1 Announce Type: new Abstract: We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain,

A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers

arXiv:2608.26194v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems have attracted significant interest for their ability to mitigate hallucinations in Large Language Models

Active sensing to characterize the heterogeneity of plant stress

arXiv:2608.27088v1 Announce Type: cross Abstract: While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physio

Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data

model-releasesarxiv-cs-ai

arXiv:2607.18064v2 Announce Type: replace-cross Abstract: Coding agents can now be left alone to improve software against a score. In this pattern--recently popularized as 'autoresearch'--the agent re

Comparative Evaluation of 3D Reconstruction Methods for Immersive Visualization of Laboratory Objects

arXiv:2608.27301v1 Announce Type: cross Abstract: In this study, we examined whether current 3D reconstruction methods can support the creation of realistic holographic representations of laboratory o

researcharxiv-cs-cv

Compositional Generalization via Structural Identification in a Category-Theoretic Framework

arXiv:2608.26465v1 Announce Type: new Abstract: Compositional generalization is usually evaluated through model accuracy. We instead ask which structural or lexical identifications make held-out COGS

Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms

arXiv:2608.27409v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves specific capabilities of large language models, but covering multiple capabilities often

CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases

model-releasesarxiv-cs-ai

arXiv:2608.27391v1 Announce Type: new Abstract: LLMs are increasingly able to answer complex questions about enterprise-scale document collections. But evaluation is hard: companies don't want to shar

Decoupling Planning and Control for Instructable Agents

arXiv:2608.26788v1 Announce Type: new Abstract: Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-l

safetyarxiv-cs-ai

Do LLMs Understand Personality? Rethinking Persona Fidelity Evaluation through Structured Behavioral Inference

arXiv:2608.26674v1 Announce Type: cross Abstract: As large language models are increasingly deployed to simulate diverse human characters, ensuring persona fidelity, defined as the extent to which an

DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali

arXiv:2608.27110v1 Announce Type: new Abstract: Reliable medical conversational AI requires authentic expert--patient interaction data, yet such datasets remain scarce, especially for low-resource lan

Evaluating AI Generated Summaries for Cancer Patients

arXiv:2608.26154v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being integrated into digital health platforms to generate summaries of complex medical data. Although t

safetyarxiv-cs-ai

Applications

Exploring the Role of LLMs in HPC Programming: A Survey

arXiv:2608.26110v1 Announce Type: cross Abstract: Large Language Models (LLMs) are emerging as promising assistants in High-Performance Computing (HPC), where programming remains complex and expertise

applicationsarxiv-cs-ai

Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement

arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete

From SQL to Knowledge Graphs: An LLM-Driven Multi-Agent Approach with Data Schema Improvement

arXiv:2608.26117v1 Announce Type: cross Abstract: RDBMS (Relational Database Management System) databases face several limitations, including slow execution with multi-hop queries and a lack of explai

Local Ai

Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs

arXiv:2608.26641v1 Announce Type: new Abstract: Decoding quality in diffusion multimodal language models (dMLLMs) depends heavily on the order in which masked tokens are committed. Existing confidence

local-aiarxiv-cs-cl

J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data

arXiv:2608.26582v1 Announce Type: cross Abstract: Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human sup

Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

arXiv:2608.26730v1 Announce Type: new Abstract: Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Aut

Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation

arXiv:2608.26489v1 Announce Type: new Abstract: Detecting woody clearing is vital for managing biodiversity. Deep learning models can detect change in woody vegetation from bitemporal remote sensing i

safetyarxiv-cs-cv

Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents

arXiv:2505.14104v2 Announce Type: replace Abstract: Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms,

LiveSim: Simulating Environment-Shaped Users in Multi-Agent Live-Stream Ecosystems

arXiv:2608.26849v1 Announce Type: new Abstract: User behavior simulation with large language models~(LLMs) is increasingly used to support multi-agent ecosystem simulation. Existing simulators typical

LLM Agents for Time-Series: A Survey

arXiv:2608.26226v1 Announce Type: new Abstract: LLM-based agents are increasingly being developed for time-series problems, but their design choices vary substantially across task settings. This surve

Making Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction

arXiv:2608.27397v1 Announce Type: cross Abstract: Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g.,

MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering

arXiv:2601.15236v2 Announce Type: replace Abstract: Large language models can memorize competing locale-specific facts yet fail to select among them when the locale changes, defaulting instead to a si

Mitigating Fabrication in Multi-Stage LLM Pipelines for Hiring: An Empirical Evaluation of Prompt Guardrails and Human-in-the-Loop Checkpoints

arXiv:2608.26171v1 Announce Type: cross Abstract: Multi-stage LLM hiring pipelines (resume improvement, interview question generation, answer feedback) can fabricate credentials, inflate qualifiers, a

safetyarxiv-cs-cl

Applications

Multi-Image Visual Token Pruning in Large Visual Language Models

arXiv:2608.26806v1 Announce Type: new Abstract: With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigat

applicationsarxiv-cs-cv

Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search

arXiv:2608.27266v1 Announce Type: new Abstract: Efficiently improving autonomous agents across diverse tasks is central to accelerating recursive self-improvement (RSI) in agentic AI, with prompt opti