Search: “reasoning”

Beyond Consensus: Downward Bias and Role Asymmetry in Multi-Agent LLM Judges for Subjective Evaluation

arXiv:2608.30373v1 Announce Type: new Abstract: Multi-Agent Debate (MAD) has been widely adopted to improve LLM-based evaluation by prompting multiple agents to negotiate and reach a consensus. Howeve

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Applications

Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation

DGX agent

arXiv:2608.30333v1 Announce Type: cross Abstract: Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purch

applicationsarxiv-cs-ai

1 Sept 2026

Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?

arXiv:2608.28649v1 Announce Type: cross Abstract: Touchpoint selection in conversion attribution, namely identifying meaningful touchpoints contributing to conversions, is essential for e-commerce rec

CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models

arXiv:2608.30922v1 Announce Type: new Abstract: Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refine

CDPR: Counterfactual Advantage-based Credit Assignment for Cost-Aware Sequential Medical Diagnosis

arXiv:2608.28599v1 Announce Type: new Abstract: Clinical diagnosis is a step-by-step, cost-aware process: a physician orders examinations one at a time, observes the results, and updates the diagnosis

safetyarxiv-cs-ai

Local Ai

CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation

arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal re

local-aiarxiv-cs-cv

Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation

arXiv:2608.30397v1 Announce Type: new Abstract: Natural-language critiques provide supervision beyond scalar rewards for non-verifiable generation, which lacks deterministic verifiers. In critique-gui

researcharxiv-cs-cl

ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition

model-releasesarxiv-cs-cv

arXiv:2608.30183v1 Announce Type: cross Abstract: Lightweight channel attention mechanisms are widely used in image classification, yet their effectiveness in fine-grained visual recognition (FGVR) re

Constrained Group Relative Policy Optimization

arXiv:2602.05863v3 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) remains the dominant critic-free approach for fine-tuning LLMs and VLMs, but its compatibility with

Context Staircase: Signature-Aligned Dynamics of Token Embeddings under Small Initialization

arXiv:2608.30315v1 Announce Type: cross Abstract: Token embeddings are the basic representational units that connect discrete tokens with continuous computation in language models. Although modern lan

Tutorials

CoReflect: A Reflective Co-Evolution Framework for Improving Conversational Evaluation

arXiv:2601.12208v2 Announce Type: replace Abstract: Evaluating conversational systems in multi-turn settings remains a fundamental challenge. Conventional pipelines typically rely on manually defined

tutorialsarxiv-cs-cl

Detecting and Repairing Hallucinations in Retrieval-Augmented Generation

arXiv:2608.29307v1 Announce Type: cross Abstract: Language models increasingly answer questions by consulting retrieved documents rather than memory alone, a design now common in search assistants and

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing

arXiv:2607.02290v2 Announce Type: replace Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a know

researcharxiv-cs-cv

Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark

arXiv:2608.30327v1 Announce Type: new Abstract: Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingu

DocIntent: Answerability-Guided Agentic Restoration for Real-World Document Visual Question Answering

arXiv:2608.29037v1 Announce Type: cross Abstract: Real-world degradations such as blur, shadow, distortion, and moire patterns severely impair the document question-answering capabilities of Multimoda

Domain Mixture Design via Log-Likelihood Differences for Aligning Language Models with a Target Model

arXiv:2603.16622v2 Announce Type: replace Abstract: Instead of directly distilling a language model, this study addresses the problem of aligning a base model with a target model in distribution by de

ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography

arXiv:2608.30893v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation requires knowledge of cardiology, electrophysiology, clinical diagnosis, ECG waveforms, signal acquisition, and i

Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift

arXiv:2608.29543v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, ofte

EvoBrowseComp: Benchmarking Search Agents on Evolving Knowledge

arXiv:2606.13120v2 Announce Type: replace Abstract: Search Agents -- large language models augmented with search tools -- have intensified the need for future-proof evaluation benchmarks. Existing ben

EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants

arXiv:2608.29387v1 Announce Type: new Abstract: Large language models can generate interactive web interfaces, but reliable generative UI requires maintaining an executable artifact as user requests e

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

arXiv:2506.11635v2 Announce Type: replace-cross Abstract: Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to k

FaceSnap: Real-Time Personalized Lightstage Facial Performance Capture

model-releasesarxiv-cs-cv

arXiv:2608.31033v1 Announce Type: new Abstract: Lightstage facial capture produces production-quality digital humans, but it is resource and labor-intensive. Multi-camera setups, hours of computation,

Applications

FISICA: A Deployed Service for Plantar-Pressure and Posture Assessment with Ontology-Grounded Recommendation

arXiv:2608.29336v1 Announce Type: new Abstract: FISICA is a body-assessment and recommendation service running in production. One standing session with two photographs returns foot-loading measures, p

applicationsarxiv-cs-cv

From AGI to ASI

arXiv:2606.12683v2 Announce Type: replace Abstract: Over the last decade, building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade ta

From Extraction to Governed Memory: Multi-Agent Knowledge Graph Construction with Domain-Expert Review

arXiv:2608.28642v1 Announce Type: new Abstract: Knowledge graphs used by agentic systems are often treated as flat stores of extracted triples, with little record of who owns a fact, why it was admitt

From Final Artifacts to Trajectories: Retrospective Process Supervision for Evidence-Grounded Long-Form Generation

arXiv:2608.30461v1 Announce Type: new Abstract: Trajectory data is getting more vital for training large language models for boosting the agentic abilities. Unlike the verifiable domains such as codin

agentsarxiv-cs-cl

HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs

arXiv:2503.04822v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many

How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction

arXiv:2601.08626v4 Announce Type: replace Abstract: Large language models (LLMs) excel at semantic understanding, yet their ability to reconstruct internal structure from scrambled inputs remains unde

Investigating Social Bias Changes in Quantized Language Models

arXiv:2602.06181v2 Announce Type: replace Abstract: Post-training quantization reduces the memory needed to run large language models but alters their social biases in ways that aggregate metrics fail

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

arXiv:2603.22978v2 Announce Type: replace Abstract: In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as image

Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs

arXiv:2608.29257v1 Announce Type: new Abstract: Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound ev

Latent-Space Intervention for Cross-Lingual Factual Consistency: Consistency Improvements without Accuracy Drops

arXiv:2608.28860v1 Announce Type: new Abstract: Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space interventi

Learning Simple Test-Time Environments for LLM Web Agents

arXiv:2608.29305v1 Announce Type: cross Abstract: Large language model (LLM) agents have demonstrated remarkable proficiency in manually constructed environments, yet their performance frequently coll

Leveraging Turn-taking Dynamics for Intent Recognition in Multi-party Conversations

arXiv:2608.28926v1 Announce Type: new Abstract: We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynamics.

researcharxiv-cs-cl

LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information

arXiv:2608.30235v1 Announce Type: new Abstract: Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG struct

LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It

arXiv:2608.31016v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the no

LLP: LLM-Based Product Pricing in E-commerce

arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft

Applications

Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective

arXiv:2608.30563v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or

applicationsarxiv-cs-cv

Applications

MURANO: Design, Run, and Reproduce Mechanistic Interpretability Experiments as Composable Pipelines

arXiv:2608.30662v1 Announce Type: new Abstract: This paper presents Murano, an open source framework for designing, running, and reproducing mechanistic interpretability studies of large language mode

applicationsarxiv-cs-cl

Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines

arXiv:2608.29589v1 Announce Type: new Abstract: Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired prompts across five English dialec

safetyarxiv-cs-ai

OPUS: A Simple yet Effective Unified Framework for Open-Vocabulary Detection

arXiv:2608.30247v1 Announce Type: new Abstract: Recent unified open-vocabulary detection (OVD) supports heterogeneous prompts, including text queries, visual exemplars, and their combinations, but oft

safetyarxiv-cs-cv

Overreliance on AI in Information-seeking from Video Content

arXiv:2603.19843v2 Announce Type: replace-cross Abstract: The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search

PaperBanana-Interact: Scientific Diagram Refinement with Multi-Turn Human Feedback

arXiv:2608.30241v1 Announce Type: new Abstract: Recent efforts have aimed to automate scientific diagram generation from paper content (Lin et al., 2026; Zhu et al., 2026a). However, fully satisfying

Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval

arXiv:2608.30553v1 Announce Type: cross Abstract: Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabili

safetyarxiv-cs-ai

ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs

arXiv:2608.25992v2 Announce Type: replace Abstract: Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative rea

Proof2Hybrid: Automatic Mathematical Benchmark Synthesis for Proof-Centric Problems

arXiv:2508.02208v3 Announce Type: replace-cross Abstract: Evaluating the mathematical capability of Large Language Models (LLMs) is a critical yet challenging frontier. Existing benchmarks fall short,

Prove2Me: An Open Collaborative Platform for Scaling Math Formalization

arXiv:2608.28433v2 Announce Type: replace Abstract: Proof assistants such as Lean 4 promise the paradigm of formally verified mathematics, but large-scale formalization projects have faced major barri

PUFFER: Incremental Fuzzy Deduplication for Continuously Evolving Corpora

arXiv:2608.28622v1 Announce Type: cross Abstract: Large language model training corpora grow through successive, often redundant releases, so each release must be deduplicated against both itself and