Search: “reasoning”

PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants

arXiv:2608.26180v1 Announce Type: cross Abstract: Shopping assistants are shifting from ranked product lists toward structured decision support, where systems must synthesize shopper context, product

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Research

Packora: Systematic Design for Generative Molecular Crystal Structure Prediction

DGX agent

arXiv:2608.26962v1 Announce Type: new Abstract: Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecu

researcharxiv-cs-lg

28 Aug 2026

pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning

arXiv:2608.27101v1 Announce Type: new Abstract: Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs), which can hallucinate domain terms, produc

researcharxiv-cs-ai

Agents

RTNav: Towards Real-Time Zero-Shot Object Navigation

arXiv:2608.26496v1 Announce Type: cross Abstract: Navigation in unknown environments to find unforeseen objects has become increasingly feasible with capable vision and language foundation models. How

agentsarxiv-cs-ai

Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models

arXiv:2608.27135v1 Announce Type: new Abstract: Multimodal foundation models are increasingly used in speech-first assistants that must interpret spoken queries and produce visually grounded decisions

SpeechGym: An Audio-Native Gym for Training Voice Agents via Reinforcement Learning

arXiv:2608.26432v1 Announce Type: cross Abstract: Voice agents must call tools and hold multi-turn dialogue entirely through speech, yet the dominant paradigm trains them in text. Existing frameworks

Survival-Guided Length Control for Efficient Diffusion Language Models

arXiv:2608.26374v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or reli

researcharxiv-cs-cl

The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning

arXiv:2608.26116v1 Announce Type: new Abstract: Existing methods for exploring cellular automata and other complex systems mostly operate in open loop: they set initial conditions, execute a full simu

safetyarxiv-cs-ai

Toward a New Science of AI as Cognitive Infrastructure

arXiv:2507.22893v3 Announce Type: replace-cross Abstract: Contemporary human-AI interaction research overlooks how AI systems fundamentally reshape human cognition pre-consciously, a critical blind sp

researcharxiv-cs-ai

TTPO: Test-Time Policy Optimization

arXiv:2608.27448v1 Announce Type: new Abstract: Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathem

safetyarxiv-cs-cl

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

arXiv:2608.27456v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the a

local-aiarxiv-cs-cv

Tutorials

Vowel Signs Are Not Letters: A Pre-tokenization Ceiling on Multilingual Tokenizer Fertility

arXiv:2608.26449v1 Announce Type: new Abstract: Byte-level BPE tokenizers that use the HuggingFace ByteLevel pre-tokenizer inherit GPT-2's word regex, where a word is defined as p{L}+, one or more Uni

tutorialsarxiv-cs-cl

Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI

arXiv:2608.26182v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains u

safetyarxiv-cs-ai

Agents

AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs

arXiv:2608.26004v1 Announce Type: cross Abstract: Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control laten

agentsarxiv-cs-cl

Hardware

BanglaMamba: Exploring State Space Models for Bangla Fake News Detection

arXiv:2608.25190v1 Announce Type: new Abstract: Fake news detection has become an important Natural Language Processing (NLP) task due to the rapid spread of misinformation through online news platfor

hardwarearxiv-cs-cl

Demystifying Reinforcement Learning Post-Training of Language Models

arXiv:2608.24949v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling

safetyarxiv-cs-cl

DualOPSD: Adaptive Privileged Teachers for On-Policy Self-Distillation

arXiv:2608.26019v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a privileged copy of the student model to provide dense supervision without an external teacher. OPSD keeps this

Dynamic Influence-Weighted Distillation for Single-IMU Activity Recognition

model-releasesarxiv-cs-lg

arXiv:2608.24904v1 Announce Type: new Abstract: Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. W

E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search

arXiv:2510.22733v3 Announce Type: replace Abstract: Text embedding models deliver competitive retrieval performance with high efficiency, but their ranking fidelity remains limited compared to LLM-bas

Applications

EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus

arXiv:2510.12899v3 Announce Type: replace Abstract: Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLM

applicationsarxiv-cs-cl

EgoNav: Bridging Learned Waypoints and Geometry-Aware Local Control for Robust Indoor Navigation

arXiv:2608.25642v1 Announce Type: new Abstract: Image-goal navigation using lightweight topological maps is a practical paradigm for indoor robot deployment: the map requires only geotagged images, an

safetyarxiv-cs-ro

Escaping Low-Dimensional Overlap: Multi-Task Model Merging via High-Dimensional Sparse Disentanglement

arXiv:2608.25354v1 Announce Type: cross Abstract: Model merging provides an efficient way to construct multi-task generalist models without additional training, but its performance often degrades unde

Fairness-Aware Test-Time Prompt Tuning

arXiv:2608.25707v1 Announce Type: new Abstract: Vision-language models have displayed remarkable capabilities in multi-modal understanding and are increasingly used in critical applications where econ

FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review

arXiv:2608.25325v1 Announce Type: cross Abstract: Deploying large language models for professional financial review requires more than measuring general financial competence: models must perform the s

FrontierChallenge: Evaluating Scientific Workflow Completion

arXiv:2608.24979v1 Announce Type: cross Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated progr

Agents

Groundhog Bit-Flip Attack: Seeding Infinite Generation Loops in Mixture-of-Experts LLMs through Bit Flips

arXiv:2608.25276v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks through

agentsarxiv-cs-cl

IDEAlign: Comparing Ideas of Large Language Models to Domain Expert

arXiv:2509.02855v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to produce open-ended, interpretive annotations, yet there is no validated, scalable measure of i

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

arXiv:2512.14237v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods

Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences

arXiv:2608.25771v1 Announce Type: cross Abstract: In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized

Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting

arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or

researcharxiv-cs-cl

Applications

Leveraging Inter-object Affordances for Efficient Planning in Contact-rich Tasks

arXiv:2608.25641v1 Announce Type: new Abstract: Traditional task-and-motion planning (TAMP) approaches primarily focus on defining sequences of actions along with the necessary geometric and kinematic

applicationsarxiv-cs-ro

LM-X: Explainable Action Modeling with Progress, Event, and Uncertainty Prediction for Generalist Robot Manipulation

arXiv:2608.25757v1 Announce Type: cross Abstract: Generalist vision--language--action (VLA) policies learn long-horizon behavior mainly through short-horizon action prediction and reveal little beyond

local-aiarxiv-cs-lg

LongVU-TTT: Causal Test-Time Training for Visual Resampling in Long Video Understanding

arXiv:2608.25729v1 Announce Type: new Abstract: Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a c

researcharxiv-cs-cv

Tutorials

Moving Beyond More Views: Redundancy-Aware Ego-Exo Fusion for Proficiency Estimation

arXiv:2608.25736v1 Announce Type: new Abstract: EgoExo proficiency estimation aims to assess action quality by integrating fine-grained motion cues from egocentric (1st-person) views with spatial cont

tutorialsarxiv-cs-cv

Multi-Modal Anomaly Detection: A Survey

arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-cr

See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

arXiv:2608.25168v1 Announce Type: new Abstract: In multi-view anomaly detection, more cross-view information can actually hurt. When multiple inspection views are naively fused in a reconstruction-bas

local-aiarxiv-cs-cv

SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation

arXiv:2608.25123v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often u

researcharxiv-cs-cl

Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens

arXiv:2608.25347v1 Announce Type: new Abstract: The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack

safetyarxiv-cs-cl

SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring

model-releasesarxiv-cs-ro

arXiv:2608.25142v1 Announce Type: new Abstract: Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degra

THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT

arXiv:2608.25845v1 Announce Type: new Abstract: Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's o

local-aiarxiv-cs-cv

Tutorials

The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline

arXiv:2608.24952v1 Announce Type: new Abstract: Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language model

tutorialsarxiv-cs-cl

Applications

Towards A Unified Information Bottleneck Framework for Time Series Explanations

arXiv:2608.25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights int

applicationsarxiv-cs-lg

Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training

arXiv:2608.25826v1 Announce Type: new Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web

When RAG Fails to Equalize: Geo-bias in Factual Question Answering over Public Companies

arXiv:2608.25717v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is widely assumed to mitigate factual errors in large language models (LLMs), but it remains unclear whether retrie

A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm

arXiv:2608.24210v1 Announce Type: new Abstract: Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This abil

researcharxiv-cs-lg

A Literate Programming Environment for Human and Machine Agents

arXiv:2608.24644v1 Announce Type: cross Abstract: This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a gra

researcharxiv-cs-ai

Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments

arXiv:2608.24099v1 Announce Type: new Abstract: GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack sy

Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability, Calibration, and Human Oversight

arXiv:2608.24314v1 Announce Type: new Abstract: Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contex